[INFO] fetching crate easynn 0.1.7-beta...
[INFO] testing easynn-0.1.7-beta against 1.75.0 for beta-1.76-1
[INFO] extracting crate easynn 0.1.7-beta into /workspace/builds/worker-5-tc1/source
[INFO] validating manifest of crates.io crate easynn 0.1.7-beta on toolchain 1.75.0
[INFO] running `Command { std: CARGO_HOME="/workspace/cargo-home" RUSTUP_HOME="/workspace/rustup-home" "/workspace/cargo-home/bin/cargo" "+1.75.0" "metadata" "--manifest-path" "Cargo.toml" "--no-deps", kill_on_drop: false }`
[INFO] started tweaking crates.io crate easynn 0.1.7-beta
[INFO] finished tweaking crates.io crate easynn 0.1.7-beta
[INFO] tweaked toml for crates.io crate easynn 0.1.7-beta written to /workspace/builds/worker-5-tc1/source/Cargo.toml
[INFO] running `Command { std: CARGO_HOME="/workspace/cargo-home" RUSTUP_HOME="/workspace/rustup-home" "/workspace/cargo-home/bin/cargo" "+1.75.0" "generate-lockfile" "--manifest-path" "Cargo.toml", kill_on_drop: false }`
[INFO] [stderr]     Updating crates.io index
[INFO] running `Command { std: CARGO_HOME="/workspace/cargo-home" RUSTUP_HOME="/workspace/rustup-home" "/workspace/cargo-home/bin/cargo" "+1.75.0" "fetch" "--manifest-path" "Cargo.toml", kill_on_drop: false }`
[INFO] running `Command { std: "docker" "create" "-v" "/var/lib/crater-agent-workspace/builds/worker-5-tc1/target:/opt/rustwide/target:rw,Z" "-v" "/var/lib/crater-agent-workspace/builds/worker-5-tc1/source:/opt/rustwide/workdir:ro,Z" "-v" "/var/lib/crater-agent-workspace/cargo-home:/opt/rustwide/cargo-home:ro,Z" "-v" "/var/lib/crater-agent-workspace/rustup-home:/opt/rustwide/rustup-home:ro,Z" "-e" "SOURCE_DIR=/opt/rustwide/workdir" "-e" "CARGO_TARGET_DIR=/opt/rustwide/target" "-e" "CARGO_HOME=/opt/rustwide/cargo-home" "-e" "RUSTUP_HOME=/opt/rustwide/rustup-home" "-w" "/opt/rustwide/workdir" "-m" "1610612736" "--user" "0:0" "--network" "none" "ghcr.io/rust-lang/crates-build-env/linux@sha256:2788e3201cd34a07e3172128adcd8b3090168a8e3bcc40d7c032b9dda1df7d1c" "/opt/rustwide/cargo-home/bin/cargo" "+1.75.0" "metadata" "--no-deps" "--format-version=1", kill_on_drop: false }`
[INFO] [stdout] 7f3f23f92cf5c08a42b08eb476cf28e722d2600cd2802e0c23b5167aa41f8e25
[INFO] running `Command { std: "docker" "start" "-a" "7f3f23f92cf5c08a42b08eb476cf28e722d2600cd2802e0c23b5167aa41f8e25", kill_on_drop: false }`
[INFO] running `Command { std: "docker" "inspect" "7f3f23f92cf5c08a42b08eb476cf28e722d2600cd2802e0c23b5167aa41f8e25", kill_on_drop: false }`
[INFO] running `Command { std: "docker" "rm" "-f" "7f3f23f92cf5c08a42b08eb476cf28e722d2600cd2802e0c23b5167aa41f8e25", kill_on_drop: false }`
[INFO] [stdout] 7f3f23f92cf5c08a42b08eb476cf28e722d2600cd2802e0c23b5167aa41f8e25
[INFO] running `Command { std: "docker" "create" "-v" "/var/lib/crater-agent-workspace/builds/worker-5-tc1/target:/opt/rustwide/target:rw,Z" "-v" "/var/lib/crater-agent-workspace/builds/worker-5-tc1/source:/opt/rustwide/workdir:ro,Z" "-v" "/var/lib/crater-agent-workspace/cargo-home:/opt/rustwide/cargo-home:ro,Z" "-v" "/var/lib/crater-agent-workspace/rustup-home:/opt/rustwide/rustup-home:ro,Z" "-e" "SOURCE_DIR=/opt/rustwide/workdir" "-e" "CARGO_TARGET_DIR=/opt/rustwide/target" "-e" "CARGO_INCREMENTAL=0" "-e" "RUST_BACKTRACE=full" "-e" "RUSTFLAGS=--cap-lints=warn" "-e" "RUSTDOCFLAGS=--cap-lints=warn" "-e" "CARGO_HOME=/opt/rustwide/cargo-home" "-e" "RUSTUP_HOME=/opt/rustwide/rustup-home" "-w" "/opt/rustwide/workdir" "-m" "1610612736" "--user" "0:0" "--network" "none" "ghcr.io/rust-lang/crates-build-env/linux@sha256:2788e3201cd34a07e3172128adcd8b3090168a8e3bcc40d7c032b9dda1df7d1c" "/opt/rustwide/cargo-home/bin/cargo" "+1.75.0" "build" "--frozen" "--message-format=json", kill_on_drop: false }`
[INFO] [stdout] e1afcde7bcafed5dae33a44e14ef38440e3fb4193b826faf70c799c1ef4aec17
[INFO] running `Command { std: "docker" "start" "-a" "e1afcde7bcafed5dae33a44e14ef38440e3fb4193b826faf70c799c1ef4aec17", kill_on_drop: false }`
[INFO] [stderr]    Compiling crossbeam-utils v0.8.18
[INFO] [stderr]    Compiling libc v0.2.151
[INFO] [stderr]    Compiling rayon-core v1.12.0
[INFO] [stderr]    Compiling ppv-lite86 v0.2.17
[INFO] [stderr]    Compiling either v1.9.0
[INFO] [stderr]    Compiling num-traits v0.2.17
[INFO] [stderr]    Compiling itertools v0.10.5
[INFO] [stderr]    Compiling crossbeam-epoch v0.9.17
[INFO] [stderr]    Compiling crossbeam-channel v0.5.10
[INFO] [stderr]    Compiling crossbeam-queue v0.3.10
[INFO] [stderr]    Compiling crossbeam-deque v0.8.4
[INFO] [stderr]    Compiling getrandom v0.2.11
[INFO] [stderr]    Compiling num_cpus v1.16.0
[INFO] [stderr]    Compiling crossbeam v0.8.3
[INFO] [stderr]    Compiling rand_core v0.6.4
[INFO] [stderr]    Compiling rand_chacha v0.3.1
[INFO] [stderr]    Compiling rand v0.8.5
[INFO] [stderr]    Compiling rayon v1.8.0
[INFO] [stderr]    Compiling easynn v0.1.7-beta (/opt/rustwide/workdir)
[INFO] [stdout] warning: unused variable: `olen`
[INFO] [stdout]   --> src/layers/dense.rs:96:13
[INFO] [stdout]    |
[INFO] [stdout] 96 |         let olen = output.flattened.len();
[INFO] [stdout]    |             ^^^^ help: if this is intentional, prefix it with an underscore: `_olen`
[INFO] [stdout]    |
[INFO] [stdout]    = note: `#[warn(unused_variables)]` on by default
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: unused variable: `dlen`
[INFO] [stdout]    --> src/layers/dense.rs:148:13
[INFO] [stdout]     |
[INFO] [stdout] 148 |         let dlen = delta.flattened.len();
[INFO] [stdout]     |             ^^^^ help: if this is intentional, prefix it with an underscore: `_dlen`
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: unused variable: `dlen`
[INFO] [stdout]    --> src/layers/dense.rs:205:13
[INFO] [stdout]     |
[INFO] [stdout] 205 |         let dlen = delta.flattened.len();
[INFO] [stdout]     |             ^^^^ help: if this is intentional, prefix it with an underscore: `_dlen`
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: variable does not need to be mutable
[INFO] [stdout]    --> src/models/sequential.rs:137:17
[INFO] [stdout]     |
[INFO] [stdout] 137 |             let mut cum_dw = &dws.par_chunks_mut(1).reduce_with(|dw1, dw2| {
[INFO] [stdout]     |                 ----^^^^^^
[INFO] [stdout]     |                 |
[INFO] [stdout]     |                 help: remove this `mut`
[INFO] [stdout]     |
[INFO] [stdout]     = note: `#[warn(unused_mut)]` on by default
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: variable does not need to be mutable
[INFO] [stdout]    --> src/models/sequential.rs:146:17
[INFO] [stdout]     |
[INFO] [stdout] 146 |             let mut cum_db = &dbs.par_chunks_mut(1).reduce_with(|db1, db2| {
[INFO] [stdout]     |                 ----^^^^^^
[INFO] [stdout]     |                 |
[INFO] [stdout]     |                 help: remove this `mut`
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: function `determine_thread` is never used
[INFO] [stdout]   --> src/layers/dense.rs:18:4
[INFO] [stdout]    |
[INFO] [stdout] 18 | fn determine_thread(len: usize) -> usize {
[INFO] [stdout]    |    ^^^^^^^^^^^^^^^^
[INFO] [stdout]    |
[INFO] [stdout]    = note: `#[warn(dead_code)]` on by default
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: method `pos2index` is never used
[INFO] [stdout]   --> src/tensor/mod.rs:38:19
[INFO] [stdout]    |
[INFO] [stdout] 26 | impl<T: NumT> Tensor<T> {
[INFO] [stdout]    | ----------------------- method in this implementation
[INFO] [stdout] ...
[INFO] [stdout] 38 |     pub(crate) fn pos2index<const RANK: usize>(&self, mut pos: usize) -> Result<TensorIndex<RANK>> {
[INFO] [stdout]    |                   ^^^^^^^^^
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: 7 warnings emitted
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stderr]     Finished dev [unoptimized + debuginfo] target(s) in 2.58s
[INFO] running `Command { std: "docker" "inspect" "e1afcde7bcafed5dae33a44e14ef38440e3fb4193b826faf70c799c1ef4aec17", kill_on_drop: false }`
[INFO] running `Command { std: "docker" "rm" "-f" "e1afcde7bcafed5dae33a44e14ef38440e3fb4193b826faf70c799c1ef4aec17", kill_on_drop: false }`
[INFO] [stdout] e1afcde7bcafed5dae33a44e14ef38440e3fb4193b826faf70c799c1ef4aec17
[INFO] running `Command { std: "docker" "create" "-v" "/var/lib/crater-agent-workspace/builds/worker-5-tc1/target:/opt/rustwide/target:rw,Z" "-v" "/var/lib/crater-agent-workspace/builds/worker-5-tc1/source:/opt/rustwide/workdir:ro,Z" "-v" "/var/lib/crater-agent-workspace/cargo-home:/opt/rustwide/cargo-home:ro,Z" "-v" "/var/lib/crater-agent-workspace/rustup-home:/opt/rustwide/rustup-home:ro,Z" "-e" "SOURCE_DIR=/opt/rustwide/workdir" "-e" "CARGO_TARGET_DIR=/opt/rustwide/target" "-e" "CARGO_INCREMENTAL=0" "-e" "RUST_BACKTRACE=full" "-e" "RUSTFLAGS=--cap-lints=warn" "-e" "RUSTDOCFLAGS=--cap-lints=warn" "-e" "CARGO_HOME=/opt/rustwide/cargo-home" "-e" "RUSTUP_HOME=/opt/rustwide/rustup-home" "-w" "/opt/rustwide/workdir" "-m" "1610612736" "--user" "0:0" "--network" "none" "ghcr.io/rust-lang/crates-build-env/linux@sha256:2788e3201cd34a07e3172128adcd8b3090168a8e3bcc40d7c032b9dda1df7d1c" "/opt/rustwide/cargo-home/bin/cargo" "+1.75.0" "test" "--frozen" "--no-run" "--message-format=json", kill_on_drop: false }`
[INFO] [stdout] d925b92d73689d4e77f69387e2f15666ddc27c8c8805d76f97c420cfd31ebc33
[INFO] running `Command { std: "docker" "start" "-a" "d925b92d73689d4e77f69387e2f15666ddc27c8c8805d76f97c420cfd31ebc33", kill_on_drop: false }`
[INFO] [stdout] warning: unused variable: `olen`
[INFO] [stdout]   --> src/layers/dense.rs:96:13
[INFO] [stdout]    |
[INFO] [stdout] 96 |         let olen = output.flattened.len();
[INFO] [stdout]    |             ^^^^ help: if this is intentional, prefix it with an underscore: `_olen`
[INFO] [stdout]    |
[INFO] [stdout]    = note: `#[warn(unused_variables)]` on by default
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: unused variable: `dlen`
[INFO] [stdout]    --> src/layers/dense.rs:148:13
[INFO] [stdout]     |
[INFO] [stdout] 148 |         let dlen = delta.flattened.len();
[INFO] [stdout]     |             ^^^^ help: if this is intentional, prefix it with an underscore: `_dlen`
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: unused variable: `dlen`
[INFO] [stdout]    --> src/layers/dense.rs:205:13
[INFO] [stdout]     |
[INFO] [stdout] 205 |         let dlen = delta.flattened.len();
[INFO] [stdout]     |             ^^^^ help: if this is intentional, prefix it with an underscore: `_dlen`
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: variable does not need to be mutable
[INFO] [stdout]    --> src/models/sequential.rs:137:17
[INFO] [stdout]     |
[INFO] [stdout] 137 |             let mut cum_dw = &dws.par_chunks_mut(1).reduce_with(|dw1, dw2| {
[INFO] [stdout]     |                 ----^^^^^^
[INFO] [stdout]     |                 |
[INFO] [stdout]     |                 help: remove this `mut`
[INFO] [stdout]     |
[INFO] [stdout]     = note: `#[warn(unused_mut)]` on by default
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: variable does not need to be mutable
[INFO] [stdout]    --> src/models/sequential.rs:146:17
[INFO] [stdout]     |
[INFO] [stdout] 146 |             let mut cum_db = &dbs.par_chunks_mut(1).reduce_with(|db1, db2| {
[INFO] [stdout]     |                 ----^^^^^^
[INFO] [stdout]     |                 |
[INFO] [stdout]     |                 help: remove this `mut`
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: function `determine_thread` is never used
[INFO] [stdout]   --> src/layers/dense.rs:18:4
[INFO] [stdout]    |
[INFO] [stdout] 18 | fn determine_thread(len: usize) -> usize {
[INFO] [stdout]    |    ^^^^^^^^^^^^^^^^
[INFO] [stdout]    |
[INFO] [stdout]    = note: `#[warn(dead_code)]` on by default
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stderr]    Compiling easynn v0.1.7-beta (/opt/rustwide/workdir)
[INFO] [stdout] warning: method `pos2index` is never used
[INFO] [stdout]   --> src/tensor/mod.rs:38:19
[INFO] [stdout]    |
[INFO] [stdout] 26 | impl<T: NumT> Tensor<T> {
[INFO] [stdout]    | ----------------------- method in this implementation
[INFO] [stdout] ...
[INFO] [stdout] 38 |     pub(crate) fn pos2index<const RANK: usize>(&self, mut pos: usize) -> Result<TensorIndex<RANK>> {
[INFO] [stdout]    |                   ^^^^^^^^^
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: 7 warnings emitted
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: unused import: `crate::layers::activation::Activation::*`
[INFO] [stdout]    --> src/models/sequential.rs:180:9
[INFO] [stdout]     |
[INFO] [stdout] 180 |     use crate::layers::activation::Activation::*;
[INFO] [stdout]     |         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[INFO] [stdout]     |
[INFO] [stdout]     = note: `#[warn(unused_imports)]` on by default
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: unused import: `rand::Rng`
[INFO] [stdout]    --> src/models/sequential.rs:207:9
[INFO] [stdout]     |
[INFO] [stdout] 207 |     use rand::Rng;
[INFO] [stdout]     |         ^^^^^^^^^
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: unused variable: `olen`
[INFO] [stdout]   --> src/layers/dense.rs:96:13
[INFO] [stdout]    |
[INFO] [stdout] 96 |         let olen = output.flattened.len();
[INFO] [stdout]    |             ^^^^ help: if this is intentional, prefix it with an underscore: `_olen`
[INFO] [stdout]    |
[INFO] [stdout]    = note: `#[warn(unused_variables)]` on by default
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: unused variable: `dlen`
[INFO] [stdout]    --> src/layers/dense.rs:148:13
[INFO] [stdout]     |
[INFO] [stdout] 148 |         let dlen = delta.flattened.len();
[INFO] [stdout]     |             ^^^^ help: if this is intentional, prefix it with an underscore: `_dlen`
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: unused variable: `dlen`
[INFO] [stdout]    --> src/layers/dense.rs:205:13
[INFO] [stdout]     |
[INFO] [stdout] 205 |         let dlen = delta.flattened.len();
[INFO] [stdout]     |             ^^^^ help: if this is intentional, prefix it with an underscore: `_dlen`
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: variable does not need to be mutable
[INFO] [stdout]    --> src/models/sequential.rs:137:17
[INFO] [stdout]     |
[INFO] [stdout] 137 |             let mut cum_dw = &dws.par_chunks_mut(1).reduce_with(|dw1, dw2| {
[INFO] [stdout]     |                 ----^^^^^^
[INFO] [stdout]     |                 |
[INFO] [stdout]     |                 help: remove this `mut`
[INFO] [stdout]     |
[INFO] [stdout]     = note: `#[warn(unused_mut)]` on by default
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: variable does not need to be mutable
[INFO] [stdout]    --> src/models/sequential.rs:146:17
[INFO] [stdout]     |
[INFO] [stdout] 146 |             let mut cum_db = &dbs.par_chunks_mut(1).reduce_with(|db1, db2| {
[INFO] [stdout]     |                 ----^^^^^^
[INFO] [stdout]     |                 |
[INFO] [stdout]     |                 help: remove this `mut`
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: function `determine_thread` is never used
[INFO] [stdout]   --> src/layers/dense.rs:18:4
[INFO] [stdout]    |
[INFO] [stdout] 18 | fn determine_thread(len: usize) -> usize {
[INFO] [stdout]    |    ^^^^^^^^^^^^^^^^
[INFO] [stdout]    |
[INFO] [stdout]    = note: `#[warn(dead_code)]` on by default
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: method `pos2index` is never used
[INFO] [stdout]   --> src/tensor/mod.rs:38:19
[INFO] [stdout]    |
[INFO] [stdout] 26 | impl<T: NumT> Tensor<T> {
[INFO] [stdout]    | ----------------------- method in this implementation
[INFO] [stdout] ...
[INFO] [stdout] 38 |     pub(crate) fn pos2index<const RANK: usize>(&self, mut pos: usize) -> Result<TensorIndex<RANK>> {
[INFO] [stdout]    |                   ^^^^^^^^^
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: 9 warnings emitted
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stderr]     Finished test [unoptimized + debuginfo] target(s) in 0.74s
[INFO] running `Command { std: "docker" "inspect" "d925b92d73689d4e77f69387e2f15666ddc27c8c8805d76f97c420cfd31ebc33", kill_on_drop: false }`
[INFO] running `Command { std: "docker" "rm" "-f" "d925b92d73689d4e77f69387e2f15666ddc27c8c8805d76f97c420cfd31ebc33", kill_on_drop: false }`
[INFO] [stdout] d925b92d73689d4e77f69387e2f15666ddc27c8c8805d76f97c420cfd31ebc33
[INFO] running `Command { std: "docker" "create" "-v" "/var/lib/crater-agent-workspace/builds/worker-5-tc1/target:/opt/rustwide/target:rw,Z" "-v" "/var/lib/crater-agent-workspace/builds/worker-5-tc1/source:/opt/rustwide/workdir:ro,Z" "-v" "/var/lib/crater-agent-workspace/cargo-home:/opt/rustwide/cargo-home:ro,Z" "-v" "/var/lib/crater-agent-workspace/rustup-home:/opt/rustwide/rustup-home:ro,Z" "-e" "SOURCE_DIR=/opt/rustwide/workdir" "-e" "CARGO_TARGET_DIR=/opt/rustwide/target" "-e" "CARGO_INCREMENTAL=0" "-e" "RUST_BACKTRACE=full" "-e" "RUSTFLAGS=--cap-lints=warn" "-e" "RUSTDOCFLAGS=--cap-lints=warn" "-e" "CARGO_HOME=/opt/rustwide/cargo-home" "-e" "RUSTUP_HOME=/opt/rustwide/rustup-home" "-w" "/opt/rustwide/workdir" "-m" "1610612736" "--user" "0:0" "--network" "none" "ghcr.io/rust-lang/crates-build-env/linux@sha256:2788e3201cd34a07e3172128adcd8b3090168a8e3bcc40d7c032b9dda1df7d1c" "/opt/rustwide/cargo-home/bin/cargo" "+1.75.0" "test" "--frozen", kill_on_drop: false }`
[INFO] [stdout] f559a1b6a4dcdfe64820bd1bc18ea259892b48b7c9ada950f07e16e0ea07cdf9
[INFO] running `Command { std: "docker" "start" "-a" "f559a1b6a4dcdfe64820bd1bc18ea259892b48b7c9ada950f07e16e0ea07cdf9", kill_on_drop: false }`
[INFO] [stderr] warning: unused variable: `olen`
[INFO] [stderr]   --> src/layers/dense.rs:96:13
[INFO] [stderr]    |
[INFO] [stderr] 96 |         let olen = output.flattened.len();
[INFO] [stderr]    |             ^^^^ help: if this is intentional, prefix it with an underscore: `_olen`
[INFO] [stderr]    |
[INFO] [stderr]    = note: `#[warn(unused_variables)]` on by default
[INFO] [stderr] 
[INFO] [stderr] warning: unused variable: `dlen`
[INFO] [stderr]    --> src/layers/dense.rs:148:13
[INFO] [stderr]     |
[INFO] [stderr] 148 |         let dlen = delta.flattened.len();
[INFO] [stderr]     |             ^^^^ help: if this is intentional, prefix it with an underscore: `_dlen`
[INFO] [stderr] 
[INFO] [stderr] warning: unused variable: `dlen`
[INFO] [stderr]    --> src/layers/dense.rs:205:13
[INFO] [stderr]     |
[INFO] [stderr] 205 |         let dlen = delta.flattened.len();
[INFO] [stderr]     |             ^^^^ help: if this is intentional, prefix it with an underscore: `_dlen`
[INFO] [stderr] 
[INFO] [stderr] warning: variable does not need to be mutable
[INFO] [stderr]    --> src/models/sequential.rs:137:17
[INFO] [stderr]     |
[INFO] [stderr] 137 |             let mut cum_dw = &dws.par_chunks_mut(1).reduce_with(|dw1, dw2| {
[INFO] [stderr]     |                 ----^^^^^^
[INFO] [stderr]     |                 |
[INFO] [stderr]     |                 help: remove this `mut`
[INFO] [stderr]     |
[INFO] [stderr]     = note: `#[warn(unused_mut)]` on by default
[INFO] [stderr] 
[INFO] [stderr] warning: variable does not need to be mutable
[INFO] [stderr]    --> src/models/sequential.rs:146:17
[INFO] [stderr]     |
[INFO] [stderr] 146 |             let mut cum_db = &dbs.par_chunks_mut(1).reduce_with(|db1, db2| {
[INFO] [stderr]     |                 ----^^^^^^
[INFO] [stderr]     |                 |
[INFO] [stderr]     |                 help: remove this `mut`
[INFO] [stderr] 
[INFO] [stderr] warning: function `determine_thread` is never used
[INFO] [stderr]   --> src/layers/dense.rs:18:4
[INFO] [stderr]    |
[INFO] [stderr] 18 | fn determine_thread(len: usize) -> usize {
[INFO] [stderr]    |    ^^^^^^^^^^^^^^^^
[INFO] [stderr]    |
[INFO] [stderr]    = note: `#[warn(dead_code)]` on by default
[INFO] [stderr] 
[INFO] [stderr] warning: method `pos2index` is never used
[INFO] [stderr]   --> src/tensor/mod.rs:38:19
[INFO] [stderr]    |
[INFO] [stderr] 26 | impl<T: NumT> Tensor<T> {
[INFO] [stderr]    | ----------------------- method in this implementation
[INFO] [stderr] ...
[INFO] [stderr] 38 |     pub(crate) fn pos2index<const RANK: usize>(&self, mut pos: usize) -> Result<TensorIndex<RANK>> {
[INFO] [stderr]    |                   ^^^^^^^^^
[INFO] [stderr] 
[INFO] [stderr] warning: unused import: `crate::layers::activation::Activation::*`
[INFO] [stderr]    --> src/models/sequential.rs:180:9
[INFO] [stderr]     |
[INFO] [stderr] 180 |     use crate::layers::activation::Activation::*;
[INFO] [stderr]     |         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[INFO] [stderr]     |
[INFO] [stderr]     = note: `#[warn(unused_imports)]` on by default
[INFO] [stderr] 
[INFO] [stderr] warning: unused import: `rand::Rng`
[INFO] [stderr]    --> src/models/sequential.rs:207:9
[INFO] [stderr]     |
[INFO] [stderr] 207 |     use rand::Rng;
[INFO] [stderr]     |         ^^^^^^^^^
[INFO] [stderr] 
[INFO] [stderr] warning: `easynn` (lib) generated 7 warnings (run `cargo fix --lib -p easynn` to apply 5 suggestions)
[INFO] [stderr] warning: `easynn` (lib test) generated 9 warnings (7 duplicates) (run `cargo fix --lib -p easynn --tests` to apply 2 suggestions)
[INFO] [stderr]     Finished test [unoptimized + debuginfo] target(s) in 0.01s
[INFO] [stderr]      Running unittests src/lib.rs (/opt/rustwide/target/debug/deps/easynn-264e313ca7fd68b3)
[INFO] [stdout] 
[INFO] [stdout] running 7 tests
[INFO] [stdout] test layers::dense::test_add_weight_delta_to ... ok
[INFO] [stdout] test layers::dense::test_dense_descend ... ok
[INFO] [stdout] test layers::dense::test_dense_forward ... ok
[INFO] [stdout] test models::sequential::test_sequential_predict ... ok
[INFO] [stdout] test layers::dense::test_dense_activate ... ok
[INFO] [stdout] test layers::dense::test_dense_backpropagate ... ok
[INFO] [stdout] test models::sequential::test_sequential_xor1 ... FAILED
[INFO] [stdout] 
[INFO] [stdout] failures:
[INFO] [stdout] 
[INFO] [stdout] ---- models::sequential::test_sequential_xor1 stdout ----
[INFO] [stdout] [Epoch 0]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.00000000004394687358116856
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.9999870067243212
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.9603875212498082
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.0015686541993598412
[INFO] [stdout] [Epoch 1]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.0015065354930526354
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.9253712120498386
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.8887265120454354
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.005796010494534985
[INFO] [stdout] [Epoch 2]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.005566488478907544
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.8591126047093564
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.8250917455564503
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.012060718613125103
[INFO] [stdout] [Epoch 3]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.011583114155958
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.8001796638551426
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.7684925491607318
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.019851590745820814
[INFO] [stdout] [Epoch 4]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.01906546775214768
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.7476780660166044
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.7180700145971536
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.028749807833726174
[INFO] [stdout] [Epoch 5]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.027611315443315913
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.7008315100208009
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.6730785822192469
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.03841388636657972
[INFO] [stdout] [Epoch 6]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.03689269646620965
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.6589653026347068
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.6328702766460319
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.04856698560372831
[INFO] [stdout] [Epoch 7]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.04664373297350705
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.6214923007612225
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.5968812056470687
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.05898619841668852
[INFO] [stdout] [Epoch 8]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.05665034495901368
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.587900865729584
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.564619991442966
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.06949352278412553
[INFO] [stdout] [Epoch 9]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.06674157928144027
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.5577445360659595
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.5356578524342642
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.07994825672274047
[INFO] [stdout] [Epoch 10]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.07678230575627201
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.5306331684301315
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.509620094958645
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.09024059832166406
[INFO] [stdout] [Epoch 11]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.08666707062785224
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.5062253332889591
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.4861788100891714
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.10028626558911961
[INFO] [stdout] [Epoch 12]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.09631492947149187
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.48422178328576987
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.4650466006662026
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.11002197889180576
[INFO] [stdout] [Epoch 13]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.10566510852736821
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.46435983907305317
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.445971189444392
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.11940167262761546
[INFO] [stdout] [Epoch 14]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.11467336639121761
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.4464085601118208
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.42873078113009677
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.12839332302753112
[INFO] [stdout] [Epoch 15]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.12330894743527569
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.4301645874080403
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.4131300697454496
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.13697629619648624
[INFO] [stdout] [Epoch 16]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.13155203486672043
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.41544856169126715
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.3989967986471167
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.145139135118825
[INFO] [stdout] [Epoch 17]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.13939162536771607
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.4021020346497294
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.3861787940764737
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.15287771676536313
[INFO] [stdout] [Epoch 18]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.14682375918103388
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.38998480286007176
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.3745414046657309
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.1601937209762762
[INFO] [stdout] [Epoch 19]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.15385004962517845
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.3789726043005331
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.3639652891691894
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.1670933617385148
[INFO] [stdout] [Epoch 20]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.16047646461321707
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.36895512607654884
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.3543445030829104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.17358633906742976
[INFO] [stdout] [Epoch 21]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.16671232003989275
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.35983427944152857
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.3455848419746686
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.17968497614323026
[INFO] [stdout] [Epoch 22]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.17256945108747812
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.35152270455343854
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.33760240545217535
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.18540351181674708
[INFO] [stdout] [Epoch 23]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.1780615327483112
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.3439424728321247
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.3303223509070513
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.19075752323279285
[INFO] [stdout] [Epoch 24]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.18320352531226997
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.33702395941112734
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.32367781061754863
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.19576345724819663
[INFO] [stdout] [Epoch 25]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.18801122434065293
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.3307048621287477
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.31760894958757224
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2004382526516149
[INFO] [stdout] [Epoch 26]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.1925008978460858
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.3249293468764345
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.31206214473926946
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.20479903801380303
[INFO] [stdout] [Epoch 27]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.19668899610792184
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.3196473020034726
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.30698926884329425
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2088628923869418
[INFO] [stdout] [Epoch 28]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2005919218478756
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.31481368693810485
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.3023470649345306
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.21264665809509636
[INFO] [stdout] [Epoch 29]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.20422585043397923
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.31038796228843596
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.29809659898100277
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2161667965703823
[INFO] [stdout] [Epoch 30]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2076065914256363
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.30633359048447933
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2942027803004958
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.21943927963803264
[INFO] [stdout] [Epoch 31]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.21074948416380074
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.30261759756034484
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2906339406961688
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2224795098783337
[INFO] [stdout] [Epoch 32]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.21366932128657948
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.29921018799111315
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.28736146454588973
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2253022647283139
[INFO] [stdout] [Epoch 33]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.21638029504449457
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2960844056249308
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2843594631614181
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.22792165986002796
[INFO] [stdout] [Epoch 34]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.21889596212898726
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2932158347150795
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.28160448765960583
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.23035112810978903
[INFO] [stdout] [Epoch 35]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.22122922343605267
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.29058233588287435
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2790752753811644
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.23260341085460592
[INFO] [stdout] [Epoch 36]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.22339231578417015
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2881638125503794
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2767525255726437
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2346905592560605
[INFO] [stdout] [Epoch 37]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.22539681310892284
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2859420039892989
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.27461870063058896
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2366239432329173
[INFO] [stdout] [Epoch 38]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.22725363508029214
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2839003016536723
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.27265784970745965
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2384142663946475
[INFO] [stdout] [Epoch 39]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.22897306144481414
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2820235859116403
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2708554519088181
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.24007158547956844
[INFO] [stdout] [Epoch 40]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.23056475069396884
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.28029808067625667
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.26919827668076085
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.24160533310262966
[INFO] [stdout] [Epoch 41]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.23203776191115372
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.278711223766159
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2676742593043081
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.24302434283678703
[INFO] [stdout] [Epoch 42]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.23340057885983564
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.27725155111166766
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2662723896869394
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.24433687583500346
[INFO] [stdout] [Epoch 43]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2346611355513201
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2759085931671553
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2649826128770339
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.24555064835277224
[INFO] [stdout] [Epoch 44]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.23582684267738283
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2746727821020027
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2637957399300653
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2466728596584342
[INFO] [stdout] [Epoch 45]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.23690461441533833
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2735353685249329
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2627033679306509
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2477102199244458
[INFO] [stdout] [Epoch 46]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2379008952148139
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2724883466541405
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.26169780812594523
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.24866897778056476
[INFO] [stdout] [Epoch 47]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.23882168625982872
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.27152438698192366
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.26077202125675136
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.24955494728251684
[INFO] [stdout] [Epoch 48]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.23967257136950182
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.27063677560049165
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25991955928602695
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2503735341095356
[INFO] [stdout] [Epoch 49]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24045874215816904
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.269819359457855
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2591345128226413
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25112976085327193
[INFO] [stdout] [Epoch 50]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24118502232285208
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2690664969013875
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2584114636234124
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2518282913007024
[INFO] [stdout] [Epoch 51]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24185589096456303
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26837301294369476
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25774544163044666
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2524734536462783
[INFO] [stdout] [Epoch 52]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24247550488125297
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2677341587524237
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2571318860651521
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2530692625949097
[INFO] [stdout] [Epoch 53]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2430477197955176
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26714557492400115
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.256566610156337
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25361944033849176
[INFO] [stdout] [Epoch 54]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24357611050045283
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2666032581521808
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25604576912868254
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25412743640545743
[INFO] [stdout] [Epoch 55]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24406398992316583
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2661035309467119
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2555658311205519
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25459644639600737
[INFO] [stdout] [Epoch 56]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24451442711808913
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2656430140963203
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2551235507374375
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2550294296258543
[INFO] [stdout] [Epoch 57]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24493026421203354
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26521860160422334
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.254715944980029
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2554291257090475
[INFO] [stdout] [Epoch 58]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24531413233033164
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.264827437854273
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25434027131457826
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2557980701161552
[INFO] [stdout] [Epoch 59]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24566846653891736
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2644668967920513
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2539940076784217
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25613860874814404
[INFO] [stdout] [Epoch 60]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2459955198410789
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2641345629283296
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2536748342357046
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25645291156901123
[INFO] [stdout] [Epoch 61]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2462973762702394
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26382821399265544
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25338061671788425
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2567429853418647
[INFO] [stdout] [Epoch 62]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2465759631216875
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2635458050827865
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2531093912008473
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25701068551390877
[INFO] [stdout] [Epoch 63]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2468330623669183
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26328545417158145
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2528593501857269
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2572577272958674
[INFO] [stdout] [Epoch 64]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24707032129431106
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2630454288470157
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25262882986401497
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2574856959809092
[INFO] [stdout] [Epoch 65]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24728926241942495
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2628241341734814
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25241629845955343
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2576960565472445
[INFO] [stdout] [Epoch 66]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24749129270733317
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2626201015736052
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25222034555063316
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25789016258735914
[INFO] [stdout] [Epoch 67]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24767771214825912
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2624319786396972
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2520396722849087
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25806926460539614
[INFO] [stdout] [Epoch 68]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24784972172638173
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2622585197927362
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25187308240828815
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25823451772258493
[INFO] [stdout] [Epoch 69]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24800843082012977
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2620985777146506
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25171947403649547
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2583869888288827
[INFO] [stdout] [Epoch 70]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24815486407061815
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2619510954866804
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2515778321047536
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25852766321719495
[INFO] [stdout] [Epoch 71]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24828996775315315
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26181509937289227
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2514472214370722
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2586574507347035
[INFO] [stdout] [Epoch 72]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2484146156849683
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26168969219356686
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2513267803820486
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.258777191483998
[INFO] [stdout] [Epoch 73]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2485296147005907
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2615740472382376
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25121571496695105
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25888766110487665
[INFO] [stdout] [Epoch 74]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24863570972448262
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2614674026727282
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25111329352623635
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25898957566590075
[INFO] [stdout] [Epoch 75]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2487335884688902
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26136905639863306
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25101884176459605
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25908359619304355
[INFO] [stdout] [Epoch 76]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24882388578315817
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26127836132739496
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25093173821817955
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2591703328610954
[INFO] [stdout] [Epoch 77]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24890718767915532
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26119472103447566
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25085141008086015
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2592503488718681
[INFO] [stdout] [Epoch 78]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24898403505590153
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26111758576214117
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25077732936531066
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25932416404169356
[INFO] [stdout] [Epoch 79]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249054927145002
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2610464487421197
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25070900937128243
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2593922581192389
[INFO] [stdout] [Epoch 80]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24912032469707657
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2609808428118743
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2506460014358753
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2594550738532561
[INFO] [stdout] [Epoch 81]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24918065292802682
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26092033730048164
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2505878919427341
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2595130198285617
[INFO] [stdout] [Epoch 82]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24923630424271043
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2608645351621531
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2505342995690838
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25956647308728414
[INFO] [stdout] [Epoch 83]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2492876407523876
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26081307033729567
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25048487275129117
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2596157815512401
[INFO] [stdout] [Epoch 84]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24933499660117106
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2607656053227016
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2504392873512754
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.259661266260188
[INFO] [stdout] [Epoch 85]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24937868011564482
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2607218289339928
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25039724450755996
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2597032234396662
[INFO] [stdout] [Epoch 86]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24941897579081584
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2606814542448569
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25035846865611416
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25974192641114646
[INFO] [stdout] [Epoch 87]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2494561461246257
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2606442166888874
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2503227057073615
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2597776273563193
[INFO] [stdout] [Epoch 88]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24949043331236997
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2606098723110154
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25028972136685346
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25981055894647437
[INFO] [stdout] [Epoch 89]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24952206081155506
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2605781961565843
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2502592995881382
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25984093584713996
[INFO] [stdout] [Epoch 90]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24955123478695443
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26054898078710065
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2502312411472864
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25986895610740635
[INFO] [stdout] [Epoch 91]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24957814544491447
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26052203491258435
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25020536232940144
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2598948024426612
[INFO] [stdout] [Epoch 92]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2496029682652935
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2604971821312615
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25018149371821924
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25991864341882276
[INFO] [stdout] [Epoch 93]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24962586513879917
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2604742597680926
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25015947908063213
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25994063454555616
[INFO] [stdout] [Epoch 94]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24964698541691413
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2604531178043146
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25013917433862026
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.259960919285403
[INFO] [stdout] [Epoch 95]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24966646688106325
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2604336178908053
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2501204466216862
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2599796299852346
[INFO] [stdout] [Epoch 96]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24968443663718173
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2604156324386534
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2501031733934398
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25999688873595944
[INFO] [stdout] [Epoch 97]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24970101194137811
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2603990437808521
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25008724164648766
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2600128081659715
[INFO] [stdout] [Epoch 98]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24971630096196196
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2603837433995149
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2500725471602518
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2600274921734102
[INFO] [stdout] [Epoch 99]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24973040348270634
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2603696312134617
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25005899381676644
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26004103660192235
[INFO] [stdout] [Epoch 100]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24974341155184956
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260356614921431
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25004649296990056
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2600535298642587
[INFO] [stdout] [Epoch 101]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24975541008099766
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2603446093965543
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2500349628638092
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26006505351771264
[INFO] [stdout] [Epoch 102]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2497664773977751
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2603335361280696
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2500243280967568
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2600756827950997
[INFO] [stdout] [Epoch 103]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24977668575577786
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26032332270657443
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2500145191267531
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2600854870946992
[INFO] [stdout] [Epoch 104]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2497861018051135
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2603139023494064
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25000547181572935
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2600945304323158
[INFO] [stdout] [Epoch 105]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24979478702656063
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2603052134630149
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499971270092392
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601028718583777
[INFO] [stdout] [Epoch 106]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24980279813215076
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602971992394257
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499894301489042
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26011056584276954
[INFO] [stdout] [Epoch 107]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24981018743476083
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26028980728413703
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24998233091504551
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601176626298821
[INFO] [stdout] [Epoch 108]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498170031891041
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602829892729931
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24997578289714303
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26012420856618335
[INFO] [stdout] [Epoch 109]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24982328990632802
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26027670063576647
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24996974328995084
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601302464024262
[INFO] [stdout] [Epoch 110]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24982908864425588
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602709002643711
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24996417261326312
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601358155724556
[INFO] [stdout] [Epoch 111]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498344372751524
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260265550243781
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499590344534887
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26014095245042096
[INFO] [stdout] [Epoch 112]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24983937073275053
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26026061560388297
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24995429522533072
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601456905880629
[INFO] [stdout] [Epoch 113]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498439212401421
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602560640906341
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24994992395200694
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601500609336159
[INFO] [stdout] [Epoch 114]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24984811852001157
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26025186595501854
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24994589206256204
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260154092033748
[INFO] [stdout] [Epoch 115]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24985198998857855
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602479937584144
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24994217320494347
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601578102198481
[INFO] [stdout] [Epoch 116]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24985556093450934
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26024442219309635
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24993874307361244
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26016123977987626
[INFO] [stdout] [Epoch 117]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24985885468396066
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26024112791669335
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24993557925055518
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601644031168899
[INFO] [stdout] [Epoch 118]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24986189275282875
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602380893995141
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24993266105865664
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26016732089528055
[INFO] [stdout] [Epoch 119]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498646949871954
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260235286783741
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499299694264683
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601700121756716
[INFO] [stdout] [Epoch 120]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24986727969288328
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602327017535665
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24992748676348897
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601724945393548
[INFO] [stdout] [Epoch 121]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498696637549649
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602303174154236
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24992519684513678
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26017478420307694
[INFO] [stdout] [Epoch 122]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24987186274800383
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260228118187522
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24992308470666028
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601768961249234
[INFO] [stdout] [Epoch 123]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24987389103774543
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602260896979678
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24992113654529283
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26017884410199005
[INFO] [stdout] [Epoch 124]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24987576187492055
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602242186907994
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499193396300086
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260180640860478
[INFO] [stdout] [Epoch 125]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24987748748177258
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26022249293932354
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24991768221829136
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601822981387988
[INFO] [stdout] [Epoch 126]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498790791318721
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26022090116618324
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24991615347936766
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26018382676423346
[INFO] [stdout] [Epoch 127]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24988054722373984
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602194329696369
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499147434234049
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26018523672364413
[INFO] [stdout] [Epoch 128]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24988190134875812
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26021807875556197
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24991344283620753
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26018653722869844
[INFO] [stdout] [Epoch 129]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498831503538125
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602168296747414
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24991224321898778
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601877367760338
[INFO] [stdout] [Epoch 130]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24988430239907364
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26021567756502123
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499111367328128
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26018884320275387
[INFO] [stdout] [Epoch 131]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498853650112958
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26021461489795955
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24991011614736702
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601898637376174
[INFO] [stdout] [Epoch 132]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24988634513297903
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26021363472962084
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990917479369482
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601908050482561
[INFO] [stdout] [Epoch 133]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24988724916771665
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602127306551905
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499083065206121
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601916732847274
[INFO] [stdout] [Epoch 134]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24988808302202398
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260211896767114
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990750565450368
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601924741196882
[INFO] [stdout] [Epoch 135]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24988885214392068
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602111276164877
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990676696224248
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601932127854501
[INFO] [stdout] [Epoch 136]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24988956155851857
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602104181774478
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499060856169888
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26019389410815913
[INFO] [stdout] [Epoch 137]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498902159008486
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020976381432326
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990545716664428
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26019452253932357
[INFO] [stdout] [Epoch 138]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498908194461392
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602091602513414
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990487750475682
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26019510218489317
[INFO] [stdout] [Epoch 139]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498913761377445
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260208603544685
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990434284368412
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26019563683208324
[INFO] [stdout] [Epoch 140]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989188961290607
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602080900567186
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499038496898415
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260196129974115
[INFO] [stdout] [Epoch 141]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989236322651365
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020761643221707
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990339482087048
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601965848330376
[INFO] [stdout] [Epoch 142]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989280007302317
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602071795764392
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499029752645817
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26019700438077764
[INFO] [stdout] [Epoch 143]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989320300667286
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602067766349044
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990258827953196
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26019739135855446
[INFO] [stdout] [Epoch 144]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989357466013
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602064049747388
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499022313371091
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601977482947896
[INFO] [stdout] [Epoch 145]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989391746169048
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260206062167471
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499019021050094
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601980775216251
[INFO] [stdout] [Epoch 146]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989423365114366
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602057459731622
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990159843199558
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601983811901602
[INFO] [stdout] [Epoch 147]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989452529440503
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020545432576986
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990131833384016
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26019866128450536
[INFO] [stdout] [Epoch 148]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989479429701425
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020518531964615
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990105998035925
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26019891963474456
[INFO] [stdout] [Epoch 149]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989504241658425
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020493719708604
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990082168345273
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260199157928893
[INFO] [stdout] [Epoch 150]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498952712742847
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602047083368417
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990060188607438
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26019937772392504
[INFO] [stdout] [Epoch 151]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498954823654337
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020449724352834
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990039915205656
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601995804559466
[INFO] [stdout] [Epoch 152]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498956770692675
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602043025378533
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499002121567264
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601997674495782
[INFO] [stdout] [Epoch 153]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498958566579515
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020412294760276
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990003967824995
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601999399266097
[INFO] [stdout] [Epoch 154]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989602230489288
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602039572993285
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989988058964774
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020009901398256
[INFO] [stdout] [Epoch 155]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989617509240603
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602038045106814
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989973385143133
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020024575115314
[INFO] [stdout] [Epoch 156]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989631601878487
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602036635833378
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989959850481067
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020038109688376
[INFO] [stdout] [Epoch 157]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989644600482483
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602035335964771
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989947366542983
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602005059355075
[INFO] [stdout] [Epoch 158]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498965658998393
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020341370076416
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498993585175875
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602006210827057
[INFO] [stdout] [Epoch 159]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989667648720876
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602033031128005
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989925230890758
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602007272908376
[INFO] [stdout] [Epoch 160]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989677848949882
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260203201110005
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989915434542295
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020082525385596
[INFO] [stdout] [Epoch 161]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989687257318188
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020310702589167
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989906398704073
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602009156118413
[INFO] [stdout] [Epoch 162]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989695935299125
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020302024571645
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989898064336058
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020099895518384
[INFO] [stdout] [Epoch 163]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498970393959377
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020294020245854
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.249898903769816
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020107582844126
[INFO] [stdout] [Epoch 164]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989711322501437
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020286637311696
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989883286411668
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602011467338961
[INFO] [stdout] [Epoch 165]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989718132261346
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602027982752923
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498987674629662
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020121213483854
[INFO] [stdout] [Epoch 166]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989724413367892
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020273546403494
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989870713903498
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602012724585928
[INFO] [stdout] [Epoch 167]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989730206861274
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020267752893794
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989865149816787
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602013280993094
[INFO] [stdout] [Epoch 168]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989735550595712
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020262409145467
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989860017680918
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020137942053995
[INFO] [stdout] [Epoch 169]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989740479486727
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020257480242637
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498985528396267
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602014267576134
[INFO] [stdout] [Epoch 170]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989745025739277
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020252933980015
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498985091773208
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602014704198263
[INFO] [stdout] [Epoch 171]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989749219058238
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020248740652485
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989846890460346
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020151069246467
[INFO] [stdout] [Epoch 172]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989753086842453
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020244872860987
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989843175833412
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020154783866684
[INFO] [stdout] [Epoch 173]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989756654363726
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602024130533349
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989839749580037
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602015821011433
[INFO] [stdout] [Epoch 174]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989759944931994
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020238014759944
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989836589313225
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020161370376255
[INFO] [stdout] [Epoch 175]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989762980047578
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020234979639867
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989833674383924
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020164285301406
[INFO] [stdout] [Epoch 176]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989765779541717
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020232180141906
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989830985746103
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201669739357
[INFO] [stdout] [Epoch 177]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989768361706113
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602022959797423
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989828505832304
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020169453846487
[INFO] [stdout] [Epoch 178]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989770743412454
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602022721626511
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989826218438888
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602017174123733
[INFO] [stdout] [Epoch 179]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989772940222654
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020225019452553
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989824108620115
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602017385105392
[INFO] [stdout] [Epoch 180]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498977496649053
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602022299318265
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498982216259054
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020175797081635
[INFO] [stdout] [Epoch 181]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989776835455568
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020221124215887
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989820367634888
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201775920357
[INFO] [stdout] [Epoch 182]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989778559329479
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020219400340516
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989818712025
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602017924764424
[INFO] [stdout] [Epoch 183]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989780149375942
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602021781029279
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989817184943203
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020180774724866
[INFO] [stdout] [Epoch 184]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989781615984202
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602021634368347
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989815776411628
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020182183255447
[INFO] [stdout] [Epoch 185]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989782968737004
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602021499092976
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498981447722698
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020183482439263
[INFO] [stdout] [Epoch 186]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989784216473154
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020213743192827
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989813278900447
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602018468076505
[INFO] [stdout] [Epoch 187]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989785367345277
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602021259232003
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989812173602255
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602018578606264
[INFO] [stdout] [Epoch 188]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989786428873106
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020211530791626
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989811154110408
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020186805553963
[INFO] [stdout] [Epoch 189]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498978740799259
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020210551671646
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989810213763605
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201877459003
[INFO] [stdout] [Epoch 190]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989788311101244
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020209648562587
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989809346417674
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602018861324584
[INFO] [stdout] [Epoch 191]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989789144099922
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020208815563545
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989808546405426
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020189413257766
[INFO] [stdout] [Epoch 192]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249897899124314
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020208047231747
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498980780849959
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201901511633
[INFO] [stdout] [Epoch 193]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989790621115904
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020733854698
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498980712787876
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019083178388
[INFO] [stdout] [Epoch 194]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989791274783932
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020668487871
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989806500095782
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020191459566644
[INFO] [stdout] [Epoch 195]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989791877706521
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020206081955927
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989805921048777
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020192038613466
[INFO] [stdout] [Epoch 196]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989792433823113
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020552583916
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989805386954247
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019257270784
[INFO] [stdout] [Epoch 197]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989792946767375
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020205012894754
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989804894322457
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019306533947
[INFO] [stdout] [Epoch 198]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979341989082
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020453977115
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989804439934596
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020193519727225
[INFO] [stdout] [Epoch 199]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979385628484
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020410337703
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.249898040208217
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020193938839997
[INFO] [stdout] [Epoch 200]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989794258800777
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020370086098
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989803634245314
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019432541629
[INFO] [stdout] [Epoch 201]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989794630068668
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020332959301
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989803277679576
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019468198194
[INFO] [stdout] [Epoch 202]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979497251435
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020298714724
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989802948794682
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019501086675
[INFO] [stdout] [Epoch 203]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979528837535
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020267128616
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498980264544173
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019531421963
[INFO] [stdout] [Epoch 204]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979557971547
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020237994597
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989802365638644
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020195594022655
[INFO] [stdout] [Epoch 205]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989795848438334
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020211122305
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989802107557174
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019585210408
[INFO] [stdout] [Epoch 206]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989796096299743
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020186336159
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989801869511052
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019609015015
[INFO] [stdout] [Epoch 207]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979632491922
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020163474207
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989801649944876
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019630971627
[INFO] [stdout] [Epoch 208]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989796535790548
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020201423870687
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498980144742404
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020196512237065
[INFO] [stdout] [Epoch 209]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989796730291539
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020122936965
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989801260625263
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020196699035797
[INFO] [stdout] [Epoch 210]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979690969307
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020104996808
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989801088328026
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019687133299
[INFO] [stdout] [Epoch 211]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979707516733
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020088449378
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989800929406536
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020197030254444
[INFO] [stdout] [Epoch 212]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979722779551
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020200731865567
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989800782822424
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019717683853
[INFO] [stdout] [Epoch 213]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979736857489
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020200591086157
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.249898006476179
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019731204302
[INFO] [stdout] [Epoch 214]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989797498425306
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020200461235704
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989800522909544
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020197436751347
[INFO] [stdout] [Epoch 215]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249897976181952
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020200341465777
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989800407882534
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019755177832
[INFO] [stdout] [Epoch 216]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979772866714
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020023099381
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498980030178528
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020197657875543
[INFO] [stdout] [Epoch 217]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989797830562938
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020012909799
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989800203924548
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019775573624
[INFO] [stdout] [Epoch 218]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989797924548382
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260202000351125
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498980011366093
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020197845999843
[INFO] [stdout] [Epoch 219]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798011237557
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201999484233
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498980003040463
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201979292561
[INFO] [stdout] [Epoch 220]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798091196896
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199868463945
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799953611685
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019800604902
[INFO] [stdout] [Epoch 221]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798164948843
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199794711973
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799882780322
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198076880363
[INFO] [stdout] [Epoch 222]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798232975294
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199726685495
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799817447714
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198142212936
[INFO] [stdout] [Epoch 223]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979829572072
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019966394003
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799757187003
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019820247363
[INFO] [stdout] [Epoch 224]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798353595108
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019960606562
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979970160445
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198258056154
[INFO] [stdout] [Epoch 225]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798406976588
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019955268412
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799650336875
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198309323694
[INFO] [stdout] [Epoch 226]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798456213966
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019950344671
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799603049292
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198356611257
[INFO] [stdout] [Epoch 227]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798501628968
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019945803168
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799559432735
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198400227784
[INFO] [stdout] [Epoch 228]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798543518305
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199416142314
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799519202197
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201984404583
[INFO] [stdout] [Epoch 229]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798582155712
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019937750489
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979948209485
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198477565626
[INFO] [stdout] [Epoch 230]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798617793607
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019934186697
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799447868216
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019851179224
[INFO] [stdout] [Epoch 231]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798650664877
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019930899567
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799416298628
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019854336179
[INFO] [stdout] [Epoch 232]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798680984293
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199278676226
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799387179878
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019857248052
[INFO] [stdout] [Epoch 233]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798708949948
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019925071054
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979936032166
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019859933872
[INFO] [stdout] [Epoch 234]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798734744587
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019922491588
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799335548493
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198624111857
[INFO] [stdout] [Epoch 235]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798758536738
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199201123717
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799312698505
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019864696182
[INFO] [stdout] [Epoch 236]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979878048186
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019917917856
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979929162241
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198668037886
[INFO] [stdout] [Epoch 237]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798800723342
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019915893705
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.249897992721825
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198687477775
[INFO] [stdout] [Epoch 238]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798819393438
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019914026693
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799254251738
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019870540851
[INFO] [stdout] [Epoch 239]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979883661414
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019912304621
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799237712979
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019872194724
[INFO] [stdout] [Epoch 240]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798852497963
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019910716235
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799222458142
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019873720205
[INFO] [stdout] [Epoch 241]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798867148708
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201990925116
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979920838758
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198751272594
[INFO] [stdout] [Epoch 242]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798880662076
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019907899821
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799195409337
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198764250807
[INFO] [stdout] [Epoch 243]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798893126383
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019906653388
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799183438624
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201987762215
[INFO] [stdout] [Epoch 244]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798904623056
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199055037163
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799172397223
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019878726288
[INFO] [stdout] [Epoch 245]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798915227224
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199044432984
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979916221298
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198797447097
[INFO] [stdout] [Epoch 246]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798925008175
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199034652014
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799152819359
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198806840694
[INFO] [stdout] [Epoch 247]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798934029803
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019902563035
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979914415497
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019881550505
[INFO] [stdout] [Epoch 248]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979894235108
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019901730904
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799136163218
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198823496776
[INFO] [stdout] [Epoch 249]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798950026357
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199009633743
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799128791887
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198830868085
[INFO] [stdout] [Epoch 250]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798957105788
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019900255429
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799121992803
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019883766715
[INFO] [stdout] [Epoch 251]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979896363563
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019899602443
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799115721545
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198843938386
[INFO] [stdout] [Epoch 252]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798969658544
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989900015
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979910993714
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019884972276
[INFO] [stdout] [Epoch 253]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798975213892
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198984446113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799104601787
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019885505809
[INFO] [stdout] [Epoch 254]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798980337968
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019897932202
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799099680618
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019885997924
[INFO] [stdout] [Epoch 255]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798985064257
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989745957
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.249897990951415
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019886451833
[INFO] [stdout] [Epoch 256]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798989423623
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019897023631
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979909095476
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198868705047
[INFO] [stdout] [Epoch 257]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798993444576
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198966215335
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799087093048
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198872566735
[INFO] [stdout] [Epoch 258]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798997153362
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019896250652
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799083531125
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019887612863
[INFO] [stdout] [Epoch 259]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979900057423
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019895908563
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799080245723
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019887941401
[INFO] [stdout] [Epoch 260]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979900372954
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019895593029
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799077215366
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198882444345
[INFO] [stdout] [Epoch 261]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799006639896
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198953019913
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799074420263
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198885239415
[INFO] [stdout] [Epoch 262]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249897990093243
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198950335477
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979907184215
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019888781751
[INFO] [stdout] [Epoch 263]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979901180032
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198947859435
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799069464194
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019889019544
[INFO] [stdout] [Epoch 264]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799014084121
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198945575607
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979906727083
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019889238878
[INFO] [stdout] [Epoch 265]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799016190628
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198943469086
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979906524774
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019889441185
[INFO] [stdout] [Epoch 266]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799018133604
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019894152608
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799063381712
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019889627786
[INFO] [stdout] [Epoch 267]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979901992574
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198939733924
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799061660542
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198897999
[INFO] [stdout] [Epoch 268]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799021578757
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019893808089
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799060072992
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198899586516
[INFO] [stdout] [Epoch 269]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799023103437
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019893655618
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799058608683
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198901050806
[INFO] [stdout] [Epoch 270]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979902450975
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019893514984
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799057258053
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019890240141
[INFO] [stdout] [Epoch 271]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249897990258069
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019893385266
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979905601229
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198903647157
[INFO] [stdout] [Epoch 272]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799027003343
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989326562
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799054863224
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198904796193
[INFO] [stdout] [Epoch 273]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799028106896
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019893155261
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799053803366
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019890585604
[INFO] [stdout] [Epoch 274]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979902912479
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198930534705
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799052825779
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019890683358
[INFO] [stdout] [Epoch 275]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799030063659
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198929595806
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799051924086
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198907735265
[INFO] [stdout] [Epoch 276]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979903092965
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198928729793
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799051092398
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019890856692
[INFO] [stdout] [Epoch 277]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979903172841
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198927931015
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799050325268
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019890933403
[INFO] [stdout] [Epoch 278]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799032465165
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892719424
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904961769
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198910041586
[INFO] [stdout] [Epoch 279]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799033144716
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892651466
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799048965045
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989106942
[INFO] [stdout] [Epoch 280]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799033771512
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892588784
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799048363084
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198911296144
[INFO] [stdout] [Epoch 281]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799034349638
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892530968
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904780784
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198911851355
[INFO] [stdout] [Epoch 282]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799034882904
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198924776405
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.249897990472957
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198912363474
[INFO] [stdout] [Epoch 283]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799035374755
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198924284516
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904682333
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198912835835
[INFO] [stdout] [Epoch 284]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979903582843
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198923830823
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799046387612
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891327152
[INFO] [stdout] [Epoch 285]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799036246887
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892341233
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799045985725
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891367338
[INFO] [stdout] [Epoch 286]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799036632857
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892302635
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799045615044
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891404404
[INFO] [stdout] [Epoch 287]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799036988872
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892267031
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904527313
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891438593
[INFO] [stdout] [Epoch 288]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799037317242
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892234191
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799044957758
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891470127
[INFO] [stdout] [Epoch 289]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799037620125
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198922039006
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799044666883
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198914992126
[INFO] [stdout] [Epoch 290]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979903789948
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198921759635
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799044398586
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198915260395
[INFO] [stdout] [Epoch 291]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799038157154
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198921501925
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799044151123
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891550784
[INFO] [stdout] [Epoch 292]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799038394822
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198921264237
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799043922864
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891573607
[INFO] [stdout] [Epoch 293]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799038614044
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892104499
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904371232
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198915946596
[INFO] [stdout] [Epoch 294]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979903881625
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892084276
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799043518127
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891614077
[INFO] [stdout] [Epoch 295]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799039002755
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198920656235
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799043339006
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891631985
[INFO] [stdout] [Epoch 296]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799039174776
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198920484183
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799043173794
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891648505
[INFO] [stdout] [Epoch 297]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799039333452
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198920325477
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799043021402
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891663741
[INFO] [stdout] [Epoch 298]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799039479815
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198920179105
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799042880845
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891677795
[INFO] [stdout] [Epoch 299]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799039614802
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892004409
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799042751196
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891690757
[INFO] [stdout] [Epoch 300]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799039739316
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198919919557
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799042631627
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891702711
[INFO] [stdout] [Epoch 301]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979903985415
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198919804693
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799042521338
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891713739
[INFO] [stdout] [Epoch 302]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799039960076
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198919698734
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799042419608
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198917239085
[INFO] [stdout] [Epoch 303]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040057778
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891960101
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799042325767
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198917332904
[INFO] [stdout] [Epoch 304]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040147895
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198919510873
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799042239222
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891741943
[INFO] [stdout] [Epoch 305]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040231023
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891942773
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904215939
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891749922
[INFO] [stdout] [Epoch 306]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040307684
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198919351034
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799042085764
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198917572845
[INFO] [stdout] [Epoch 307]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040378408
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891928029
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799042017855
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198917640724
[INFO] [stdout] [Epoch 308]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040443625
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891921505
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041955207
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198917703347
[INFO] [stdout] [Epoch 309]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040503796
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198919154863
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041897431
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891776109
[INFO] [stdout] [Epoch 310]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040559274
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198919099363
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904184414
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891781436
[INFO] [stdout] [Epoch 311]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040610453
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198919048143
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904179499
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891786349
[INFO] [stdout] [Epoch 312]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904065767
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891900091
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041749647
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891790881
[INFO] [stdout] [Epoch 313]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040701222
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918957327
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904170781
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198917950615
[INFO] [stdout] [Epoch 314]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040741398
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891891714
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904166924
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198917989174
[INFO] [stdout] [Epoch 315]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040778438
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918880055
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041633662
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891802473
[INFO] [stdout] [Epoch 316]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904081261
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891884587
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904160084
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918057513
[INFO] [stdout] [Epoch 317]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040844127
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891881434
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904157057
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918087756
[INFO] [stdout] [Epoch 318]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040873203
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891878523
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041542654
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918115667
[INFO] [stdout] [Epoch 319]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040900015
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918758397
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041516914
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891814139
[INFO] [stdout] [Epoch 320]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040924748
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891873365
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904149315
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918165116
[INFO] [stdout] [Epoch 321]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040947558
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891871081
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041471247
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918187
[INFO] [stdout] [Epoch 322]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040968602
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891868974
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041451036
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891820718
[INFO] [stdout] [Epoch 323]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040988017
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891867031
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041432398
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918225806
[INFO] [stdout] [Epoch 324]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041005922
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891865237
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041415192
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918242976
[INFO] [stdout] [Epoch 325]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041022437
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918635834
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041399344
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891825881
[INFO] [stdout] [Epoch 326]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904103766
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891862059
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041384714
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918273424
[INFO] [stdout] [Epoch 327]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904105172
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989186065
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041371216
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891828689
[INFO] [stdout] [Epoch 328]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041064673
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891859352
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041358784
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918299303
[INFO] [stdout] [Epoch 329]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041076627
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891858155
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041347294
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918310755
[INFO] [stdout] [Epoch 330]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041087646
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918570503
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041336716
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891832131
[INFO] [stdout] [Epoch 331]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041097807
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891856031
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904132695
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891833106
[INFO] [stdout] [Epoch 332]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041107197
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891855091
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041317937
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918340043
[INFO] [stdout] [Epoch 333]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041115848
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891854223
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041309632
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918348325
[INFO] [stdout] [Epoch 334]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904112382
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891853424
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041301974
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891835597
[INFO] [stdout] [Epoch 335]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904113119
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918526855
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041294902
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918363013
[INFO] [stdout] [Epoch 336]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041137983
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891852003
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041288366
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918369514
[INFO] [stdout] [Epoch 337]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041144248
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891851374
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904128236
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989183755
[INFO] [stdout] [Epoch 338]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041150015
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891850795
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041276822
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838101
[INFO] [stdout] [Epoch 339]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115534
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918502613
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041271715
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918386095
[INFO] [stdout] [Epoch 340]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041160243
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891849767
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041266997
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918390786
[INFO] [stdout] [Epoch 341]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041164767
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891849312
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041262659
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918395115
[INFO] [stdout] [Epoch 342]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041168947
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891848893
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904125864
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989183991
[INFO] [stdout] [Epoch 343]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041172797
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918485044
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041254943
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840278
[INFO] [stdout] [Epoch 344]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904117636
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891848146
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041251523
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840618
[INFO] [stdout] [Epoch 345]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041179647
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891847815
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041248373
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918409304
[INFO] [stdout] [Epoch 346]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041182673
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184751
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041245464
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918412196
[INFO] [stdout] [Epoch 347]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041185468
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891847228
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041242777
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918414855
[INFO] [stdout] [Epoch 348]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041188043
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891846968
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041240301
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918417303
[INFO] [stdout] [Epoch 349]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904119042
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918467285
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041238026
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918419546
[INFO] [stdout] [Epoch 350]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041192606
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918465076
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904123593
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842163
[INFO] [stdout] [Epoch 351]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041194632
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891846303
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041233987
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842354
[INFO] [stdout] [Epoch 352]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041196492
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918461146
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904123221
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842531
[INFO] [stdout] [Epoch 353]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904119821
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918459403
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041230557
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842694
[INFO] [stdout] [Epoch 354]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041199792
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918457793
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041229036
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918428433
[INFO] [stdout] [Epoch 355]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041201247
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891845631
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041227637
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918429804
[INFO] [stdout] [Epoch 356]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249897990412026
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891845494
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041226338
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918431076
[INFO] [stdout] [Epoch 357]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904120384
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918453685
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904122515
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843224
[INFO] [stdout] [Epoch 358]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041204983
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918452514
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041224053
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843332
[INFO] [stdout] [Epoch 359]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041206037
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891845144
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041223043
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184343
[INFO] [stdout] [Epoch 360]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041207003
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918450443
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041222122
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843521
[INFO] [stdout] [Epoch 361]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041207908
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918449516
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041221256
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843605
[INFO] [stdout] [Epoch 362]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041208735
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918448667
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041220456
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918436827
[INFO] [stdout] [Epoch 363]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249897990412095
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918447873
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041219712
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843754
[INFO] [stdout] [Epoch 364]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041210206
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918447146
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041219035
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918438187
[INFO] [stdout] [Epoch 365]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041210858
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891844648
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041218425
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918438786
[INFO] [stdout] [Epoch 366]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041211452
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918445847
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041217847
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843934
[INFO] [stdout] [Epoch 367]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041212007
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891844528
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041217314
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843985
[INFO] [stdout] [Epoch 368]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041212518
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891844475
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216826
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918440307
[INFO] [stdout] [Epoch 369]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041212984
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891844425
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216382
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918440735
[INFO] [stdout] [Epoch 370]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041213417
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918443793
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918441134
[INFO] [stdout] [Epoch 371]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041213817
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918443377
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121557
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918441495
[INFO] [stdout] [Epoch 372]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041214195
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918442966
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041215227
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844182
[INFO] [stdout] [Epoch 373]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041214528
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918442605
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214905
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918442117
[INFO] [stdout] [Epoch 374]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041214844
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891844228
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214594
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184424
[INFO] [stdout] [Epoch 375]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215133
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891844196
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214328
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844266
[INFO] [stdout] [Epoch 376]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249897990412154
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891844168
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121406
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844289
[INFO] [stdout] [Epoch 377]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121565
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918441406
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041213828
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918443105
[INFO] [stdout] [Epoch 378]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215877
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918441156
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041213617
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844328
[INFO] [stdout] [Epoch 379]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216082
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891844093
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041213417
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918443466
[INFO] [stdout] [Epoch 380]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121627
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184407
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121323
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844364
[INFO] [stdout] [Epoch 381]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216454
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184405
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041213062
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844377
[INFO] [stdout] [Epoch 382]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121661
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891844032
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212907
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844391
[INFO] [stdout] [Epoch 383]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216765
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918440146
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212774
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844402
[INFO] [stdout] [Epoch 384]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216898
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843999
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121263
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844415
[INFO] [stdout] [Epoch 385]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041217037
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843982
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212496
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844424
[INFO] [stdout] [Epoch 386]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041217153
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918439685
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212385
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918444337
[INFO] [stdout] [Epoch 387]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041217264
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918439547
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212285
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918444404
[INFO] [stdout] [Epoch 388]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041217364
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918439436
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212196
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918444487
[INFO] [stdout] [Epoch 389]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121746
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843931
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212096
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918444554
[INFO] [stdout] [Epoch 390]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041217553
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918439197
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212019
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844461
[INFO] [stdout] [Epoch 391]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121762
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843911
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121194
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918444654
[INFO] [stdout] [Epoch 392]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041217692
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918439
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211885
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918444704
[INFO] [stdout] [Epoch 393]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041217753
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918438936
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121182
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918444737
[INFO] [stdout] [Epoch 394]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041217814
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918438847
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211763
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844477
[INFO] [stdout] [Epoch 395]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041217875
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843875
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121172
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918444803
[INFO] [stdout] [Epoch 396]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041217925
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918438686
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211663
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918444826
[INFO] [stdout] [Epoch 397]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041217975
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843861
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121162
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844486
[INFO] [stdout] [Epoch 398]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121802
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918438553
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211574
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844487
[INFO] [stdout] [Epoch 399]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218058
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843848
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121154
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844488
[INFO] [stdout] [Epoch 400]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218097
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918438425
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211497
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918444903
[INFO] [stdout] [Epoch 401]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218136
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843837
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211463
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918444903
[INFO] [stdout] [Epoch 402]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218158
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918438314
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121144
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918444915
[INFO] [stdout] [Epoch 403]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218186
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843826
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211408
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918444915
[INFO] [stdout] [Epoch 404]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218214
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843821
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211386
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918444915
[INFO] [stdout] [Epoch 405]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121824
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918438164
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211375
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918444903
[INFO] [stdout] [Epoch 406]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218258
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843812
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211352
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918444903
[INFO] [stdout] [Epoch 407]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121827
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918438076
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121134
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844489
[INFO] [stdout] [Epoch 408]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218286
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918438053
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121133
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844488
[INFO] [stdout] [Epoch 409]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218302
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843801
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211308
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844488
[INFO] [stdout] [Epoch 410]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218314
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918437976
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211297
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844487
[INFO] [stdout] [Epoch 411]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121833
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918437937
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211286
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844486
[INFO] [stdout] [Epoch 412]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218347
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843789
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211286
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918444837
[INFO] [stdout] [Epoch 413]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218352
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843787
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211264
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918444837
[INFO] [stdout] [Epoch 414]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121837
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918437826
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211253
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918444826
[INFO] [stdout] [Epoch 415]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121838
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843779
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211241
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918444815
[INFO] [stdout] [Epoch 416]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218397
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843776
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121123
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918444803
[INFO] [stdout] [Epoch 417]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218408
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918437715
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211208
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918444803
[INFO] [stdout] [Epoch 418]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218425
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918437676
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211197
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844479
[INFO] [stdout] [Epoch 419]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218436
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843764
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211186
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844478
[INFO] [stdout] [Epoch 420]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218452
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184376
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211186
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844476
[INFO] [stdout] [Epoch 421]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218452
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918437576
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211186
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918444737
[INFO] [stdout] [Epoch 422]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218458
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918437554
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211186
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918444715
[INFO] [stdout] [Epoch 423]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218458
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843753
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211175
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918444687
[INFO] [stdout] [Epoch 424]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218463
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843751
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211175
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918444676
[INFO] [stdout] [Epoch 425]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218463
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918437487
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211175
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918444654
[INFO] [stdout] [Epoch 426]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218463
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918437465
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211175
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844463
[INFO] [stdout] [Epoch 427]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218463
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843744
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211175
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844461
[INFO] [stdout] [Epoch 428]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121847
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918437404
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211175
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918444587
[INFO] [stdout] [Epoch 429]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121847
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843738
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211175
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918444565
[INFO] [stdout] [Epoch 430]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218475
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843735
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211164
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918444554
[INFO] [stdout] [Epoch 431]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121849
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918437315
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211164
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844453
[INFO] [stdout] [Epoch 432]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121849
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918437293
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211164
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844451
[INFO] [stdout] [Epoch 433]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121849
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843727
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211153
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918444487
[INFO] [stdout] [Epoch 434]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121849
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843725
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211153
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918444465
[INFO] [stdout] [Epoch 435]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121849
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918437226
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211153
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844444
[INFO] [stdout] [Epoch 436]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121849
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918437204
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211153
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918444415
[INFO] [stdout] [Epoch 437]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121849
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843717
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211153
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844439
[INFO] [stdout] [Epoch 438]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121849
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843715
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211153
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844437
[INFO] [stdout] [Epoch 439]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218497
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843712
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211153
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844435
[INFO] [stdout] [Epoch 440]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218497
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184371
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211153
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918444326
[INFO] [stdout] [Epoch 441]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218497
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918437076
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211164
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918444304
[INFO] [stdout] [Epoch 442]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218497
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918437054
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211164
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844428
[INFO] [stdout] [Epoch 443]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218502
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843703
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211164
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844426
[INFO] [stdout] [Epoch 444]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218497
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843701
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211164
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844424
[INFO] [stdout] [Epoch 445]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218497
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843699
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211164
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918444215
[INFO] [stdout] [Epoch 446]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218497
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918436965
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211164
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918444193
[INFO] [stdout] [Epoch 447]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218497
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918436943
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211164
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844417
[INFO] [stdout] [Epoch 448]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218497
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843692
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211164
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844415
[INFO] [stdout] [Epoch 449]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218497
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184369
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211164
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844412
[INFO] [stdout] [Epoch 450]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218497
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918436876
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211164
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844409
[INFO] [stdout] [Epoch 451]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121849
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918436854
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211175
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918444065
[INFO] [stdout] [Epoch 452]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121849
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843684
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211175
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918444043
[INFO] [stdout] [Epoch 453]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121849
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918436815
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211175
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844402
[INFO] [stdout] [Epoch 454]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121849
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918436793
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211175
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918444
[INFO] [stdout] [Epoch 455]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121849
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843677
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211175
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918443976
[INFO] [stdout] [Epoch 456]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121849
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843675
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211175
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918443954
[INFO] [stdout] [Epoch 457]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121849
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918436727
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211186
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844392
[INFO] [stdout] [Epoch 458]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218486
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918436716
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211186
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184439
[INFO] [stdout] [Epoch 459]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218486
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918436693
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211186
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918443876
[INFO] [stdout] [Epoch 460]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218486
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843667
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211186
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844385
[INFO] [stdout] [Epoch 461]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121848
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843665
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211197
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918443815
[INFO] [stdout] [Epoch 462]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121848
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918436627
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211197
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918443793
[INFO] [stdout] [Epoch 463]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218475
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918436604
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211197
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844377
[INFO] [stdout] [Epoch 464]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218475
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843658
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211197
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844375
[INFO] [stdout] [Epoch 465]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121847
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918436566
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211208
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918443715
[INFO] [stdout] [Epoch 466]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218475
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918436543
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211197
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918443704
[INFO] [stdout] [Epoch 467]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121848
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843651
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211186
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918443693
[INFO] [stdout] [Epoch 468]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121849
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843649
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211197
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844367
[INFO] [stdout] [Epoch 469]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218486
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918436466
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211197
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844364
[INFO] [stdout] [Epoch 470]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218486
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918436444
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211197
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918443616
[INFO] [stdout] [Epoch 471]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121848
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843642
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211208
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918443593
[INFO] [stdout] [Epoch 472]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218475
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184364
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211208
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918443554
[INFO] [stdout] [Epoch 473]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218475
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843639
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121122
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844353
[INFO] [stdout] [Epoch 474]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121847
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918436377
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121122
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844351
[INFO] [stdout] [Epoch 475]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218463
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918436355
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121122
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918443477
[INFO] [stdout] [Epoch 476]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218463
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843633
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121123
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918443455
[INFO] [stdout] [Epoch 477]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218458
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843631
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121123
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844342
[INFO] [stdout] [Epoch 478]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218458
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843628
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121123
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844341
[INFO] [stdout] [Epoch 479]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218463
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843626
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121122
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184434
[INFO] [stdout] [Epoch 480]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121847
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843624
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211208
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918443377
[INFO] [stdout] [Epoch 481]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121848
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918436205
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121122
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918443355
[INFO] [stdout] [Epoch 482]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218475
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918436194
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121122
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844332
[INFO] [stdout] [Epoch 483]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121847
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843617
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121123
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918443294
[INFO] [stdout] [Epoch 484]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218463
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843615
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121123
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844326
[INFO] [stdout] [Epoch 485]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218458
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918436127
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211241
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844324
[INFO] [stdout] [Epoch 486]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218452
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918436116
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211241
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918443205
[INFO] [stdout] [Epoch 487]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218447
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918436105
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211253
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844318
[INFO] [stdout] [Epoch 488]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121844
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843608
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211264
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844315
[INFO] [stdout] [Epoch 489]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121844
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843606
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211253
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844314
[INFO] [stdout] [Epoch 490]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218447
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843604
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211253
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918443116
[INFO] [stdout] [Epoch 491]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218452
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918436016
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211241
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918443105
[INFO] [stdout] [Epoch 492]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218458
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918435977
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211241
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844308
[INFO] [stdout] [Epoch 493]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218463
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918435944
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121123
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844307
[INFO] [stdout] [Epoch 494]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121847
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843592
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211241
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844304
[INFO] [stdout] [Epoch 495]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218463
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843591
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211253
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844301
[INFO] [stdout] [Epoch 496]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218458
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184359
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211253
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918442977
[INFO] [stdout] [Epoch 497]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218447
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843588
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211264
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918442955
[INFO] [stdout] [Epoch 498]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121844
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918435855
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211264
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844292
[INFO] [stdout] [Epoch 499]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218436
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918435855
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211275
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844289
[INFO] [stdout] [Epoch 500]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121843
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918435833
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211286
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918442866
[INFO] [stdout] [Epoch 501]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121843
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843581
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211275
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918442844
[INFO] [stdout] [Epoch 502]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121843
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843579
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211275
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918442833
[INFO] [stdout] [Epoch 503]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218436
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918435766
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211275
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844281
[INFO] [stdout] [Epoch 504]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121844
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918435733
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211275
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844279
[INFO] [stdout] [Epoch 505]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121844
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918435705
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211264
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844278
[INFO] [stdout] [Epoch 506]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218447
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843567
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211264
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918442755
[INFO] [stdout] [Epoch 507]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218452
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843565
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211275
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918442716
[INFO] [stdout] [Epoch 508]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121844
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843564
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211275
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918442694
[INFO] [stdout] [Epoch 509]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218436
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843563
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211286
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844266
[INFO] [stdout] [Epoch 510]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218425
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918435605
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211297
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844263
[INFO] [stdout] [Epoch 511]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121842
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918435605
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211308
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918442594
[INFO] [stdout] [Epoch 512]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218408
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918435583
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121132
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844257
[INFO] [stdout] [Epoch 513]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218408
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843556
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211308
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844255
[INFO] [stdout] [Epoch 514]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218408
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843554
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211308
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844253
[INFO] [stdout] [Epoch 515]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218413
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918435516
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211308
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918442516
[INFO] [stdout] [Epoch 516]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218413
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918435494
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211308
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918442494
[INFO] [stdout] [Epoch 517]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121842
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843547
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211308
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844247
[INFO] [stdout] [Epoch 518]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121842
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918435444
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211297
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918442444
[INFO] [stdout] [Epoch 519]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218425
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843542
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211297
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844242
[INFO] [stdout] [Epoch 520]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218425
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184354
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211297
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844241
[INFO] [stdout] [Epoch 521]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218425
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843538
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211297
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844239
[INFO] [stdout] [Epoch 522]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121843
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918435355
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211308
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918442355
[INFO] [stdout] [Epoch 523]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121842
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918435333
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121132
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844232
[INFO] [stdout] [Epoch 524]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218408
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843532
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121133
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844229
[INFO] [stdout] [Epoch 525]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218402
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843531
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121133
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918442267
[INFO] [stdout] [Epoch 526]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218408
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843529
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121133
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918442256
[INFO] [stdout] [Epoch 527]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218408
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918435267
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121133
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918442233
[INFO] [stdout] [Epoch 528]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218408
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918435244
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121132
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844221
[INFO] [stdout] [Epoch 529]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218408
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843522
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121132
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844219
[INFO] [stdout] [Epoch 530]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218408
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184352
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121132
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844216
[INFO] [stdout] [Epoch 531]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218413
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843518
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121132
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844214
[INFO] [stdout] [Epoch 532]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218413
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843515
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121132
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918442117
[INFO] [stdout] [Epoch 533]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218413
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843513
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121132
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918442095
[INFO] [stdout] [Epoch 534]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218413
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918435106
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121132
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844207
[INFO] [stdout] [Epoch 535]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218413
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918435083
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121132
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844205
[INFO] [stdout] [Epoch 536]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218413
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843506
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121133
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844203
[INFO] [stdout] [Epoch 537]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218402
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843505
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121134
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918441995
[INFO] [stdout] [Epoch 538]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218397
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843504
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211352
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844197
[INFO] [stdout] [Epoch 539]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218397
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918435017
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211352
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844195
[INFO] [stdout] [Epoch 540]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218397
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918434995
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211352
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844193
[INFO] [stdout] [Epoch 541]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218397
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843497
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211352
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918441906
[INFO] [stdout] [Epoch 542]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218397
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843495
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211352
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844188
[INFO] [stdout] [Epoch 543]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218397
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843493
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211352
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918441856
[INFO] [stdout] [Epoch 544]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121839
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918434906
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211352
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918441834
[INFO] [stdout] [Epoch 545]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121839
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843488
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211352
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844181
[INFO] [stdout] [Epoch 546]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121839
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918434856
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211352
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844179
[INFO] [stdout] [Epoch 547]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121839
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918434834
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211352
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918441756
[INFO] [stdout] [Epoch 548]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121839
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843482
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211364
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918441734
[INFO] [stdout] [Epoch 549]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121839
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843479
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211364
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844171
[INFO] [stdout] [Epoch 550]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121839
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918434767
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211364
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844169
[INFO] [stdout] [Epoch 551]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121839
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918434756
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211364
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918441667
[INFO] [stdout] [Epoch 552]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121839
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918434734
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211364
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918441645
[INFO] [stdout] [Epoch 553]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218386
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843472
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211375
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844162
[INFO] [stdout] [Epoch 554]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218386
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184347
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211375
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918441595
[INFO] [stdout] [Epoch 555]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218386
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843468
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211375
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844157
[INFO] [stdout] [Epoch 556]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121838
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918434656
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211375
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844155
[INFO] [stdout] [Epoch 557]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121838
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918434634
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211375
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918441517
[INFO] [stdout] [Epoch 558]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218375
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843462
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211386
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918441495
[INFO] [stdout] [Epoch 559]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218375
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918434595
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211386
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918441473
[INFO] [stdout] [Epoch 560]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218375
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918434584
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211386
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844145
[INFO] [stdout] [Epoch 561]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218375
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843456
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211397
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844143
[INFO] [stdout] [Epoch 562]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218375
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843454
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211397
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918441406
[INFO] [stdout] [Epoch 563]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121837
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843452
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211397
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918441373
[INFO] [stdout] [Epoch 564]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121837
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918434495
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211397
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844135
[INFO] [stdout] [Epoch 565]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218363
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918434473
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211408
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918441323
[INFO] [stdout] [Epoch 566]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218358
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843446
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211408
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184413
[INFO] [stdout] [Epoch 567]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218358
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843445
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211408
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844127
[INFO] [stdout] [Epoch 568]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218352
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843443
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121142
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918441245
[INFO] [stdout] [Epoch 569]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218352
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918434406
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121142
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918441223
[INFO] [stdout] [Epoch 570]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218347
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918434395
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121143
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184412
[INFO] [stdout] [Epoch 571]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121834
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918434373
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121143
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844117
[INFO] [stdout] [Epoch 572]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218347
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843435
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121143
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918441145
[INFO] [stdout] [Epoch 573]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121834
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843434
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121144
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918441123
[INFO] [stdout] [Epoch 574]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218336
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843431
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121144
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844109
[INFO] [stdout] [Epoch 575]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121833
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843429
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211452
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844107
[INFO] [stdout] [Epoch 576]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121833
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843428
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211452
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844104
[INFO] [stdout] [Epoch 577]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218325
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843427
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211452
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844102
[INFO] [stdout] [Epoch 578]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121832
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918434245
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211463
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918440984
[INFO] [stdout] [Epoch 579]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218314
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918434223
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211463
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844096
[INFO] [stdout] [Epoch 580]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218308
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843421
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211475
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844093
[INFO] [stdout] [Epoch 581]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218302
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184342
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211475
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918440907
[INFO] [stdout] [Epoch 582]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218297
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843418
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211486
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918440884
[INFO] [stdout] [Epoch 583]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218302
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918434156
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211486
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844085
[INFO] [stdout] [Epoch 584]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218297
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918434145
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211497
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844083
[INFO] [stdout] [Epoch 585]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121829
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918434123
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211486
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844082
[INFO] [stdout] [Epoch 586]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218297
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184341
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211497
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918440796
[INFO] [stdout] [Epoch 587]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218302
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843407
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211486
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844077
[INFO] [stdout] [Epoch 588]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218297
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843407
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211497
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918440735
[INFO] [stdout] [Epoch 589]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121829
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843404
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211497
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918440723
[INFO] [stdout] [Epoch 590]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218297
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843402
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211497
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184407
[INFO] [stdout] [Epoch 591]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218297
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918433995
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211497
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844069
[INFO] [stdout] [Epoch 592]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218302
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843396
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211486
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918440657
[INFO] [stdout] [Epoch 593]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218297
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843395
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211497
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918440635
[INFO] [stdout] [Epoch 594]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121829
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843393
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211508
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844061
[INFO] [stdout] [Epoch 595]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218297
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918433907
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211497
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184406
[INFO] [stdout] [Epoch 596]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218308
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918433884
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211497
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844058
[INFO] [stdout] [Epoch 597]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218314
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843385
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211486
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918440557
[INFO] [stdout] [Epoch 598]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218302
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843384
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211497
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918440524
[INFO] [stdout] [Epoch 599]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218297
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843382
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211497
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844051
[INFO] [stdout] [Epoch 600]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218302
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918433796
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211497
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918440485
[INFO] [stdout] [Epoch 601]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218308
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918433773
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211497
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918440474
[INFO] [stdout] [Epoch 602]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218308
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918433746
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211497
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844045
[INFO] [stdout] [Epoch 603]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218314
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918433723
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211497
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844044
[INFO] [stdout] [Epoch 604]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121832
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184337
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211486
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918440407
[INFO] [stdout] [Epoch 605]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218308
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843368
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211497
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918440374
[INFO] [stdout] [Epoch 606]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218302
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843367
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211508
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844036
[INFO] [stdout] [Epoch 607]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218308
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918433646
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211497
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844034
[INFO] [stdout] [Epoch 608]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218308
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843361
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211497
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844033
[INFO] [stdout] [Epoch 609]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218314
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843359
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211497
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918440307
[INFO] [stdout] [Epoch 610]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121832
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843357
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211497
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918440285
[INFO] [stdout] [Epoch 611]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218325
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918433546
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211497
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918440274
[INFO] [stdout] [Epoch 612]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218325
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918433524
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211486
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844024
[INFO] [stdout] [Epoch 613]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121832
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184335
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211497
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184402
[INFO] [stdout] [Epoch 614]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218308
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184335
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211508
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844019
[INFO] [stdout] [Epoch 615]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218314
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918433474
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211508
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844017
[INFO] [stdout] [Epoch 616]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218314
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843345
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211508
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918440146
[INFO] [stdout] [Epoch 617]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218314
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843342
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211497
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918440135
[INFO] [stdout] [Epoch 618]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121832
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918433396
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211497
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918440113
[INFO] [stdout] [Epoch 619]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121832
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918433374
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211497
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844009
[INFO] [stdout] [Epoch 620]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218325
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843335
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211497
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844008
[INFO] [stdout] [Epoch 621]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218325
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843333
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211497
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844006
[INFO] [stdout] [Epoch 622]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218325
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918433307
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211497
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918440035
[INFO] [stdout] [Epoch 623]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121833
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918433274
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211497
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918440013
[INFO] [stdout] [Epoch 624]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218336
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843325
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211497
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844
[INFO] [stdout] [Epoch 625]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218336
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843323
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211497
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843998
[INFO] [stdout] [Epoch 626]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218336
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184332
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211486
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918439946
[INFO] [stdout] [Epoch 627]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218325
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184332
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211497
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843991
[INFO] [stdout] [Epoch 628]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121832
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843318
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211508
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918439885
[INFO] [stdout] [Epoch 629]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121832
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843317
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211508
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918439874
[INFO] [stdout] [Epoch 630]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121832
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918433146
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211508
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843985
[INFO] [stdout] [Epoch 631]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121832
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918433124
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211508
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843983
[INFO] [stdout] [Epoch 632]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121832
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184331
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211508
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843981
[INFO] [stdout] [Epoch 633]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121832
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843308
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211508
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918439785
[INFO] [stdout] [Epoch 634]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121832
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843306
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211508
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918439763
[INFO] [stdout] [Epoch 635]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121832
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918433046
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211508
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843975
[INFO] [stdout] [Epoch 636]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218325
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918433013
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211508
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843973
[INFO] [stdout] [Epoch 637]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218325
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843299
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211508
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843971
[INFO] [stdout] [Epoch 638]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218325
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843298
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211508
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918439685
[INFO] [stdout] [Epoch 639]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218325
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843296
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121152
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918439663
[INFO] [stdout] [Epoch 640]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218325
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918432935
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121152
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918439635
[INFO] [stdout] [Epoch 641]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218325
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843291
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121152
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918439613
[INFO] [stdout] [Epoch 642]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218325
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918432885
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121152
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843959
[INFO] [stdout] [Epoch 643]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218325
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918432863
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121152
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843957
[INFO] [stdout] [Epoch 644]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218325
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843284
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121152
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918439547
[INFO] [stdout] [Epoch 645]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218325
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843282
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121152
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918439524
[INFO] [stdout] [Epoch 646]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218325
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843281
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121152
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184395
[INFO] [stdout] [Epoch 647]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121832
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918432785
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121152
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843948
[INFO] [stdout] [Epoch 648]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121832
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918432774
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121152
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843946
[INFO] [stdout] [Epoch 649]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218325
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843274
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121153
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918439436
[INFO] [stdout] [Epoch 650]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218325
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843273
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121153
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918439413
[INFO] [stdout] [Epoch 651]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218325
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843271
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121153
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843939
[INFO] [stdout] [Epoch 652]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121832
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918432685
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121153
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918439363
[INFO] [stdout] [Epoch 653]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121832
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918432663
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121153
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843934
[INFO] [stdout] [Epoch 654]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121832
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843265
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121154
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843932
[INFO] [stdout] [Epoch 655]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218314
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918432624
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121154
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918439297
[INFO] [stdout] [Epoch 656]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218314
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918432613
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121154
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918439275
[INFO] [stdout] [Epoch 657]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218314
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843259
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121154
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843925
[INFO] [stdout] [Epoch 658]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218308
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843257
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121154
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843923
[INFO] [stdout] [Epoch 659]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218308
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918432547
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211552
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843921
[INFO] [stdout] [Epoch 660]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218302
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918432536
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211552
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918439186
[INFO] [stdout] [Epoch 661]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218308
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918432513
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211552
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918439164
[INFO] [stdout] [Epoch 662]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218302
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184325
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211563
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843914
[INFO] [stdout] [Epoch 663]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218302
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843248
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211563
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843912
[INFO] [stdout] [Epoch 664]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218297
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843246
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211563
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918439097
[INFO] [stdout] [Epoch 665]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218297
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918432436
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211563
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843906
[INFO] [stdout] [Epoch 666]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121829
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918432413
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211563
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918439047
[INFO] [stdout] [Epoch 667]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218302
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843239
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211563
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918439025
[INFO] [stdout] [Epoch 668]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218297
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843238
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211563
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918439
[INFO] [stdout] [Epoch 669]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218297
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843237
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211574
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843898
[INFO] [stdout] [Epoch 670]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121829
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843234
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211563
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843897
[INFO] [stdout] [Epoch 671]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218297
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843232
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211574
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918438936
[INFO] [stdout] [Epoch 672]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218297
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918432297
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211574
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918438914
[INFO] [stdout] [Epoch 673]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121829
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918432275
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211563
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184389
[INFO] [stdout] [Epoch 674]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218302
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843225
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211563
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843889
[INFO] [stdout] [Epoch 675]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218308
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843222
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211563
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843887
[INFO] [stdout] [Epoch 676]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218308
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843221
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211574
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918438836
[INFO] [stdout] [Epoch 677]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218302
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918432186
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211574
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843881
[INFO] [stdout] [Epoch 678]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218297
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918432164
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211563
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918438797
[INFO] [stdout] [Epoch 679]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218302
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843214
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211574
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918438775
[INFO] [stdout] [Epoch 680]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218302
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843213
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211574
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843874
[INFO] [stdout] [Epoch 681]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218297
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843211
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211574
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843873
[INFO] [stdout] [Epoch 682]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218302
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843208
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211574
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843871
[INFO] [stdout] [Epoch 683]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218297
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843207
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211574
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184387
[INFO] [stdout] [Epoch 684]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218302
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918432047
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211574
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918438664
[INFO] [stdout] [Epoch 685]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218297
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918432025
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211574
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918438653
[INFO] [stdout] [Epoch 686]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218302
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918432
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211574
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843863
[INFO] [stdout] [Epoch 687]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218297
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843198
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211574
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843861
[INFO] [stdout] [Epoch 688]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218308
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843196
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211574
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918438586
[INFO] [stdout] [Epoch 689]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218302
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918431936
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211586
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918438564
[INFO] [stdout] [Epoch 690]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218297
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918431925
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211586
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918438525
[INFO] [stdout] [Epoch 691]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121829
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184319
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211586
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918438514
[INFO] [stdout] [Epoch 692]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218297
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843189
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211597
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843849
[INFO] [stdout] [Epoch 693]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121829
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843187
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211586
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843847
[INFO] [stdout] [Epoch 694]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218297
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918431847
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211597
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843845
[INFO] [stdout] [Epoch 695]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121829
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918431825
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211586
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918438425
[INFO] [stdout] [Epoch 696]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218297
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918431797
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211597
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918438403
[INFO] [stdout] [Epoch 697]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121829
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918431775
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211597
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843839
[INFO] [stdout] [Epoch 698]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121829
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918431753
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211586
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843837
[INFO] [stdout] [Epoch 699]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218297
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843173
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211597
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843835
[INFO] [stdout] [Epoch 700]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121829
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843172
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211586
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918438325
[INFO] [stdout] [Epoch 701]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218302
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184317
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211586
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918438314
[INFO] [stdout] [Epoch 702]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218308
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918431664
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211597
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843828
[INFO] [stdout] [Epoch 703]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218297
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918431653
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211586
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843827
[INFO] [stdout] [Epoch 704]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218302
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843164
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211597
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843823
[INFO] [stdout] [Epoch 705]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218297
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843162
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211597
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843822
[INFO] [stdout] [Epoch 706]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218297
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184316
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211597
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184382
[INFO] [stdout] [Epoch 707]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218302
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918431575
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211597
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918438175
[INFO] [stdout] [Epoch 708]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218297
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918431553
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211597
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918438153
[INFO] [stdout] [Epoch 709]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218297
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843153
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211597
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843814
[INFO] [stdout] [Epoch 710]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218302
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918431503
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211597
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843811
[INFO] [stdout] [Epoch 711]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218297
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843149
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211597
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184381
[INFO] [stdout] [Epoch 712]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218297
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843147
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211597
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918438076
[INFO] [stdout] [Epoch 713]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218302
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843146
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211608
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918438053
[INFO] [stdout] [Epoch 714]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218297
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918431436
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211597
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843803
[INFO] [stdout] [Epoch 715]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218302
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918431414
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211597
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843802
[INFO] [stdout] [Epoch 716]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218302
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843139
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211608
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918437987
[INFO] [stdout] [Epoch 717]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218297
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843138
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121162
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843795
[INFO] [stdout] [Epoch 718]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218286
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843137
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121162
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918437937
[INFO] [stdout] [Epoch 719]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121829
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843135
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121162
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918437915
[INFO] [stdout] [Epoch 720]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121829
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918431325
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211608
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918437903
[INFO] [stdout] [Epoch 721]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121829
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918431303
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211608
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843788
[INFO] [stdout] [Epoch 722]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218297
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843128
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121162
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843785
[INFO] [stdout] [Epoch 723]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218286
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843126
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121162
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918437837
[INFO] [stdout] [Epoch 724]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218286
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843123
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121162
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918437815
[INFO] [stdout] [Epoch 725]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121829
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843121
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121162
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843779
[INFO] [stdout] [Epoch 726]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121829
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918431187
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121162
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843778
[INFO] [stdout] [Epoch 727]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121829
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918431164
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211608
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843776
[INFO] [stdout] [Epoch 728]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218302
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843114
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211608
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918437737
[INFO] [stdout] [Epoch 729]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218302
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211608
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918437726
[INFO] [stdout] [Epoch 730]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218302
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843111
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121162
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918437687
[INFO] [stdout] [Epoch 731]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121829
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184311
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121162
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918437665
[INFO] [stdout] [Epoch 732]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121829
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918431076
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121162
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843764
[INFO] [stdout] [Epoch 733]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218297
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918431053
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121162
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843763
[INFO] [stdout] [Epoch 734]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218297
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843103
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121162
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843761
[INFO] [stdout] [Epoch 735]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218297
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843101
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121162
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918437587
[INFO] [stdout] [Epoch 736]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218297
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918430987
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121162
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918437565
[INFO] [stdout] [Epoch 737]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218297
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843096
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121162
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918437554
[INFO] [stdout] [Epoch 738]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218297
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918430937
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121162
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843753
[INFO] [stdout] [Epoch 739]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218297
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918430926
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121162
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843751
[INFO] [stdout] [Epoch 740]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218297
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918430903
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121162
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918437487
[INFO] [stdout] [Epoch 741]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218302
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843089
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121162
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918437476
[INFO] [stdout] [Epoch 742]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218302
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843087
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121162
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918437454
[INFO] [stdout] [Epoch 743]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218302
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843085
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121162
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843743
[INFO] [stdout] [Epoch 744]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218302
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918430826
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121163
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843739
[INFO] [stdout] [Epoch 745]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121829
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918430815
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121163
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843737
[INFO] [stdout] [Epoch 746]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121829
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843079
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121163
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843735
[INFO] [stdout] [Epoch 747]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121829
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843078
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121164
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843734
[INFO] [stdout] [Epoch 748]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121829
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843076
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121164
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918437315
[INFO] [stdout] [Epoch 749]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121829
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918430737
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121164
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918437293
[INFO] [stdout] [Epoch 750]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121829
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918430715
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121164
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843727
[INFO] [stdout] [Epoch 751]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218286
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843069
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121164
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843725
[INFO] [stdout] [Epoch 752]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218286
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918430676
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121164
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918437226
[INFO] [stdout] [Epoch 753]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218286
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918430665
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121164
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918437204
[INFO] [stdout] [Epoch 754]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121829
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843063
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121164
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843718
[INFO] [stdout] [Epoch 755]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121829
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843062
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211652
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843716
[INFO] [stdout] [Epoch 756]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218286
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184306
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211652
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843715
[INFO] [stdout] [Epoch 757]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218286
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918430576
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211652
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843712
[INFO] [stdout] [Epoch 758]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218286
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918430554
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211652
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184371
[INFO] [stdout] [Epoch 759]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121828
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843054
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211652
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918437076
[INFO] [stdout] [Epoch 760]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121828
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843053
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211663
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918437054
[INFO] [stdout] [Epoch 761]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121828
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843051
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211663
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843703
[INFO] [stdout] [Epoch 762]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218275
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918430487
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211663
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843701
[INFO] [stdout] [Epoch 763]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218275
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918430465
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211663
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843699
[INFO] [stdout] [Epoch 764]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121827
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918430454
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211674
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918436965
[INFO] [stdout] [Epoch 765]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121827
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843044
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211674
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918436943
[INFO] [stdout] [Epoch 766]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121827
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843042
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211674
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843692
[INFO] [stdout] [Epoch 767]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121827
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918430393
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211674
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184369
[INFO] [stdout] [Epoch 768]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121827
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843037
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211685
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918436876
[INFO] [stdout] [Epoch 769]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218264
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843036
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211685
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918436854
[INFO] [stdout] [Epoch 770]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218264
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843035
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211685
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918436827
[INFO] [stdout] [Epoch 771]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218258
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918430326
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211697
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918436804
[INFO] [stdout] [Epoch 772]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218258
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918430304
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211697
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843677
[INFO] [stdout] [Epoch 773]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218252
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918430293
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211697
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843675
[INFO] [stdout] [Epoch 774]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218247
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843028
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211708
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918436727
[INFO] [stdout] [Epoch 775]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218247
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843026
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211708
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918436704
[INFO] [stdout] [Epoch 776]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121824
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843025
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211708
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843668
[INFO] [stdout] [Epoch 777]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218236
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843024
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121172
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843666
[INFO] [stdout] [Epoch 778]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218236
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918430215
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121172
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843664
[INFO] [stdout] [Epoch 779]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218236
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918430193
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121172
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918436616
[INFO] [stdout] [Epoch 780]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121823
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843018
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121173
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918436593
[INFO] [stdout] [Epoch 781]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121823
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843017
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121173
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918436566
[INFO] [stdout] [Epoch 782]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218225
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843015
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121174
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843653
[INFO] [stdout] [Epoch 783]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121822
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918430126
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121174
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843651
[INFO] [stdout] [Epoch 784]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218214
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918430126
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121174
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843649
[INFO] [stdout] [Epoch 785]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218214
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184301
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211752
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918436466
[INFO] [stdout] [Epoch 786]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218208
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918430076
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211752
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918436444
[INFO] [stdout] [Epoch 787]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218203
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918430076
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211763
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843641
[INFO] [stdout] [Epoch 788]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218197
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918430054
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211763
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843639
[INFO] [stdout] [Epoch 789]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218191
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918430043
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211774
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918436366
[INFO] [stdout] [Epoch 790]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218186
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843003
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211774
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918436344
[INFO] [stdout] [Epoch 791]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218186
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843001
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211774
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843633
[INFO] [stdout] [Epoch 792]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218191
[INFO] [stderr] error: test failed, to rerun pass `--lib`
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842999
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211774
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184363
[INFO] [stdout] [Epoch 793]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218186
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918429965
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211785
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843627
[INFO] [stdout] [Epoch 794]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121818
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918429954
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211785
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843625
[INFO] [stdout] [Epoch 795]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121818
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918429943
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211797
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918436227
[INFO] [stdout] [Epoch 796]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218175
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842992
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211797
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918436194
[INFO] [stdout] [Epoch 797]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218164
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842992
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211808
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843617
[INFO] [stdout] [Epoch 798]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218158
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184299
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211808
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843615
[INFO] [stdout] [Epoch 799]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218153
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842989
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121182
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918436116
[INFO] [stdout] [Epoch 800]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218147
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918429877
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121183
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918436094
[INFO] [stdout] [Epoch 801]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218141
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918429854
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121183
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843607
[INFO] [stdout] [Epoch 802]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218141
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842984
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121183
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843606
[INFO] [stdout] [Epoch 803]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218147
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918429804
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121182
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843604
[INFO] [stdout] [Epoch 804]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218153
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842978
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121182
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918436027
[INFO] [stdout] [Epoch 805]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218158
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842976
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121182
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918436016
[INFO] [stdout] [Epoch 806]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218158
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842974
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211808
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843599
[INFO] [stdout] [Epoch 807]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218164
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918429716
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211808
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918435977
[INFO] [stdout] [Epoch 808]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121817
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918429693
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121182
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918435944
[INFO] [stdout] [Epoch 809]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218164
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842968
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121182
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843592
[INFO] [stdout] [Epoch 810]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218158
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842967
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121183
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184359
[INFO] [stdout] [Epoch 811]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218147
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842965
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121184
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918435866
[INFO] [stdout] [Epoch 812]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218141
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842965
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121184
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918435844
[INFO] [stdout] [Epoch 813]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218136
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918429627
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211852
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843581
[INFO] [stdout] [Epoch 814]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218125
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918429627
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211863
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843579
[INFO] [stdout] [Epoch 815]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218125
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918429605
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211852
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843578
[INFO] [stdout] [Epoch 816]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121813
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842958
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211852
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918435755
[INFO] [stdout] [Epoch 817]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121813
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918429555
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211852
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918435744
[INFO] [stdout] [Epoch 818]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218136
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842953
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211852
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918435716
[INFO] [stdout] [Epoch 819]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218136
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842951
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121184
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918435705
[INFO] [stdout] [Epoch 820]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218141
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842949
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121184
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918435683
[INFO] [stdout] [Epoch 821]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218141
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918429466
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121184
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843567
[INFO] [stdout] [Epoch 822]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218147
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918429444
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121184
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843565
[INFO] [stdout] [Epoch 823]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218153
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842942
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121184
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843564
[INFO] [stdout] [Epoch 824]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218153
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184294
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121184
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918435605
[INFO] [stdout] [Epoch 825]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218141
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842939
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211852
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918435583
[INFO] [stdout] [Epoch 826]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218136
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918429377
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211863
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843555
[INFO] [stdout] [Epoch 827]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218125
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918429377
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211874
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843553
[INFO] [stdout] [Epoch 828]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121812
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918429355
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211885
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918435494
[INFO] [stdout] [Epoch 829]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218114
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918429344
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211874
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918435483
[INFO] [stdout] [Epoch 830]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121812
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842932
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211874
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843546
[INFO] [stdout] [Epoch 831]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121812
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184293
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211874
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918435444
[INFO] [stdout] [Epoch 832]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121812
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842929
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211874
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843542
[INFO] [stdout] [Epoch 833]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218125
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842926
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211874
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184354
[INFO] [stdout] [Epoch 834]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218125
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842924
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211874
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843539
[INFO] [stdout] [Epoch 835]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218125
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918429216
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211874
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918435367
[INFO] [stdout] [Epoch 836]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218125
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918429194
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211874
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918435355
[INFO] [stdout] [Epoch 837]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121813
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842917
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211874
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918435333
[INFO] [stdout] [Epoch 838]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121813
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842915
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211863
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843531
[INFO] [stdout] [Epoch 839]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121813
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918429127
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211874
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843529
[INFO] [stdout] [Epoch 840]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121812
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918429127
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211885
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918435256
[INFO] [stdout] [Epoch 841]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218108
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918429116
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211896
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843522
[INFO] [stdout] [Epoch 842]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218108
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918429105
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211896
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843521
[INFO] [stdout] [Epoch 843]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218108
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842908
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211896
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843519
[INFO] [stdout] [Epoch 844]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218108
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842906
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211896
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843516
[INFO] [stdout] [Epoch 845]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218108
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842904
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211896
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843515
[INFO] [stdout] [Epoch 846]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218108
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918429016
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211896
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843513
[INFO] [stdout] [Epoch 847]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218108
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918429005
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211896
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918435106
[INFO] [stdout] [Epoch 848]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218108
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842898
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211896
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918435095
[INFO] [stdout] [Epoch 849]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218108
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918428966
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211896
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843507
[INFO] [stdout] [Epoch 850]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218108
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918428944
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211896
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843505
[INFO] [stdout] [Epoch 851]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218108
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842892
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211896
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843504
[INFO] [stdout] [Epoch 852]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218108
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184289
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211896
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918435017
[INFO] [stdout] [Epoch 853]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218108
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842888
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211896
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918434995
[INFO] [stdout] [Epoch 854]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218108
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918428866
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211908
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843497
[INFO] [stdout] [Epoch 855]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218108
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918428855
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211908
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843496
[INFO] [stdout] [Epoch 856]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218108
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918428833
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121192
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843493
[INFO] [stdout] [Epoch 857]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218103
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842881
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121192
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918434906
[INFO] [stdout] [Epoch 858]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218103
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184288
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121192
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843488
[INFO] [stdout] [Epoch 859]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218103
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842879
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121192
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918434856
[INFO] [stdout] [Epoch 860]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218097
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918428766
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121192
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918434845
[INFO] [stdout] [Epoch 861]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218097
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918428744
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121192
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843482
[INFO] [stdout] [Epoch 862]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218097
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918428716
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121192
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184348
[INFO] [stdout] [Epoch 863]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218097
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918428705
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121193
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843478
[INFO] [stdout] [Epoch 864]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218097
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918428694
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121193
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918434756
[INFO] [stdout] [Epoch 865]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218091
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842867
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121193
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918434734
[INFO] [stdout] [Epoch 866]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218091
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842865
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121193
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843472
[INFO] [stdout] [Epoch 867]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218091
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842864
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121193
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184347
[INFO] [stdout] [Epoch 868]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218086
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842863
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121193
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843468
[INFO] [stdout] [Epoch 869]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218086
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918428605
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121194
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918434656
[INFO] [stdout] [Epoch 870]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218091
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918428583
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121194
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918434634
[INFO] [stdout] [Epoch 871]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218086
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842856
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121194
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843461
[INFO] [stdout] [Epoch 872]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218086
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842855
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121194
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918434584
[INFO] [stdout] [Epoch 873]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218086
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842854
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211952
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843456
[INFO] [stdout] [Epoch 874]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121808
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918428517
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211952
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843454
[INFO] [stdout] [Epoch 875]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121808
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918428494
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211952
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843453
[INFO] [stdout] [Epoch 876]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218075
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918428483
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211952
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918434506
[INFO] [stdout] [Epoch 877]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218075
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842847
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211963
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918434484
[INFO] [stdout] [Epoch 878]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121807
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842845
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211963
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843446
[INFO] [stdout] [Epoch 879]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121807
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842842
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211963
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843444
[INFO] [stdout] [Epoch 880]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218064
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842841
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211963
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843442
[INFO] [stdout] [Epoch 881]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218064
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184284
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211974
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918434395
[INFO] [stdout] [Epoch 882]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218058
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842838
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211974
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918434373
[INFO] [stdout] [Epoch 883]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218064
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918428356
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211974
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843435
[INFO] [stdout] [Epoch 884]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218058
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918428344
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211985
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918434323
[INFO] [stdout] [Epoch 885]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218058
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918428333
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211985
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184343
[INFO] [stdout] [Epoch 886]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218053
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842831
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211985
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843428
[INFO] [stdout] [Epoch 887]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218047
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184283
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211996
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918434256
[INFO] [stdout] [Epoch 888]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218047
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842829
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211996
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918434234
[INFO] [stdout] [Epoch 889]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218042
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918428267
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212007
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843421
[INFO] [stdout] [Epoch 890]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218036
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918428256
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212007
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843419
[INFO] [stdout] [Epoch 891]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218036
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918428245
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212007
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843417
[INFO] [stdout] [Epoch 892]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121803
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842822
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212019
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918434145
[INFO] [stdout] [Epoch 893]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218025
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842821
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212019
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843411
[INFO] [stdout] [Epoch 894]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121802
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184282
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121203
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843409
[INFO] [stdout] [Epoch 895]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218025
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842818
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121203
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843407
[INFO] [stdout] [Epoch 896]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121802
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842815
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212019
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918434057
[INFO] [stdout] [Epoch 897]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218025
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842813
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121203
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843403
[INFO] [stdout] [Epoch 898]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121802
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918428117
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212019
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843402
[INFO] [stdout] [Epoch 899]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218025
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918428095
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121203
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918433995
[INFO] [stdout] [Epoch 900]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121802
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918428084
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212019
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918433984
[INFO] [stdout] [Epoch 901]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218025
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842806
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121203
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843396
[INFO] [stdout] [Epoch 902]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218025
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842804
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121203
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843393
[INFO] [stdout] [Epoch 903]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121802
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842803
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121203
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843392
[INFO] [stdout] [Epoch 904]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218025
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918428006
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121203
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918433896
[INFO] [stdout] [Epoch 905]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121802
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918427995
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121204
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918433873
[INFO] [stdout] [Epoch 906]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218014
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842797
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121204
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843385
[INFO] [stdout] [Epoch 907]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218008
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842796
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212052
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843384
[INFO] [stdout] [Epoch 908]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218014
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842794
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121204
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918433807
[INFO] [stdout] [Epoch 909]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218014
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842793
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121204
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918433796
[INFO] [stdout] [Epoch 910]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121802
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918427906
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212052
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918433785
[INFO] [stdout] [Epoch 911]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218025
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918427884
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121204
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918433757
[INFO] [stdout] [Epoch 912]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218014
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918427856
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121204
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918433735
[INFO] [stdout] [Epoch 913]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121802
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918427834
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121204
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843371
[INFO] [stdout] [Epoch 914]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218014
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842782
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121204
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184337
[INFO] [stdout] [Epoch 915]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121802
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842781
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212052
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843368
[INFO] [stdout] [Epoch 916]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218014
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842779
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121204
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918433657
[INFO] [stdout] [Epoch 917]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121802
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918427767
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212052
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918433646
[INFO] [stdout] [Epoch 918]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218025
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918427745
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121204
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918433624
[INFO] [stdout] [Epoch 919]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218014
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842772
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212052
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184336
[INFO] [stdout] [Epoch 920]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218008
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842772
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212063
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843358
[INFO] [stdout] [Epoch 921]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218014
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184277
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212052
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843357
[INFO] [stdout] [Epoch 922]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121802
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842768
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212052
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918433546
[INFO] [stdout] [Epoch 923]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218008
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918427656
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212063
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918433524
[INFO] [stdout] [Epoch 924]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121802
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918427634
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121204
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843351
[INFO] [stdout] [Epoch 925]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218025
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842761
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212052
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843349
[INFO] [stdout] [Epoch 926]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218014
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918427595
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212052
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918433474
[INFO] [stdout] [Epoch 927]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121803
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842756
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212052
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843345
[INFO] [stdout] [Epoch 928]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218025
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842756
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212052
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843343
[INFO] [stdout] [Epoch 929]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218014
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842754
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212052
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843342
[INFO] [stdout] [Epoch 930]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121803
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842752
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212052
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918433396
[INFO] [stdout] [Epoch 931]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218025
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918427506
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212052
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918433374
[INFO] [stdout] [Epoch 932]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218014
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918427495
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212052
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843336
[INFO] [stdout] [Epoch 933]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121803
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842746
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212052
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843334
[INFO] [stdout] [Epoch 934]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121802
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842745
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212052
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843333
[INFO] [stdout] [Epoch 935]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218036
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842742
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212052
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918433307
[INFO] [stdout] [Epoch 936]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218025
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918427406
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212052
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918433285
[INFO] [stdout] [Epoch 937]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121802
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918427395
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212063
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843326
[INFO] [stdout] [Epoch 938]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121802
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918427373
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212063
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843325
[INFO] [stdout] [Epoch 939]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218025
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842736
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212063
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843323
[INFO] [stdout] [Epoch 940]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121803
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842734
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212063
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843322
[INFO] [stdout] [Epoch 941]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121803
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842731
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212052
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843318
[INFO] [stdout] [Epoch 942]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218025
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842729
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212063
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843317
[INFO] [stdout] [Epoch 943]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218025
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842728
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212063
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918433146
[INFO] [stdout] [Epoch 944]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121803
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918427256
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212063
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918433135
[INFO] [stdout] [Epoch 945]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121803
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918427234
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212063
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918433113
[INFO] [stdout] [Epoch 946]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121803
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918427223
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212063
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184331
[INFO] [stdout] [Epoch 947]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218036
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184272
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212063
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843308
[INFO] [stdout] [Epoch 948]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218036
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842718
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212063
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843307
[INFO] [stdout] [Epoch 949]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218036
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918427156
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212052
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918433046
[INFO] [stdout] [Epoch 950]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218036
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918427134
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212052
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918433024
[INFO] [stdout] [Epoch 951]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121803
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918427134
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212063
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918433
[INFO] [stdout] [Epoch 952]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121803
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842711
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212063
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843299
[INFO] [stdout] [Epoch 953]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121803
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842709
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212063
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843297
[INFO] [stdout] [Epoch 954]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121803
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842707
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212063
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843296
[INFO] [stdout] [Epoch 955]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218036
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918427045
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212063
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918432935
[INFO] [stdout] [Epoch 956]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218042
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842702
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212063
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843291
[INFO] [stdout] [Epoch 957]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218042
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918427007
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212063
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918432896
[INFO] [stdout] [Epoch 958]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218042
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918426984
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212063
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918432874
[INFO] [stdout] [Epoch 959]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218042
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918426973
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212063
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918432863
[INFO] [stdout] [Epoch 960]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218042
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842695
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212063
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843284
[INFO] [stdout] [Epoch 961]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218042
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842693
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212063
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843282
[INFO] [stdout] [Epoch 962]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218042
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918426907
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212063
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843281
[INFO] [stdout] [Epoch 963]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218042
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918426896
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212063
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918432785
[INFO] [stdout] [Epoch 964]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218042
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918426884
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212063
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918432774
[INFO] [stdout] [Epoch 965]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218042
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842686
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212063
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843275
[INFO] [stdout] [Epoch 966]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218042
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842684
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212063
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843273
[INFO] [stdout] [Epoch 967]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218042
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842682
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212063
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843272
[INFO] [stdout] [Epoch 968]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218042
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918426807
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212063
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918432696
[INFO] [stdout] [Epoch 969]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218042
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918426796
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212063
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918432674
[INFO] [stdout] [Epoch 970]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218047
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918426773
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212063
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918432663
[INFO] [stdout] [Epoch 971]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218047
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918426746
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212063
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918432635
[INFO] [stdout] [Epoch 972]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218047
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918426724
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212074
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918432613
[INFO] [stdout] [Epoch 973]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218047
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184267
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212074
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843259
[INFO] [stdout] [Epoch 974]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218042
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842669
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212074
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843258
[INFO] [stdout] [Epoch 975]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218042
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842668
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212074
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843256
[INFO] [stdout] [Epoch 976]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218042
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918426657
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212074
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918432536
[INFO] [stdout] [Epoch 977]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218042
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918426635
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212074
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918432513
[INFO] [stdout] [Epoch 978]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218042
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918426624
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212074
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184325
[INFO] [stdout] [Epoch 979]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218036
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842661
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212085
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843248
[INFO] [stdout] [Epoch 980]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218036
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842659
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212085
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843246
[INFO] [stdout] [Epoch 981]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218036
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842657
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212085
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918432436
[INFO] [stdout] [Epoch 982]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218036
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918426557
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212085
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918432424
[INFO] [stdout] [Epoch 983]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121803
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918426546
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212085
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184324
[INFO] [stdout] [Epoch 984]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121803
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918426524
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212085
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843238
[INFO] [stdout] [Epoch 985]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218036
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184265
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212096
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843235
[INFO] [stdout] [Epoch 986]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121803
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842649
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212096
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843233
[INFO] [stdout] [Epoch 987]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121803
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918426474
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212096
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843232
[INFO] [stdout] [Epoch 988]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218025
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842645
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212096
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918432297
[INFO] [stdout] [Epoch 989]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218025
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842643
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212107
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918432275
[INFO] [stdout] [Epoch 990]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218025
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842643
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212107
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843225
[INFO] [stdout] [Epoch 991]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121802
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918426407
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212096
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843224
[INFO] [stdout] [Epoch 992]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121803
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918426385
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212096
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843222
[INFO] [stdout] [Epoch 993]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218025
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842636
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212107
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918432197
[INFO] [stdout] [Epoch 994]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218025
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842635
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212107
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918432186
[INFO] [stdout] [Epoch 995]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121802
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842633
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212096
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918432175
[INFO] [stdout] [Epoch 996]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121803
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918426307
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212107
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843215
[INFO] [stdout] [Epoch 997]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218025
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918426296
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212107
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843213
[INFO] [stdout] [Epoch 998]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218025
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918426274
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212107
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843211
[INFO] [stdout] [Epoch 999]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218025
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842626
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212118
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891843208
[INFO] [stdout] thread 'models::sequential::test_sequential_xor1' panicked at src/models/sequential.rs:242:5:
[INFO] [stdout] assertion `left == right` failed
[INFO] [stdout]   left: [0.0, 0.0, 0.0, 0.0]
[INFO] [stdout]  right: [0.0, 1.0, 1.0, 0.0]
[INFO] [stdout] stack backtrace:
[INFO] [stdout]    0:     0x561223e6a1dc - std::backtrace_rs::backtrace::libunwind::trace::ha637c64ce894333a
[INFO] [stdout]                                at /rustc/82e1608dfa6e0b5569232559e3d385fea5a93112/library/std/src/../../backtrace/src/backtrace/libunwind.rs:104:5
[INFO] [stdout]    1:     0x561223e6a1dc - std::backtrace_rs::backtrace::trace_unsynchronized::h47f62dea28e0c88d
[INFO] [stdout]                                at /rustc/82e1608dfa6e0b5569232559e3d385fea5a93112/library/std/src/../../backtrace/src/backtrace/mod.rs:66:5
[INFO] [stdout]    2:     0x561223e6a1dc - std::sys_common::backtrace::_print_fmt::h9eef0abe20ede486
[INFO] [stdout]                                at /rustc/82e1608dfa6e0b5569232559e3d385fea5a93112/library/std/src/sys_common/backtrace.rs:67:5
[INFO] [stdout]    3:     0x561223e6a1dc - <std::sys_common::backtrace::_print::DisplayBacktrace as core::fmt::Display>::fmt::hed7f999df88cc644
[INFO] [stdout]                                at /rustc/82e1608dfa6e0b5569232559e3d385fea5a93112/library/std/src/sys_common/backtrace.rs:44:22
[INFO] [stdout]    4:     0x561223e94320 - core::fmt::rt::Argument::fmt::h1539a9308b8d058d
[INFO] [stdout]                                at /rustc/82e1608dfa6e0b5569232559e3d385fea5a93112/library/core/src/fmt/rt.rs:142:9
[INFO] [stdout]    5:     0x561223e94320 - core::fmt::write::h3a39390d8560d9c9
[INFO] [stdout]                                at /rustc/82e1608dfa6e0b5569232559e3d385fea5a93112/library/core/src/fmt/mod.rs:1120:17
[INFO] [stdout]    6:     0x561223e67b3f - std::io::Write::write_fmt::h040b369f3a0f9cbc
[INFO] [stdout]                                at /rustc/82e1608dfa6e0b5569232559e3d385fea5a93112/library/std/src/io/mod.rs:1762:15
[INFO] [stdout]    7:     0x561223e69fc4 - std::sys_common::backtrace::_print::h894006fb5c6f3d45
[INFO] [stdout]                                at /rustc/82e1608dfa6e0b5569232559e3d385fea5a93112/library/std/src/sys_common/backtrace.rs:47:5
[INFO] [stdout]    8:     0x561223e69fc4 - std::sys_common::backtrace::print::h23a2d212c6fff936
[INFO] [stdout]                                at /rustc/82e1608dfa6e0b5569232559e3d385fea5a93112/library/std/src/sys_common/backtrace.rs:34:9
[INFO] [stdout]    9:     0x561223e6bc87 - std::panicking::default_hook::{{closure}}::h8a1d2ee00185001a
[INFO] [stdout]   10:     0x561223e6b973 - std::panicking::default_hook::h6038f2eba384e475
[INFO] [stdout]                                at /rustc/82e1608dfa6e0b5569232559e3d385fea5a93112/library/std/src/panicking.rs:289:9
[INFO] [stdout]   11:     0x561223dd09a7 - <alloc::boxed::Box<F,A> as core::ops::function::Fn<Args>>::call::h0ad024c9bdc6d768
[INFO] [stdout]                                at /rustc/82e1608dfa6e0b5569232559e3d385fea5a93112/library/alloc/src/boxed.rs:2021:9
[INFO] [stdout]   12:     0x561223dd09a7 - test::test_main::{{closure}}::h7500b54257359db6
[INFO] [stdout]                                at /rustc/82e1608dfa6e0b5569232559e3d385fea5a93112/library/test/src/lib.rs:138:21
[INFO] [stdout]   13:     0x561223e6c298 - <alloc::boxed::Box<F,A> as core::ops::function::Fn<Args>>::call::h1f8f335eaa9cfaee
[INFO] [stdout]                                at /rustc/82e1608dfa6e0b5569232559e3d385fea5a93112/library/alloc/src/boxed.rs:2021:9
[INFO] [stdout]   14:     0x561223e6c298 - std::panicking::rust_panic_with_hook::h2b5517d590cab22e
[INFO] [stdout]                                at /rustc/82e1608dfa6e0b5569232559e3d385fea5a93112/library/std/src/panicking.rs:783:13
[INFO] [stdout]   15:     0x561223e6bfee - std::panicking::begin_panic_handler::{{closure}}::h233112c06e0ef43e
[INFO] [stdout]                                at /rustc/82e1608dfa6e0b5569232559e3d385fea5a93112/library/std/src/panicking.rs:657:13
[INFO] [stdout]   16:     0x561223e6a6a6 - std::sys_common::backtrace::__rust_end_short_backtrace::h6e893f24d7ebbff8
[INFO] [stdout]                                at /rustc/82e1608dfa6e0b5569232559e3d385fea5a93112/library/std/src/sys_common/backtrace.rs:170:18
[INFO] [stdout]   17:     0x561223e6bd52 - rust_begin_unwind
[INFO] [stdout]                                at /rustc/82e1608dfa6e0b5569232559e3d385fea5a93112/library/std/src/panicking.rs:645:5
[INFO] [stdout]   18:     0x561223d5e155 - core::panicking::panic_fmt::hbf0e066aabfa482c
[INFO] [stdout]                                at /rustc/82e1608dfa6e0b5569232559e3d385fea5a93112/library/core/src/panicking.rs:72:14
[INFO] [stdout]   19:     0x561223d5e56b - core::panicking::assert_failed_inner::h0bba8b7c26ee4492
[INFO] [stdout]   20:     0x561223d7d67e - core::panicking::assert_failed::h506f70a13171e31a
[INFO] [stdout]                                at /rustc/82e1608dfa6e0b5569232559e3d385fea5a93112/library/core/src/panicking.rs:279:5
[INFO] [stdout]   21:     0x561223d92456 - easynn::models::sequential::test_sequential_xor1::h4cd49a714a840de7
[INFO] [stdout]                                at /opt/rustwide/workdir/src/models/sequential.rs:242:5
[INFO] [stdout]   22:     0x561223d91057 - easynn::models::sequential::test_sequential_xor1::{{closure}}::h7fd1c41a3bbe4933
[INFO] [stdout]                                at /opt/rustwide/workdir/src/models/sequential.rs:205:26
[INFO] [stdout]   23:     0x561223d71986 - core::ops::function::FnOnce::call_once::h2e99d9403eadaa47
[INFO] [stdout]                                at /rustc/82e1608dfa6e0b5569232559e3d385fea5a93112/library/core/src/ops/function.rs:250:5
[INFO] [stdout]   24:     0x561223dd5fff - core::ops::function::FnOnce::call_once::h2b5d431d5046312d
[INFO] [stdout]                                at /rustc/82e1608dfa6e0b5569232559e3d385fea5a93112/library/core/src/ops/function.rs:250:5
[INFO] [stdout]   25:     0x561223dd5fff - test::__rust_begin_short_backtrace::h7714669af176e207
[INFO] [stdout]                                at /rustc/82e1608dfa6e0b5569232559e3d385fea5a93112/library/test/src/lib.rs:628:18
[INFO] [stdout]   26:     0x561223dd5015 - test::run_test_in_process::{{closure}}::h85218797dcbcec12
[INFO] [stdout]                                at /rustc/82e1608dfa6e0b5569232559e3d385fea5a93112/library/test/src/lib.rs:651:60
[INFO] [stdout]   27:     0x561223dd5015 - <core::panic::unwind_safe::AssertUnwindSafe<F> as core::ops::function::FnOnce<()>>::call_once::h76c106ea31c14d7b
[INFO] [stdout]                                at /rustc/82e1608dfa6e0b5569232559e3d385fea5a93112/library/core/src/panic/unwind_safe.rs:272:9
[INFO] [stdout]   28:     0x561223dd5015 - std::panicking::try::do_call::h3bd5d0f6620ee946
[INFO] [stdout]                                at /rustc/82e1608dfa6e0b5569232559e3d385fea5a93112/library/std/src/panicking.rs:552:40
[INFO] [stdout]   29:     0x561223dd5015 - std::panicking::try::h232e535e4744fe31
[INFO] [stdout]                                at /rustc/82e1608dfa6e0b5569232559e3d385fea5a93112/library/std/src/panicking.rs:516:19
[INFO] [stdout]   30:     0x561223dd5015 - std::panic::catch_unwind::h3ce3bd6ca8dbac68
[INFO] [stdout]                                at /rustc/82e1608dfa6e0b5569232559e3d385fea5a93112/library/std/src/panic.rs:142:14
[INFO] [stdout]   31:     0x561223dd5015 - test::run_test_in_process::hc97b257c9621286a
[INFO] [stdout]                                at /rustc/82e1608dfa6e0b5569232559e3d385fea5a93112/library/test/src/lib.rs:651:27
[INFO] [stdout]   32:     0x561223dd5015 - test::run_test::{{closure}}::h2aa798823a438d38
[INFO] [stdout]                                at /rustc/82e1608dfa6e0b5569232559e3d385fea5a93112/library/test/src/lib.rs:574:43
[INFO] [stdout]   33:     0x561223d9d2d6 - test::run_test::{{closure}}::h87d231c78fb59832
[INFO] [stdout]                                at /rustc/82e1608dfa6e0b5569232559e3d385fea5a93112/library/test/src/lib.rs:602:41
[INFO] [stdout]   34:     0x561223d9d2d6 - std::sys_common::backtrace::__rust_begin_short_backtrace::he6df152596625789
[INFO] [stdout]                                at /rustc/82e1608dfa6e0b5569232559e3d385fea5a93112/library/std/src/sys_common/backtrace.rs:154:18
[INFO] [stdout]   35:     0x561223da2387 - std::thread::Builder::spawn_unchecked_::{{closure}}::{{closure}}::ha94be15d1e0acf36
[INFO] [stdout]                                at /rustc/82e1608dfa6e0b5569232559e3d385fea5a93112/library/std/src/thread/mod.rs:529:17
[INFO] [stdout]   36:     0x561223da2387 - <core::panic::unwind_safe::AssertUnwindSafe<F> as core::ops::function::FnOnce<()>>::call_once::h9dc6bd6372f16606
[INFO] [stdout]                                at /rustc/82e1608dfa6e0b5569232559e3d385fea5a93112/library/core/src/panic/unwind_safe.rs:272:9
[INFO] [stdout]   37:     0x561223da2387 - std::panicking::try::do_call::h9057517f80303cd4
[INFO] [stdout]                                at /rustc/82e1608dfa6e0b5569232559e3d385fea5a93112/library/std/src/panicking.rs:552:40
[INFO] [stdout]   38:     0x561223da2387 - std::panicking::try::he3a30b11ee45340c
[INFO] [stdout]                                at /rustc/82e1608dfa6e0b5569232559e3d385fea5a93112/library/std/src/panicking.rs:516:19
[INFO] [stdout]   39:     0x561223da2387 - std::panic::catch_unwind::h410458cfd3528b8b
[INFO] [stdout]                                at /rustc/82e1608dfa6e0b5569232559e3d385fea5a93112/library/std/src/panic.rs:142:14
[INFO] [stdout]   40:     0x561223da2387 - std::thread::Builder::spawn_unchecked_::{{closure}}::h72ac3bdb567b737b
[INFO] [stdout]                                at /rustc/82e1608dfa6e0b5569232559e3d385fea5a93112/library/std/src/thread/mod.rs:528:30
[INFO] [stdout]   41:     0x561223da2387 - core::ops::function::FnOnce::call_once{{vtable.shim}}::h38f34da3654a8344
[INFO] [stdout]                                at /rustc/82e1608dfa6e0b5569232559e3d385fea5a93112/library/core/src/ops/function.rs:250:5
[INFO] [stdout]   42:     0x561223e70e05 - <alloc::boxed::Box<F,A> as core::ops::function::FnOnce<Args>>::call_once::hc7eafaff61e32df9
[INFO] [stdout]                                at /rustc/82e1608dfa6e0b5569232559e3d385fea5a93112/library/alloc/src/boxed.rs:2007:9
[INFO] [stdout]   43:     0x561223e70e05 - <alloc::boxed::Box<F,A> as core::ops::function::FnOnce<Args>>::call_once::h6ba4a5de48dd2304
[INFO] [stdout]                                at /rustc/82e1608dfa6e0b5569232559e3d385fea5a93112/library/alloc/src/boxed.rs:2007:9
[INFO] [stdout]   44:     0x561223e70e05 - std::sys::unix::thread::Thread::new::thread_start::he469335aef763e45
[INFO] [stdout]                                at /rustc/82e1608dfa6e0b5569232559e3d385fea5a93112/library/std/src/sys/unix/thread.rs:108:17
[INFO] [stdout]   45:     0x7f80c3db0ac3 - <unknown>
[INFO] [stdout]   46:     0x7f80c3e41bf4 - __clone
[INFO] [stdout]   47:                0x0 - <unknown>
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] failures:
[INFO] [stdout]     models::sequential::test_sequential_xor1
[INFO] [stdout] 
[INFO] [stdout] test result: FAILED. 6 passed; 1 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.55s
[INFO] [stdout] 
[INFO] running `Command { std: "docker" "inspect" "f559a1b6a4dcdfe64820bd1bc18ea259892b48b7c9ada950f07e16e0ea07cdf9", kill_on_drop: false }`
[INFO] running `Command { std: "docker" "rm" "-f" "f559a1b6a4dcdfe64820bd1bc18ea259892b48b7c9ada950f07e16e0ea07cdf9", kill_on_drop: false }`
[INFO] [stdout] f559a1b6a4dcdfe64820bd1bc18ea259892b48b7c9ada950f07e16e0ea07cdf9
[INFO] testing easynn-0.1.7-beta against beta-2023-12-22 for beta-1.76-1
[INFO] extracting crate easynn 0.1.7-beta into /workspace/builds/worker-5-tc2/source
[INFO] validating manifest of crates.io crate easynn 0.1.7-beta on toolchain beta-2023-12-22
[INFO] running `Command { std: CARGO_HOME="/workspace/cargo-home" RUSTUP_HOME="/workspace/rustup-home" "/workspace/cargo-home/bin/cargo" "+beta-2023-12-22" "metadata" "--manifest-path" "Cargo.toml" "--no-deps", kill_on_drop: false }`
[INFO] started tweaking crates.io crate easynn 0.1.7-beta
[INFO] finished tweaking crates.io crate easynn 0.1.7-beta
[INFO] tweaked toml for crates.io crate easynn 0.1.7-beta written to /workspace/builds/worker-5-tc2/source/Cargo.toml
[INFO] running `Command { std: CARGO_HOME="/workspace/cargo-home" RUSTUP_HOME="/workspace/rustup-home" "/workspace/cargo-home/bin/cargo" "+beta-2023-12-22" "generate-lockfile" "--manifest-path" "Cargo.toml", kill_on_drop: false }`
[INFO] [stderr]     Updating crates.io index
[INFO] running `Command { std: CARGO_HOME="/workspace/cargo-home" RUSTUP_HOME="/workspace/rustup-home" "/workspace/cargo-home/bin/cargo" "+beta-2023-12-22" "fetch" "--manifest-path" "Cargo.toml", kill_on_drop: false }`
[INFO] running `Command { std: "docker" "create" "-v" "/var/lib/crater-agent-workspace/builds/worker-5-tc2/target:/opt/rustwide/target:rw,Z" "-v" "/var/lib/crater-agent-workspace/builds/worker-5-tc2/source:/opt/rustwide/workdir:ro,Z" "-v" "/var/lib/crater-agent-workspace/cargo-home:/opt/rustwide/cargo-home:ro,Z" "-v" "/var/lib/crater-agent-workspace/rustup-home:/opt/rustwide/rustup-home:ro,Z" "-e" "SOURCE_DIR=/opt/rustwide/workdir" "-e" "CARGO_TARGET_DIR=/opt/rustwide/target" "-e" "CARGO_HOME=/opt/rustwide/cargo-home" "-e" "RUSTUP_HOME=/opt/rustwide/rustup-home" "-w" "/opt/rustwide/workdir" "-m" "1610612736" "--user" "0:0" "--network" "none" "ghcr.io/rust-lang/crates-build-env/linux@sha256:2788e3201cd34a07e3172128adcd8b3090168a8e3bcc40d7c032b9dda1df7d1c" "/opt/rustwide/cargo-home/bin/cargo" "+beta-2023-12-22" "metadata" "--no-deps" "--format-version=1", kill_on_drop: false }`
[INFO] [stdout] f1c15ead11e1a430519f98621c221c0726307bace5ab7f3014a94df6b0a9ebdf
[INFO] running `Command { std: "docker" "start" "-a" "f1c15ead11e1a430519f98621c221c0726307bace5ab7f3014a94df6b0a9ebdf", kill_on_drop: false }`
[INFO] running `Command { std: "docker" "inspect" "f1c15ead11e1a430519f98621c221c0726307bace5ab7f3014a94df6b0a9ebdf", kill_on_drop: false }`
[INFO] running `Command { std: "docker" "rm" "-f" "f1c15ead11e1a430519f98621c221c0726307bace5ab7f3014a94df6b0a9ebdf", kill_on_drop: false }`
[INFO] [stdout] f1c15ead11e1a430519f98621c221c0726307bace5ab7f3014a94df6b0a9ebdf
[INFO] running `Command { std: "docker" "create" "-v" "/var/lib/crater-agent-workspace/builds/worker-5-tc2/target:/opt/rustwide/target:rw,Z" "-v" "/var/lib/crater-agent-workspace/builds/worker-5-tc2/source:/opt/rustwide/workdir:ro,Z" "-v" "/var/lib/crater-agent-workspace/cargo-home:/opt/rustwide/cargo-home:ro,Z" "-v" "/var/lib/crater-agent-workspace/rustup-home:/opt/rustwide/rustup-home:ro,Z" "-e" "SOURCE_DIR=/opt/rustwide/workdir" "-e" "CARGO_TARGET_DIR=/opt/rustwide/target" "-e" "CARGO_INCREMENTAL=0" "-e" "RUST_BACKTRACE=full" "-e" "RUSTFLAGS=--cap-lints=warn" "-e" "RUSTDOCFLAGS=--cap-lints=warn" "-e" "CARGO_HOME=/opt/rustwide/cargo-home" "-e" "RUSTUP_HOME=/opt/rustwide/rustup-home" "-w" "/opt/rustwide/workdir" "-m" "1610612736" "--user" "0:0" "--network" "none" "ghcr.io/rust-lang/crates-build-env/linux@sha256:2788e3201cd34a07e3172128adcd8b3090168a8e3bcc40d7c032b9dda1df7d1c" "/opt/rustwide/cargo-home/bin/cargo" "+beta-2023-12-22" "build" "--frozen" "--message-format=json", kill_on_drop: false }`
[INFO] [stdout] b4ba974c94639798742f2ab50677756be6f9cbc3c4f33111ad22e58f8d8083b8
[INFO] running `Command { std: "docker" "start" "-a" "b4ba974c94639798742f2ab50677756be6f9cbc3c4f33111ad22e58f8d8083b8", kill_on_drop: false }`
[INFO] [stderr]    Compiling crossbeam-utils v0.8.18
[INFO] [stderr]    Compiling libc v0.2.151
[INFO] [stderr]    Compiling rayon-core v1.12.0
[INFO] [stderr]    Compiling ppv-lite86 v0.2.17
[INFO] [stderr]    Compiling either v1.9.0
[INFO] [stderr]    Compiling num-traits v0.2.17
[INFO] [stderr]    Compiling itertools v0.10.5
[INFO] [stderr]    Compiling crossbeam-epoch v0.9.17
[INFO] [stderr]    Compiling crossbeam-channel v0.5.10
[INFO] [stderr]    Compiling crossbeam-queue v0.3.10
[INFO] [stderr]    Compiling crossbeam-deque v0.8.4
[INFO] [stderr]    Compiling getrandom v0.2.11
[INFO] [stderr]    Compiling num_cpus v1.16.0
[INFO] [stderr]    Compiling rand_core v0.6.4
[INFO] [stderr]    Compiling crossbeam v0.8.3
[INFO] [stderr]    Compiling rand_chacha v0.3.1
[INFO] [stderr]    Compiling rand v0.8.5
[INFO] [stderr]    Compiling rayon v1.8.0
[INFO] [stderr]    Compiling easynn v0.1.7-beta (/opt/rustwide/workdir)
[INFO] [stdout] warning: unused variable: `olen`
[INFO] [stdout]   --> src/layers/dense.rs:96:13
[INFO] [stdout]    |
[INFO] [stdout] 96 |         let olen = output.flattened.len();
[INFO] [stdout]    |             ^^^^ help: if this is intentional, prefix it with an underscore: `_olen`
[INFO] [stdout]    |
[INFO] [stdout]    = note: `#[warn(unused_variables)]` on by default
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: unused variable: `dlen`
[INFO] [stdout]    --> src/layers/dense.rs:148:13
[INFO] [stdout]     |
[INFO] [stdout] 148 |         let dlen = delta.flattened.len();
[INFO] [stdout]     |             ^^^^ help: if this is intentional, prefix it with an underscore: `_dlen`
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: unused variable: `dlen`
[INFO] [stdout]    --> src/layers/dense.rs:205:13
[INFO] [stdout]     |
[INFO] [stdout] 205 |         let dlen = delta.flattened.len();
[INFO] [stdout]     |             ^^^^ help: if this is intentional, prefix it with an underscore: `_dlen`
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: variable does not need to be mutable
[INFO] [stdout]    --> src/models/sequential.rs:137:17
[INFO] [stdout]     |
[INFO] [stdout] 137 |             let mut cum_dw = &dws.par_chunks_mut(1).reduce_with(|dw1, dw2| {
[INFO] [stdout]     |                 ----^^^^^^
[INFO] [stdout]     |                 |
[INFO] [stdout]     |                 help: remove this `mut`
[INFO] [stdout]     |
[INFO] [stdout]     = note: `#[warn(unused_mut)]` on by default
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: variable does not need to be mutable
[INFO] [stdout]    --> src/models/sequential.rs:146:17
[INFO] [stdout]     |
[INFO] [stdout] 146 |             let mut cum_db = &dbs.par_chunks_mut(1).reduce_with(|db1, db2| {
[INFO] [stdout]     |                 ----^^^^^^
[INFO] [stdout]     |                 |
[INFO] [stdout]     |                 help: remove this `mut`
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: function `determine_thread` is never used
[INFO] [stdout]   --> src/layers/dense.rs:18:4
[INFO] [stdout]    |
[INFO] [stdout] 18 | fn determine_thread(len: usize) -> usize {
[INFO] [stdout]    |    ^^^^^^^^^^^^^^^^
[INFO] [stdout]    |
[INFO] [stdout]    = note: `#[warn(dead_code)]` on by default
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: method `pos2index` is never used
[INFO] [stdout]   --> src/tensor/mod.rs:38:19
[INFO] [stdout]    |
[INFO] [stdout] 26 | impl<T: NumT> Tensor<T> {
[INFO] [stdout]    | ----------------------- method in this implementation
[INFO] [stdout] ...
[INFO] [stdout] 38 |     pub(crate) fn pos2index<const RANK: usize>(&self, mut pos: usize) -> Result<TensorIndex<RANK>> {
[INFO] [stdout]    |                   ^^^^^^^^^
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: 7 warnings emitted
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stderr]     Finished dev [unoptimized + debuginfo] target(s) in 2.52s
[INFO] running `Command { std: "docker" "inspect" "b4ba974c94639798742f2ab50677756be6f9cbc3c4f33111ad22e58f8d8083b8", kill_on_drop: false }`
[INFO] running `Command { std: "docker" "rm" "-f" "b4ba974c94639798742f2ab50677756be6f9cbc3c4f33111ad22e58f8d8083b8", kill_on_drop: false }`
[INFO] [stdout] b4ba974c94639798742f2ab50677756be6f9cbc3c4f33111ad22e58f8d8083b8
[INFO] running `Command { std: "docker" "create" "-v" "/var/lib/crater-agent-workspace/builds/worker-5-tc2/target:/opt/rustwide/target:rw,Z" "-v" "/var/lib/crater-agent-workspace/builds/worker-5-tc2/source:/opt/rustwide/workdir:ro,Z" "-v" "/var/lib/crater-agent-workspace/cargo-home:/opt/rustwide/cargo-home:ro,Z" "-v" "/var/lib/crater-agent-workspace/rustup-home:/opt/rustwide/rustup-home:ro,Z" "-e" "SOURCE_DIR=/opt/rustwide/workdir" "-e" "CARGO_TARGET_DIR=/opt/rustwide/target" "-e" "CARGO_INCREMENTAL=0" "-e" "RUST_BACKTRACE=full" "-e" "RUSTFLAGS=--cap-lints=warn" "-e" "RUSTDOCFLAGS=--cap-lints=warn" "-e" "CARGO_HOME=/opt/rustwide/cargo-home" "-e" "RUSTUP_HOME=/opt/rustwide/rustup-home" "-w" "/opt/rustwide/workdir" "-m" "1610612736" "--user" "0:0" "--network" "none" "ghcr.io/rust-lang/crates-build-env/linux@sha256:2788e3201cd34a07e3172128adcd8b3090168a8e3bcc40d7c032b9dda1df7d1c" "/opt/rustwide/cargo-home/bin/cargo" "+beta-2023-12-22" "test" "--frozen" "--no-run" "--message-format=json", kill_on_drop: false }`
[INFO] [stdout] 657e3a795e0a9afb698ca610e72afb5f69fbd89f599d7668adaf9945d7980eb7
[INFO] running `Command { std: "docker" "start" "-a" "657e3a795e0a9afb698ca610e72afb5f69fbd89f599d7668adaf9945d7980eb7", kill_on_drop: false }`
[INFO] [stdout] warning: unused variable: `olen`
[INFO] [stdout]   --> src/layers/dense.rs:96:13
[INFO] [stdout]    |
[INFO] [stdout] 96 |         let olen = output.flattened.len();
[INFO] [stdout]    |             ^^^^ help: if this is intentional, prefix it with an underscore: `_olen`
[INFO] [stdout]    |
[INFO] [stdout]    = note: `#[warn(unused_variables)]` on by default
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: unused variable: `dlen`
[INFO] [stdout]    --> src/layers/dense.rs:148:13
[INFO] [stdout]     |
[INFO] [stdout] 148 |         let dlen = delta.flattened.len();
[INFO] [stdout]     |             ^^^^ help: if this is intentional, prefix it with an underscore: `_dlen`
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: unused variable: `dlen`
[INFO] [stdout]    --> src/layers/dense.rs:205:13
[INFO] [stdout]     |
[INFO] [stdout] 205 |         let dlen = delta.flattened.len();
[INFO] [stdout]     |             ^^^^ help: if this is intentional, prefix it with an underscore: `_dlen`
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: variable does not need to be mutable
[INFO] [stdout]    --> src/models/sequential.rs:137:17
[INFO] [stdout]     |
[INFO] [stdout] 137 |             let mut cum_dw = &dws.par_chunks_mut(1).reduce_with(|dw1, dw2| {
[INFO] [stdout]     |                 ----^^^^^^
[INFO] [stdout]     |                 |
[INFO] [stdout]     |                 help: remove this `mut`
[INFO] [stdout]     |
[INFO] [stdout]     = note: `#[warn(unused_mut)]` on by default
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: variable does not need to be mutable
[INFO] [stdout]    --> src/models/sequential.rs:146:17
[INFO] [stdout]     |
[INFO] [stdout] 146 |             let mut cum_db = &dbs.par_chunks_mut(1).reduce_with(|db1, db2| {
[INFO] [stdout]     |                 ----^^^^^^
[INFO] [stdout]     |                 |
[INFO] [stdout]     |                 help: remove this `mut`
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: function `determine_thread` is never used
[INFO] [stdout]   --> src/layers/dense.rs:18:4
[INFO] [stdout]    |
[INFO] [stdout] 18 | fn determine_thread(len: usize) -> usize {
[INFO] [stdout]    |    ^^^^^^^^^^^^^^^^
[INFO] [stdout]    |
[INFO] [stdout]    = note: `#[warn(dead_code)]` on by default
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: method `pos2index` is never used
[INFO] [stdout]   --> src/tensor/mod.rs:38:19
[INFO] [stdout]    |
[INFO] [stdout] 26 | impl<T: NumT> Tensor<T> {
[INFO] [stdout]    | ----------------------- method in this implementation
[INFO] [stdout] ...
[INFO] [stdout] 38 |     pub(crate) fn pos2index<const RANK: usize>(&self, mut pos: usize) -> Result<TensorIndex<RANK>> {
[INFO] [stdout]    |                   ^^^^^^^^^
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stderr]    Compiling easynn v0.1.7-beta (/opt/rustwide/workdir)
[INFO] [stdout] warning: 7 warnings emitted
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: unused import: `crate::layers::activation::Activation::*`
[INFO] [stdout]    --> src/models/sequential.rs:180:9
[INFO] [stdout]     |
[INFO] [stdout] 180 |     use crate::layers::activation::Activation::*;
[INFO] [stdout]     |         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[INFO] [stdout]     |
[INFO] [stdout]     = note: `#[warn(unused_imports)]` on by default
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: unused import: `rand::Rng`
[INFO] [stdout]    --> src/models/sequential.rs:207:9
[INFO] [stdout]     |
[INFO] [stdout] 207 |     use rand::Rng;
[INFO] [stdout]     |         ^^^^^^^^^
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: unused variable: `olen`
[INFO] [stdout]   --> src/layers/dense.rs:96:13
[INFO] [stdout]    |
[INFO] [stdout] 96 |         let olen = output.flattened.len();
[INFO] [stdout]    |             ^^^^ help: if this is intentional, prefix it with an underscore: `_olen`
[INFO] [stdout]    |
[INFO] [stdout]    = note: `#[warn(unused_variables)]` on by default
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: unused variable: `dlen`
[INFO] [stdout]    --> src/layers/dense.rs:148:13
[INFO] [stdout]     |
[INFO] [stdout] 148 |         let dlen = delta.flattened.len();
[INFO] [stdout]     |             ^^^^ help: if this is intentional, prefix it with an underscore: `_dlen`
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: unused variable: `dlen`
[INFO] [stdout]    --> src/layers/dense.rs:205:13
[INFO] [stdout]     |
[INFO] [stdout] 205 |         let dlen = delta.flattened.len();
[INFO] [stdout]     |             ^^^^ help: if this is intentional, prefix it with an underscore: `_dlen`
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: variable does not need to be mutable
[INFO] [stdout]    --> src/models/sequential.rs:137:17
[INFO] [stdout]     |
[INFO] [stdout] 137 |             let mut cum_dw = &dws.par_chunks_mut(1).reduce_with(|dw1, dw2| {
[INFO] [stdout]     |                 ----^^^^^^
[INFO] [stdout]     |                 |
[INFO] [stdout]     |                 help: remove this `mut`
[INFO] [stdout]     |
[INFO] [stdout]     = note: `#[warn(unused_mut)]` on by default
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: variable does not need to be mutable
[INFO] [stdout]    --> src/models/sequential.rs:146:17
[INFO] [stdout]     |
[INFO] [stdout] 146 |             let mut cum_db = &dbs.par_chunks_mut(1).reduce_with(|db1, db2| {
[INFO] [stdout]     |                 ----^^^^^^
[INFO] [stdout]     |                 |
[INFO] [stdout]     |                 help: remove this `mut`
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: function `determine_thread` is never used
[INFO] [stdout]   --> src/layers/dense.rs:18:4
[INFO] [stdout]    |
[INFO] [stdout] 18 | fn determine_thread(len: usize) -> usize {
[INFO] [stdout]    |    ^^^^^^^^^^^^^^^^
[INFO] [stdout]    |
[INFO] [stdout]    = note: `#[warn(dead_code)]` on by default
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: method `pos2index` is never used
[INFO] [stdout]   --> src/tensor/mod.rs:38:19
[INFO] [stdout]    |
[INFO] [stdout] 26 | impl<T: NumT> Tensor<T> {
[INFO] [stdout]    | ----------------------- method in this implementation
[INFO] [stdout] ...
[INFO] [stdout] 38 |     pub(crate) fn pos2index<const RANK: usize>(&self, mut pos: usize) -> Result<TensorIndex<RANK>> {
[INFO] [stdout]    |                   ^^^^^^^^^
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: 9 warnings emitted
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stderr]     Finished test [unoptimized + debuginfo] target(s) in 0.74s
[INFO] running `Command { std: "docker" "inspect" "657e3a795e0a9afb698ca610e72afb5f69fbd89f599d7668adaf9945d7980eb7", kill_on_drop: false }`
[INFO] running `Command { std: "docker" "rm" "-f" "657e3a795e0a9afb698ca610e72afb5f69fbd89f599d7668adaf9945d7980eb7", kill_on_drop: false }`
[INFO] [stdout] 657e3a795e0a9afb698ca610e72afb5f69fbd89f599d7668adaf9945d7980eb7
[INFO] running `Command { std: "docker" "create" "-v" "/var/lib/crater-agent-workspace/builds/worker-5-tc2/target:/opt/rustwide/target:rw,Z" "-v" "/var/lib/crater-agent-workspace/builds/worker-5-tc2/source:/opt/rustwide/workdir:ro,Z" "-v" "/var/lib/crater-agent-workspace/cargo-home:/opt/rustwide/cargo-home:ro,Z" "-v" "/var/lib/crater-agent-workspace/rustup-home:/opt/rustwide/rustup-home:ro,Z" "-e" "SOURCE_DIR=/opt/rustwide/workdir" "-e" "CARGO_TARGET_DIR=/opt/rustwide/target" "-e" "CARGO_INCREMENTAL=0" "-e" "RUST_BACKTRACE=full" "-e" "RUSTFLAGS=--cap-lints=warn" "-e" "RUSTDOCFLAGS=--cap-lints=warn" "-e" "CARGO_HOME=/opt/rustwide/cargo-home" "-e" "RUSTUP_HOME=/opt/rustwide/rustup-home" "-w" "/opt/rustwide/workdir" "-m" "1610612736" "--user" "0:0" "--network" "none" "ghcr.io/rust-lang/crates-build-env/linux@sha256:2788e3201cd34a07e3172128adcd8b3090168a8e3bcc40d7c032b9dda1df7d1c" "/opt/rustwide/cargo-home/bin/cargo" "+beta-2023-12-22" "test" "--frozen", kill_on_drop: false }`
[INFO] [stdout] 5b9eb7ea34f225c5a8dadfb97abd0a7e755f53c6f49d4795fa238b1215db8093
[INFO] running `Command { std: "docker" "start" "-a" "5b9eb7ea34f225c5a8dadfb97abd0a7e755f53c6f49d4795fa238b1215db8093", kill_on_drop: false }`
[INFO] [stderr] warning: unused variable: `olen`
[INFO] [stderr]   --> src/layers/dense.rs:96:13
[INFO] [stderr]    |
[INFO] [stderr] 96 |         let olen = output.flattened.len();
[INFO] [stderr]    |             ^^^^ help: if this is intentional, prefix it with an underscore: `_olen`
[INFO] [stderr]    |
[INFO] [stderr]    = note: `#[warn(unused_variables)]` on by default
[INFO] [stderr] 
[INFO] [stderr] warning: unused variable: `dlen`
[INFO] [stderr]    --> src/layers/dense.rs:148:13
[INFO] [stderr]     |
[INFO] [stderr] 148 |         let dlen = delta.flattened.len();
[INFO] [stderr]     |             ^^^^ help: if this is intentional, prefix it with an underscore: `_dlen`
[INFO] [stderr] 
[INFO] [stderr] warning: unused variable: `dlen`
[INFO] [stderr]    --> src/layers/dense.rs:205:13
[INFO] [stderr]     |
[INFO] [stderr] 205 |         let dlen = delta.flattened.len();
[INFO] [stderr]     |             ^^^^ help: if this is intentional, prefix it with an underscore: `_dlen`
[INFO] [stderr] 
[INFO] [stderr] warning: variable does not need to be mutable
[INFO] [stderr]    --> src/models/sequential.rs:137:17
[INFO] [stderr]     |
[INFO] [stderr] 137 |             let mut cum_dw = &dws.par_chunks_mut(1).reduce_with(|dw1, dw2| {
[INFO] [stderr]     |                 ----^^^^^^
[INFO] [stderr]     |                 |
[INFO] [stderr]     |                 help: remove this `mut`
[INFO] [stderr]     |
[INFO] [stderr]     = note: `#[warn(unused_mut)]` on by default
[INFO] [stderr] 
[INFO] [stderr] warning: variable does not need to be mutable
[INFO] [stderr]    --> src/models/sequential.rs:146:17
[INFO] [stderr]     |
[INFO] [stderr] 146 |             let mut cum_db = &dbs.par_chunks_mut(1).reduce_with(|db1, db2| {
[INFO] [stderr]     |                 ----^^^^^^
[INFO] [stderr]     |                 |
[INFO] [stderr]     |                 help: remove this `mut`
[INFO] [stderr] 
[INFO] [stderr] warning: function `determine_thread` is never used
[INFO] [stderr]   --> src/layers/dense.rs:18:4
[INFO] [stderr]    |
[INFO] [stderr] 18 | fn determine_thread(len: usize) -> usize {
[INFO] [stderr]    |    ^^^^^^^^^^^^^^^^
[INFO] [stderr]    |
[INFO] [stderr]    = note: `#[warn(dead_code)]` on by default
[INFO] [stderr] 
[INFO] [stderr] warning: method `pos2index` is never used
[INFO] [stderr]   --> src/tensor/mod.rs:38:19
[INFO] [stderr]    |
[INFO] [stderr] 26 | impl<T: NumT> Tensor<T> {
[INFO] [stderr]    | ----------------------- method in this implementation
[INFO] [stderr] ...
[INFO] [stderr] 38 |     pub(crate) fn pos2index<const RANK: usize>(&self, mut pos: usize) -> Result<TensorIndex<RANK>> {
[INFO] [stderr]    |                   ^^^^^^^^^
[INFO] [stderr] 
[INFO] [stderr] warning: unused import: `crate::layers::activation::Activation::*`
[INFO] [stderr]    --> src/models/sequential.rs:180:9
[INFO] [stderr]     |
[INFO] [stderr] 180 |     use crate::layers::activation::Activation::*;
[INFO] [stderr]     |         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[INFO] [stderr]     |
[INFO] [stderr]     = note: `#[warn(unused_imports)]` on by default
[INFO] [stderr] 
[INFO] [stderr] warning: unused import: `rand::Rng`
[INFO] [stderr]    --> src/models/sequential.rs:207:9
[INFO] [stderr]     |
[INFO] [stderr] 207 |     use rand::Rng;
[INFO] [stderr]     |         ^^^^^^^^^
[INFO] [stderr] 
[INFO] [stderr] warning: `easynn` (lib) generated 7 warnings (run `cargo fix --lib -p easynn` to apply 5 suggestions)
[INFO] [stderr] warning: `easynn` (lib test) generated 9 warnings (7 duplicates) (run `cargo fix --lib -p easynn --tests` to apply 2 suggestions)
[INFO] [stderr]     Finished test [unoptimized + debuginfo] target(s) in 0.01s
[INFO] [stderr]      Running unittests src/lib.rs (/opt/rustwide/target/debug/deps/easynn-688f2372db183e5c)
[INFO] [stdout] 
[INFO] [stdout] running 7 tests
[INFO] [stdout] test layers::dense::test_add_weight_delta_to ... ok
[INFO] [stdout] test layers::dense::test_dense_activate ... ok
[INFO] [stdout] test layers::dense::test_dense_descend ... ok
[INFO] [stdout] test layers::dense::test_dense_forward ... ok
[INFO] [stdout] test models::sequential::test_sequential_predict ... ok
[INFO] [stdout] test layers::dense::test_dense_backpropagate ... ok
[INFO] [stdout] test models::sequential::test_sequential_xor1 ... FAILED
[INFO] [stdout] 
[INFO] [stdout] failures:
[INFO] [stdout] 
[INFO] [stdout] ---- models::sequential::test_sequential_xor1 stdout ----
[INFO] [stdout] [Epoch 0]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.00000000004922613504238022
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.9999862484248131
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.9603867929844415
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.001568683043326216
[INFO] [stdout] [Epoch 1]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.0015065631948063892
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.9253705392285885
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.888725865872741
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.005796061633631771
[INFO] [stdout] [Epoch 2]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.005566537592925642
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.8591120067589695
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.825091171289206
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.012060786654501316
[INFO] [stdout] [Epoch 3]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.011583179502954745
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.8001791315845207
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.7684920379719006
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.019851671261885202
[INFO] [stdout] [Epoch 4]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.01906554507986994
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.7476775914524908
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.7180695588292938
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.028749897205619677
[INFO] [stdout] [Epoch 5]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.02761140127621499
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.7008310862379489
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.6730781752214098
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.03841398165198118
[INFO] [stdout] [Epoch 6]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.03689278797848246
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.6589649236104755
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.6328699126341203
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.0485670844252849
[INFO] [stdout] [Epoch 7]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.04664382788194509
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.6214919612510236
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.5968808795842181
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.058986298867922386
[INFO] [stdout] [Epoch 8]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.056650441432636076
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.5879005611601319
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.5646196989370238
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.06949362335014365
[INFO] [stdout] [Epoch 9]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.06674167586534371
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.5577442624437277
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.5356575896498731
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.07994835621374485
[INFO] [stdout] [Epoch 10]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.07678240130752914
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.530632922263403
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.5096198585407617
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.09024069581684019
[INFO] [stdout] [Epoch 11]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.08666716426232536
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.5062251115167871
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.4861785970997745
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.10028636038855256
[INFO] [stdout] [Epoch 12]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.09631502051698204
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.48422158322576786
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.4650464085291344
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.1100220704774977
[INFO] [stdout] [Epoch 13]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.10566519648638976
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.46435965836876314
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.44597101589651494
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.11940176063032219
[INFO] [stdout] [Epoch 14]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.11467345090914798
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.44640839668984533
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.428730624180124
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.12839340719901554
[INFO] [stdout] [Epoch 15]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.12330902827370732
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.43016443944112553
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.41312992763849077
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.1369763763863427
[INFO] [stdout] [Epoch 16]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.1315521118812033
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.41544842756648187
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.3989966698341157
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.14513921125515997
[INFO] [stdout] [Epoch 17]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.13939169848920302
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.40210191294101083
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.38617867718784255
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.15287778883872521
[INFO] [stdout] [Epoch 18]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.14682382840044744
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.38998469230451815
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.3745412984885808
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.16019378902632297
[INFO] [stdout] [Epoch 19]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.15385011498060544
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.37897250377790803
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.36396519262764776
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.1670934258429804
[INFO] [stdout] [Epoch 20]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.16047652617931288
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.3689550345915475
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.3543444152210879
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.17358639933302702
[INFO] [stdout] [Epoch 21]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.16671237791914395
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.35983419610841205
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.3455847619419033
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.17968503269817543
[INFO] [stdout] [Epoch 22]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.1725695054030234
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.3515226285827085
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.3376023324902344
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.18540356480464487
[INFO] [stdout] [Epoch 23]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.17806158363806812
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.34394240351895095
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.33032228433901684
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.19075757280765981
[INFO] [stdout] [Epoch 24]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.18320357292415576
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.3370238961252857
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.3236777498381546
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.19576350357043512
[INFO] [stdout] [Epoch 25]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.18801126882871763
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.3307048043058607
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.31760889405479126
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.20043829588477965
[INFO] [stdout] [Epoch 26]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.1925009393674072
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.3249292940103337
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.3120620939669783
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.20479907832202776
[INFO] [stdout] [Epoch 27]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.19668903482013383
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.31964725363953195
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.30698922239487053
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.20886292993291283
[INFO] [stdout] [Epoch 28]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.20059195790722184
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.3148136426674109
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.3023470224172548
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.21264669303847938
[INFO] [stdout] [Epoch 29]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.20422588399380237
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.31038792174268504
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2980965600411565
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.21616682906662263
[INFO] [stdout] [Epoch 30]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2076066226352259
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.30633355333150086
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.29420274461906293
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.21943930983746304
[INFO] [stdout] [Epoch 31]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.21074951316753618
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.30261756350017205
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.29063390798506195
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.22247953792553302
[INFO] [stdout] [Epoch 32]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2136693482233141
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.29921015675252505
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.28736143454462815
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2253022907616709
[INFO] [stdout] [Epoch 33]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2163803200471368
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.29608437696240536
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.284359435634203
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.22792168401147062
[INFO] [stdout] [Epoch 34]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.21889598532424057
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2932158084061061
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.28160446239273884
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.23035115050464658
[INFO] [stdout] [Epoch 35]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.22122924494428325
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2905823117255782
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2790752521807647
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.23260343161157798
[INFO] [stdout] [Epoch 36]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.22339233571937683
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2881637903614167
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2767525042626286
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.23469057848726863
[INFO] [stdout] [Epoch 37]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2253968315787871
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.28594198360199297
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.27461868105088216
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.23662396104402236
[INFO] [stdout] [Epoch 38]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.22725365218629054
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.28390028291635505
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2726578317123994
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.23841428288501623
[INFO] [stdout] [Epoch 39]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.22897307728237848
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2820235686861794
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2708554353657422
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2400716007424774
[INFO] [stdout] [Epoch 40]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2305647653526818
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2802980648367413
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2691982614687451
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.24160534722549537
[INFO] [stdout] [Epoch 41]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.23203777547497012
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.27871120919772785
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.26767424531303974
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.24302435590141525
[INFO] [stdout] [Epoch 42]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.23340059140732153
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.27725153770946065
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2662723768157106
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.24433688791784874
[INFO] [stdout] [Epoch 43]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.23466114715590242
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.27590858083537567
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.26498260103384197
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.24555065952520452
[INFO] [stdout] [Epoch 44]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.23582685340760517
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.274672770753075
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.26379572903080284
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.24667286998700644
[INFO] [stdout] [Epoch 45]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.23690462433511816
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.27353535807874857
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.26270335789838195
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.24771022947116
[INFO] [stdout] [Epoch 46]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.23790090438369785
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.27248833703737824
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2616977988902518
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.24866898660313327
[INFO] [stdout] [Epoch 47]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.23882169473324363
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.27152437812743724
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2607720127531463
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.24955495543461736
[INFO] [stdout] [Epoch 48]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.23967257919899976
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.27063676744675896
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25991955145542467
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2503735416410576
[INFO] [stdout] [Epoch 49]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2404587493916639
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26981935194848
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2591345056108791
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2511297678105545
[INFO] [stdout] [Epoch 50]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24118502900484773
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2690664899846499
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2584114569808182
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2518282977267615
[INFO] [stdout] [Epoch 51]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24185589713637204
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26837300657214247
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2577454355114474
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25247345958102413
[INFO] [stdout] [Epoch 52]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24247551058120498
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.267734152882504
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25713188042791996
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25306926807535246
[INFO] [stdout] [Epoch 53]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24304772505915712
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26714556951572593
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2565666049624676
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25361944539893727
[INFO] [stdout] [Epoch 54]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24357611536072726
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.266603253168822
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2560457643429021
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2541274410776921
[INFO] [stdout] [Epoch 55]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24406399441060286
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26610352635452567
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25556582671045297
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2545964507094725
[INFO] [stdout] [Epoch 56]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24451443126096414
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26564300986429507
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2551235466732365
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2550294336078093
[INFO] [stdout] [Epoch 57]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2449302680365263
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2652185977038511
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25471594123434704
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25542912938472406
[INFO] [stdout] [Epoch 58]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24531413586067483
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2648274342593408
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25434026786224023
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25579807350890177
[INFO] [stdout] [Epoch 59]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24566846979753465
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2644668934784504
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2539940044962739
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25613861187956016
[INFO] [stdout] [Epoch 60]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24599552284871468
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26413455987388224
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25367483130244733
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25645291445907425
[INFO] [stdout] [Epoch 61]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24629737904607973
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26382821117695576
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25338061401391987
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2567429880090476
[INFO] [stdout] [Epoch 62]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24657596568347376
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26354580248705595
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2531093887081407
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2570106879752895
[INFO] [stdout] [Epoch 63]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2468330647310523
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26328545177853513
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2528593478876779
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2572577295672319
[INFO] [stdout] [Epoch 64]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2470703234759536
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26304542664074215
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2526288277453422
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2574856980768464
[INFO] [stdout] [Epoch 65]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24728926443258709
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2628241321393303
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25241629650618685
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.257696058481235
[INFO] [stdout] [Epoch 66]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2474912945649619
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2626200996980863
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2522203437496166
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2578901643718579
[INFO] [stdout] [Epoch 67]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24767771386231593
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26243197691038644
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2520396706243101
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25806926625190846
[INFO] [stdout] [Epoch 68]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24784972330791633
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2622585181981905
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2518730808771177
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2582345192417379
[INFO] [stdout] [Epoch 69]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24800843227934843
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26209857624432903
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25171947262462957
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25838699023049133
[INFO] [stdout] [Epoch 70]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24815486541694717
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2619510941308719
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2515778308028657
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2585276645103225
[INFO] [stdout] [Epoch 71]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24828996899529707
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2618150981226501
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25144722023656985
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25865745192771955
[INFO] [stdout] [Epoch 72]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2484146168309651
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26168969104064654
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25132677927501407
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25877719258462994
[INFO] [stdout] [Epoch 73]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24852961575786184
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2615740461750428
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25121571394608855
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2588876621202587
[INFO] [stdout] [Epoch 74]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2486357106998797
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2614674016922581
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25111329258482246
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25898957660261884
[INFO] [stdout] [Epoch 75]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24873358936873843
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2613690554944354
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2510188408964339
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2590835970571772
[INFO] [stdout] [Epoch 76]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24882388661329627
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.261278360493523
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2509317374175581
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2591703336582562
[INFO] [stdout] [Epoch 77]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24890718844497253
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2611947202654483
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2508514093425155
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2592503496072354
[INFO] [stdout] [Epoch 78]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2489840357623723
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2611175850529058
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2507773286843899
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2593241647200478
[INFO] [stdout] [Epoch 79]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24905492779671742
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2610464480880188
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25070900874331264
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2593922587449918
[INFO] [stdout] [Epoch 80]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24912032529827358
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26098084220861434
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25064600085673305
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25945507443047894
[INFO] [stdout] [Epoch 81]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2491806534826156
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2609203367441044
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.250587891408618
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2595130203610118
[INFO] [stdout] [Epoch 82]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24923630475429936
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26086453464900905
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25053429907648866
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2595664735784283
[INFO] [stdout] [Epoch 83]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24928764122430633
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2608130698640206
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25048487229698596
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25961578200427765
[INFO] [stdout] [Epoch 84]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24933499703649215
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26076560488619316
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2504392869322808
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25966126667807093
[INFO] [stdout] [Epoch 85]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24937868051720327
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2607218285313909
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25039724412112885
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25970322382511823
[INFO] [stdout] [Epoch 86]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24941897616122752
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26068145387352415
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2503584682997138
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25974192676668073
[INFO] [stdout] [Epoch 87]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24945614646630432
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2606442163463918
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2503227053786561
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25977762768425466
[INFO] [stdout] [Epoch 88]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24949043362754245
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26060987199511443
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2502897210636897
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2598105592489499
[INFO] [stdout] [Epoch 89]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2495220611022759
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26057819586521047
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25025929930852997
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25984093612612924
[INFO] [stdout] [Epoch 90]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24955123505511906
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2605489805183472
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25023124088940274
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25986895636473056
[INFO] [stdout] [Epoch 91]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24957814569227188
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2605220346646927
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2502053620915532
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2598948026800001
[INFO] [stdout] [Epoch 92]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2496029684934569
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2604971819026103
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25018149349884955
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25991864363772604
[INFO] [stdout] [Epoch 93]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249625865349257
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2604742595571865
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25015947887830475
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25994063474745344
[INFO] [stdout] [Epoch 94]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24964698561103935
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2604531176097748
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2501391741520108
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2599609194716135
[INFO] [stdout] [Epoch 95]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2496664670601228
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2604336177113598
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2501204464495733
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.259979630156975
[INFO] [stdout] [Epoch 96]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2496844368023441
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2604156322731294
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2501031732346969
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2599968888943523
[INFO] [stdout] [Epoch 97]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2497010120937214
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260399043628168
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2500872415000761
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26001280831205215
[INFO] [stdout] [Epoch 98]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24971630110248047
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26038374325867325
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25007254702521353
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2600274923081339
[INFO] [stdout] [Epoch 99]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2497304036123175
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26036963108354244
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2500589936922181
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26004103672617007
[INFO] [stdout] [Epoch 100]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24974341167139966
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2603566148015857
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25004649285502695
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26005352997884335
[INFO] [stdout] [Epoch 101]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24975541019126726
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2603446092860004
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25003496275785914
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26006505362338406
[INFO] [stdout] [Epoch 102]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24976647749948416
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26033353602608544
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.250024327999037
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26007568289254945
[INFO] [stdout] [Epoch 103]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24977668584959078
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26032332261249436
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25001451903662436
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26008548718456515
[INFO] [stdout] [Epoch 104]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2497861018916429
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2603139022626167
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25000547173260207
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2600945305151864
[INFO] [stdout] [Epoch 105]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2497947871063716
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2603052133829494
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24999712693256976
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26010287193479603
[INFO] [stdout] [Epoch 106]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24980279820576495
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602971991655618
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24998943007819083
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26011056591323617
[INFO] [stdout] [Epoch 107]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249810187502659
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602898072159934
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24998233084982566
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601176626948592
[INFO] [stdout] [Epoch 108]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24981700325172987
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602829892101254
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24997578283699007
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26012420862609675
[INFO] [stdout] [Epoch 109]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24982328996409064
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26027670057776486
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24996974323447124
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260130246457669
[INFO] [stdout] [Epoch 110]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24982908869753284
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602709002108579
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24996417256209405
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26013581562339033
[INFO] [stdout] [Epoch 111]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24983443732429167
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602655501944077
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499590344062954
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601409524973819
[INFO] [stdout] [Epoch 112]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24983937077807336
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602606155583279
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499542951818045
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601456906313586
[INFO] [stdout] [Epoch 113]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24984392128194477
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26025606404860097
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24994992391186296
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26015006097353105
[INFO] [stdout] [Epoch 114]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24984811855856734
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602518659162338
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24994589202553766
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26015409207054474
[INFO] [stdout] [Epoch 115]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498519900241394
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26024799372262564
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24994217317079664
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601578102537685
[INFO] [stdout] [Epoch 116]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24985556096730768
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26024442216007126
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499387430421196
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26016123981114353
[INFO] [stdout] [Epoch 117]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24985885471421085
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26024112788621734
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24993557922151038
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26016440314571
[INFO] [stdout] [Epoch 118]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498618927807286
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26023808937138915
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24993266103186967
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601673209218436
[INFO] [stdout] [Epoch 119]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24986469501292752
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602352867577845
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24992996940176387
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26017001220015273
[INFO] [stdout] [Epoch 120]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24986727971661576
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602327017296102
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499274867407055
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601724945619156
[INFO] [stdout] [Epoch 121]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24986966377685302
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26023031739331226
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499251968241251
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601747842238663
[INFO] [stdout] [Epoch 122]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24987186276819062
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26022811816711244
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499230846872829
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601768961440791
[INFO] [stdout] [Epoch 123]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498738910563631
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602260896791277
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499211365274224
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601788441196391
[INFO] [stdout] [Epoch 124]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498757618920911
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26022421867340684
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24991933961352833
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601806408767372
[INFO] [stdout] [Epoch 125]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24987748749760824
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260222492923266
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24991768220309335
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601822981537759
[INFO] [stdout] [Epoch 126]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24987907914647645
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26022090115135743
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24991615346535243
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601838267780282
[INFO] [stdout] [Epoch 127]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24988054723720846
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260219432955947
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24991474341048053
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601852367363482
[INFO] [stdout] [Epoch 128]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24988190136117913
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26021807874291986
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24991344282428948
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601865372403963
[INFO] [stdout] [Epoch 129]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498831503652672
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260216829663066
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24991224320799785
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601877367868039
[INFO] [stdout] [Epoch 130]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24988430240963713
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26021567755423697
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499111367226788
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260188843212668
[INFO] [stdout] [Epoch 131]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24988536502103728
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26021461488799763
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499101161380226
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26018986374674224
[INFO] [stdout] [Epoch 132]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24988634514196228
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602136347204174
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499091747850787
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601908050566528
[INFO] [stdout] [Epoch 133]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498872491760006
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602127306466864
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990830651266768
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26019167329245263
[INFO] [stdout] [Epoch 134]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498880830296628
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26021189675925516
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499075056471789
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26019247412679386
[INFO] [stdout] [Epoch 135]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24988885215096435
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602111276092243
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990676695548927
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601932127919844
[INFO] [stdout] [Epoch 136]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24988956156501355
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602104181707331
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499060856107625
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601938941141666
[INFO] [stdout] [Epoch 137]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989021590683744
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602097638081148
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990545716090418
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26019452254484493
[INFO] [stdout] [Epoch 138]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498908194516611
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020916024560004
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990487749946516
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26019510218996617
[INFO] [stdout] [Epoch 139]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989137614283574
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602086035393744
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990434283880628
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26019563683674246
[INFO] [stdout] [Epoch 140]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989188961759987
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020809005180556
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499038496853451
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26019612997839286
[INFO] [stdout] [Epoch 141]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989236323084105
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020761642767043
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990339481672605
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601965848369638
[INFO] [stdout] [Epoch 142]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989280007701273
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020717957223055
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990297526076183
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601970043843793
[INFO] [stdout] [Epoch 143]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498932030103508
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020677663100755
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990258827601122
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26019739136185693
[INFO] [stdout] [Epoch 144]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989357466352047
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020640497112946
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990223133386477
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601977482978161
[INFO] [stdout] [Epoch 145]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498939174648157
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020606216412706
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499019021020196
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260198077524397
[INFO] [stdout] [Epoch 146]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989423365402424
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260205745970063
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990159842924076
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601983811926972
[INFO] [stdout] [Epoch 147]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989452529705994
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602054543228963
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990131833130214
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26019866128682567
[INFO] [stdout] [Epoch 148]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989479429946113
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602051853169808
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990105997802092
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601989196368652
[INFO] [stdout] [Epoch 149]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989504241883928
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602049371946127
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990082168129865
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601991579308295
[INFO] [stdout] [Epoch 150]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498952712763627
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020470833454545
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990060188409036
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601993777256916
[INFO] [stdout] [Epoch 151]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989548236734835
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020449724139555
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990039915022927
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26019958045755653
[INFO] [stdout] [Epoch 152]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989567707103155
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602043025358712
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990021215504385
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601997674510436
[INFO] [stdout] [Epoch 153]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498958566595768
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020412294575945
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499000396767007
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601999399279418
[INFO] [stdout] [Epoch 154]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989602230639005
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602039572976134
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498998805882214
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020009901519175
[INFO] [stdout] [Epoch 155]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249896175093785
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020380450908465
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989973385011852
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020024575224887
[INFO] [stdout] [Epoch 156]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989631602005483
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020366358185015
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989959850360266
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020038109787486
[INFO] [stdout] [Epoch 157]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989644600599414
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020353359509
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498994736643185
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020050593640204
[INFO] [stdout] [Epoch 158]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989656590091594
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020341369947
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989935851656517
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602006210835113
[INFO] [stdout] [Epoch 159]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989667648819977
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602033031115922
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989925230796742
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020072729156113
[INFO] [stdout] [Epoch 160]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249896778490411
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602032011088754
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989915434455837
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020082525450394
[INFO] [stdout] [Epoch 161]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989687257402138
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260203107024835
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989906398624617
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602009156124195
[INFO] [stdout] [Epoch 162]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989695935376366
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020302024472675
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989898064263044
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602009989556977
[INFO] [stdout] [Epoch 163]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989703939664817
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260202940201531
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989890376914534
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602010758288957
[INFO] [stdout] [Epoch 164]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498971132256677
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602028663722466
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989883286350076
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020114673429584
[INFO] [stdout] [Epoch 165]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498971813232142
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020279827447473
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498987674624009
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602012121351879
[INFO] [stdout] [Epoch 166]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498972441342311
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602027354632661
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989870713851614
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602012724588957
[INFO] [stdout] [Epoch 167]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989730206912023
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020267752821374
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498986514976921
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602013280995692
[INFO] [stdout] [Epoch 168]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989735550642325
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602026240907719
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989860017637305
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020137942076016
[INFO] [stdout] [Epoch 169]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989740479529518
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602025748017819
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498985528392272
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602014267577971
[INFO] [stdout] [Epoch 170]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498974502577856
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602025293391908
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989850917695494
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602014704199765
[INFO] [stdout] [Epoch 171]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989749219094287
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260202487405948
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498984689042688
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602015106925838
[INFO] [stdout] [Epoch 172]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989753086875513
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020244872806286
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498984317580281
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602015478387573
[INFO] [stdout] [Epoch 173]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498975665439403
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602024130528157
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989839749552098
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602015821012073
[INFO] [stdout] [Epoch 174]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989759944959744
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020238014710584
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989836589287728
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020161370380224
[INFO] [stdout] [Epoch 175]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989762980072977
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020234979592866
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989833674360692
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020164285303127
[INFO] [stdout] [Epoch 176]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989765779564949
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020232180097075
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989830985724937
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602016697393535
[INFO] [stdout] [Epoch 177]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989768361727355
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020229597931416
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989828505813055
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020169453844233
[INFO] [stdout] [Epoch 178]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989770743431863
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020227216224134
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989826218421404
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020171741233333
[INFO] [stdout] [Epoch 179]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498977294024037
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020225019413273
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989824108604267
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602017385104828
[INFO] [stdout] [Epoch 180]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989774966506664
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020222993144954
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989822162576208
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020175797074485
[INFO] [stdout] [Epoch 181]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989776835470248
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020221124179665
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989820367621934
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020177592027166
[INFO] [stdout] [Epoch 182]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498977855934282
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602021940030563
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989818712013345
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602017924763443
[INFO] [stdout] [Epoch 183]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989780149388047
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020217810259166
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989817184932725
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602018077471388
[INFO] [stdout] [Epoch 184]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989781615995174
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020216343650965
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989815776402236
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020182183243395
[INFO] [stdout] [Epoch 185]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989782968746932
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602021499089831
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989814477218608
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602018348242619
[INFO] [stdout] [Epoch 186]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989784216482108
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020213743162357
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498981327889301
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020184680751074
[INFO] [stdout] [Epoch 187]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498978536735334
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602021259229047
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498981217359566
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602018578604782
[INFO] [stdout] [Epoch 188]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989786428880353
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020211530762893
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989811154104594
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020186805538353
[INFO] [stdout] [Epoch 189]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989787407999078
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602021055164369
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989810213758523
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602018774588399
[INFO] [stdout] [Epoch 190]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989788311107036
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020964853531
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498980934641327
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602018861322886
[INFO] [stdout] [Epoch 191]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989789144105073
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020208815536927
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989808546401618
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020189413240186
[INFO] [stdout] [Epoch 192]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498978991243597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020804720573
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498980780849636
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019015114517
[INFO] [stdout] [Epoch 193]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989790621119928
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020207338521506
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989807127876043
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020190831765233
[INFO] [stdout] [Epoch 194]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979127478745
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020668485376
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989806500093562
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020191459547515
[INFO] [stdout] [Epoch 195]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979187770957
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020608193145
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498980592104699
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020192038593914
[INFO] [stdout] [Epoch 196]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989792433825744
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020205525815115
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498980538695287
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020192572687856
[INFO] [stdout] [Epoch 197]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989792946769598
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020205012871106
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989804894321482
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020193065319114
[INFO] [stdout] [Epoch 198]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989793419892675
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020204539747915
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989804439933963
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019351970652
[INFO] [stdout] [Epoch 199]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979385628635
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020410335413
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.249898040208214
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020193938818975
[INFO] [stdout] [Epoch 200]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979425880197
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020370083842
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989803634245325
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020194325394974
[INFO] [stdout] [Epoch 201]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989794630069573
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020332957072
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989803277679842
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020194681960374
[INFO] [stdout] [Epoch 202]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989794972515
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020298712522
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989802948795203
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019501084495
[INFO] [stdout] [Epoch 203]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979528837576
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020267126439
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989802645442485
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020195314197614
[INFO] [stdout] [Epoch 204]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989795579715676
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020237992443
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989802365639588
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020195594000445
[INFO] [stdout] [Epoch 205]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979584843833
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020211120171
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989802107558315
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020195852081673
[INFO] [stdout] [Epoch 206]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989796096299555
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020201863340453
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989801869512374
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019609012757
[INFO] [stdout] [Epoch 207]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989796324918848
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020201634721124
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989801649946383
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020196309693516
[INFO] [stdout] [Epoch 208]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979653579
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020201423849926
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989801447425705
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020196512214167
[INFO] [stdout] [Epoch 209]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989796730290845
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020122934905
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989801260627073
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019669901276
[INFO] [stdout] [Epoch 210]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989796909692233
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020104994762
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498980108832997
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019687130983
[INFO] [stdout] [Epoch 211]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989797075166365
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020200884473466
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.249898009294086
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019703023117
[INFO] [stdout] [Epoch 212]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989797227794439
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020073184536
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498980078282459
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019717681515
[INFO] [stdout] [Epoch 213]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989797368573702
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020059106606
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989800647620172
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019731201955
[INFO] [stdout] [Epoch 214]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989797498424024
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020200461215726
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498980052291192
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020197436727777
[INFO] [stdout] [Epoch 215]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989797618193826
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260202003414459
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989800407884988
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019755175468
[INFO] [stdout] [Epoch 216]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989797728665675
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020023097403
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989800301787823
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020197657851823
[INFO] [stdout] [Epoch 217]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989797830561392
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020200129078297
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498980020392718
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020197755712454
[INFO] [stdout] [Epoch 218]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989797924546756
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020200035092905
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989800113663627
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020197845975984
[INFO] [stdout] [Epoch 219]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798011235864
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019994840379
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989800030407405
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019792923219
[INFO] [stdout] [Epoch 220]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798091195134
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019986844449
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799953614528
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019800602504
[INFO] [stdout] [Epoch 221]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798164947016
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019979469259
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979988278322
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198076856343
[INFO] [stdout] [Epoch 222]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798232973412
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019972666617
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799817450656
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019814218888
[INFO] [stdout] [Epoch 223]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798295718793
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199663920773
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979975718999
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019820244953
[INFO] [stdout] [Epoch 224]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798353593143
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199606046407
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799701607468
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019825803204
[INFO] [stdout] [Epoch 225]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249897984069746
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019955266493
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799650339917
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019830929957
[INFO] [stdout] [Epoch 226]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798456211948
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019950342757
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799603052365
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019835658711
[INFO] [stdout] [Epoch 227]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798501626914
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199458012583
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979955943584
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198400203615
[INFO] [stdout] [Epoch 228]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798543516223
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199416123263
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799519205327
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019844043409
[INFO] [stdout] [Epoch 229]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798582153597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019937748587
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799482098002
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019847754141
[INFO] [stdout] [Epoch 230]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979861779148
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019934184796
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979944787138
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198511768017
[INFO] [stdout] [Epoch 231]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798650662723
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199308976705
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799416301847
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198543337536
[INFO] [stdout] [Epoch 232]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798680982112
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019927865731
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799387183098
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019857245627
[INFO] [stdout] [Epoch 233]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798708947758
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019925069165
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979936032489
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019859931446
[INFO] [stdout] [Epoch 234]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798734742372
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199224897006
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799335551743
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201986240876
[INFO] [stdout] [Epoch 235]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798758534512
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199201104854
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979931270178
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019864693754
[INFO] [stdout] [Epoch 236]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798780479622
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199179159736
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799291625686
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019866801361
[INFO] [stdout] [Epoch 237]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798800721094
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019915891824
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799272185786
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201986874535
[INFO] [stdout] [Epoch 238]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798819391176
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019914024814
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799254255035
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019870538424
[INFO] [stdout] [Epoch 239]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979883661187
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019912302743
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799237716287
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019872192297
[INFO] [stdout] [Epoch 240]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798852495687
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199107143593
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799222461462
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198737177774
[INFO] [stdout] [Epoch 241]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798867146415
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019909249285
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.249897992083909
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019875124833
[INFO] [stdout] [Epoch 242]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798880659791
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019907897946
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799195412665
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019876422653
[INFO] [stdout] [Epoch 243]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979889312408
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199066515165
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799183441966
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198776197223
[INFO] [stdout] [Epoch 244]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798904620747
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019905501848
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799172400562
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019878723861
[INFO] [stdout] [Epoch 245]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798915224903
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199044414294
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799162216333
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019879742283
[INFO] [stdout] [Epoch 246]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798925005843
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199034633346
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799152822723
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019880681642
[INFO] [stdout] [Epoch 247]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798934027463
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019902561171
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799144158356
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198815480783
[INFO] [stdout] [Epoch 248]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798942348734
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019901729042
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799136166604
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198823472496
[INFO] [stdout] [Epoch 249]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798950023992
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199009615147
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799128795284
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019883084381
[INFO] [stdout] [Epoch 250]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798957103423
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199002535715
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979912199621
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198837642866
[INFO] [stdout] [Epoch 251]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979896363325
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198996005856
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799115724964
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201988439141
[INFO] [stdout] [Epoch 252]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798969656157
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198989982946
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979910994056
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198849698495
[INFO] [stdout] [Epoch 253]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249897989752115
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019898442757
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799104605195
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198855033827
[INFO] [stdout] [Epoch 254]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798980335584
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019897930349
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799099684037
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198859954974
[INFO] [stdout] [Epoch 255]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798985061865
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019897457718
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799095144918
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019886449408
[INFO] [stdout] [Epoch 256]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798989421236
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989702178
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979909095819
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198868680794
[INFO] [stdout] [Epoch 257]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798993442167
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019896619685
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979908709648
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019887254248
[INFO] [stdout] [Epoch 258]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798997150953
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019896248805
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799083534564
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198876104383
[INFO] [stdout] [Epoch 259]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799000571825
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198959067165
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799080249153
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019887938978
[INFO] [stdout] [Epoch 260]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799003727134
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019895591184
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799077218808
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198882420115
[INFO] [stdout] [Epoch 261]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799006637478
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198953001483
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799074423713
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201988852152
[INFO] [stdout] [Epoch 262]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799009321886
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198950317036
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799071845603
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019888779329
[INFO] [stdout] [Epoch 263]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799011797906
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019894784102
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799069467625
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019889017124
[INFO] [stdout] [Epoch 264]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799014081712
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198945557194
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979906727426
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201988923646
[INFO] [stdout] [Epoch 265]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799016188224
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019894345067
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979906525117
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019889438767
[INFO] [stdout] [Epoch 266]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979901813119
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019894150768
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799063385143
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019889625368
[INFO] [stdout] [Epoch 267]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799019923337
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019893971553
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799061663984
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019889797483
[INFO] [stdout] [Epoch 268]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799021576337
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198938062494
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979906007642
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019889956236
[INFO] [stdout] [Epoch 269]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799023101017
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198936537814
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799058612125
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019890102665
[INFO] [stdout] [Epoch 270]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979902450734
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019893513147
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799057261503
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019890237726
[INFO] [stdout] [Epoch 271]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979902580448
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198933834315
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979905601572
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019890362303
[INFO] [stdout] [Epoch 272]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799027000922
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019893263785
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799054866663
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198904772057
[INFO] [stdout] [Epoch 273]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979902810448
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019893153428
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799053806805
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198905831915
[INFO] [stdout] [Epoch 274]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799029122375
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198930516364
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979905282922
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019890680948
[INFO] [stdout] [Epoch 275]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799030061244
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892957749
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799051927525
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019890771116
[INFO] [stdout] [Epoch 276]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799030927232
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892871148
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979905109583
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019890854283
[INFO] [stdout] [Epoch 277]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979903172599
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892791271
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979905032871
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019890930994
[INFO] [stdout] [Epoch 278]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979903246274
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198927175947
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799049621142
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198910017495
[INFO] [stdout] [Epoch 279]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799033142285
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892649638
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799048968508
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198910670117
[INFO] [stdout] [Epoch 280]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799033769072
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892586957
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799048366534
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198911272074
[INFO] [stdout] [Epoch 281]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799034347218
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892529142
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799047811292
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198911827297
[INFO] [stdout] [Epoch 282]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799034880472
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892475815
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904729914
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198912339426
[INFO] [stdout] [Epoch 283]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979903537233
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892426628
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904682676
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989128118
[INFO] [stdout] [Epoch 284]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799035826005
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892381259
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799046391054
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198913247483
[INFO] [stdout] [Epoch 285]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799036244464
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198923394117
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799045989167
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198913649356
[INFO] [stdout] [Epoch 286]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799036630436
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198923008125
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799045618483
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198914020026
[INFO] [stdout] [Epoch 287]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979903698644
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989226521
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799045276584
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891436192
[INFO] [stdout] [Epoch 288]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979903731481
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892232372
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799044961222
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891467726
[INFO] [stdout] [Epoch 289]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799037617685
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198922020826
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799044670336
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198914968135
[INFO] [stdout] [Epoch 290]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799037897054
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892174145
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799044402028
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198915236425
[INFO] [stdout] [Epoch 291]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799038154728
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198921483745
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799044154554
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198915483866
[INFO] [stdout] [Epoch 292]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799038392396
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198921246074
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799043926295
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198915712117
[INFO] [stdout] [Epoch 293]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979903861162
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892102684
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904371576
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198915922643
[INFO] [stdout] [Epoch 294]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799038813823
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892082461
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799043521568
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891611682
[INFO] [stdout] [Epoch 295]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799039000324
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892063809
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799043342448
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198916295917
[INFO] [stdout] [Epoch 296]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799039172356
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892046605
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799043177235
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891646111
[INFO] [stdout] [Epoch 297]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979903933102
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198920307364
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799043024843
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989166135
[INFO] [stdout] [Epoch 298]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799039477384
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892016099
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799042884284
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891675404
[INFO] [stdout] [Epoch 299]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799039612381
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892002597
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799042754626
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198916883686
[INFO] [stdout] [Epoch 300]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799039736907
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891990143
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799042635047
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198917003245
[INFO] [stdout] [Epoch 301]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799039851746
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198919786586
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799042524757
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891711352
[INFO] [stdout] [Epoch 302]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799039957675
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198919680637
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799042423014
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891721523
[INFO] [stdout] [Epoch 303]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904005537
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891958292
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799042329186
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891730905
[INFO] [stdout] [Epoch 304]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040145486
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891949279
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904224263
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989173956
[INFO] [stdout] [Epoch 305]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040228614
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891940964
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799042162797
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891747541
[INFO] [stdout] [Epoch 306]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040305297
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891933295
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904208916
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198917549037
[INFO] [stdout] [Epoch 307]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040376015
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891926221
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904202124
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891761694
[INFO] [stdout] [Epoch 308]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040441238
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198919196974
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041958602
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891767956
[INFO] [stdout] [Epoch 309]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040501406
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198919136783
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041900818
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198917737336
[INFO] [stdout] [Epoch 310]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040556906
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891908127
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041847516
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198917790616
[INFO] [stdout] [Epoch 311]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040608088
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198919030074
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041798363
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198917839754
[INFO] [stdout] [Epoch 312]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249897990406553
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918982856
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041753022
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891788508
[INFO] [stdout] [Epoch 313]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040698846
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918939297
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041711208
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891792689
[INFO] [stdout] [Epoch 314]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904073901
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918899107
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041672625
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891796544
[INFO] [stdout] [Epoch 315]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040776062
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891886205
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041637048
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891800101
[INFO] [stdout] [Epoch 316]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904081023
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918827864
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041604227
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891803382
[INFO] [stdout] [Epoch 317]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040841754
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891879632
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041573957
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918064075
[INFO] [stdout] [Epoch 318]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040870828
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918767223
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904154603
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891809198
[INFO] [stdout] [Epoch 319]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040897648
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989187404
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041520278
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918117726
[INFO] [stdout] [Epoch 320]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904092239
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891871564
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041496522
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891814146
[INFO] [stdout] [Epoch 321]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249897990409452
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918692805
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904147461
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891816336
[INFO] [stdout] [Epoch 322]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040966248
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891867174
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.249897990414544
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918183557
[INFO] [stdout] [Epoch 323]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040985652
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918652326
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041435762
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918202175
[INFO] [stdout] [Epoch 324]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041003563
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891863441
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041418567
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891821936
[INFO] [stdout] [Epoch 325]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041020072
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891861787
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041402705
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918235193
[INFO] [stdout] [Epoch 326]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041035302
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891860263
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041388075
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918249815
[INFO] [stdout] [Epoch 327]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041049354
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891858856
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041374577
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918263293
[INFO] [stdout] [Epoch 328]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041062313
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918575577
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041362135
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891827572
[INFO] [stdout] [Epoch 329]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904107427
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891856362
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041350658
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918287185
[INFO] [stdout] [Epoch 330]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041085287
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918552584
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904134008
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891829775
[INFO] [stdout] [Epoch 331]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041095448
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918542414
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041330313
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989183075
[INFO] [stdout] [Epoch 332]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041104838
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918533
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041321298
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918316495
[INFO] [stdout] [Epoch 333]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041113486
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891852434
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041312996
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891832479
[INFO] [stdout] [Epoch 334]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904112146
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891851635
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041305338
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891833243
[INFO] [stdout] [Epoch 335]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041128827
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891850897
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041298266
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891833949
[INFO] [stdout] [Epoch 336]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041135613
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891850216
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041291752
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918345977
[INFO] [stdout] [Epoch 337]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904114187
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918495896
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041285735
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918351983
[INFO] [stdout] [Epoch 338]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904114764
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918490106
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041280195
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891835751
[INFO] [stdout] [Epoch 339]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041152963
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891848476
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904127508
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891836261
[INFO] [stdout] [Epoch 340]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041157879
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891847985
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904127036
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918367315
[INFO] [stdout] [Epoch 341]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041162414
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918475296
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041265998
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918371656
[INFO] [stdout] [Epoch 342]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041166605
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891847108
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126198
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918375653
[INFO] [stdout] [Epoch 343]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041170455
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891846722
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041258284
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918379345
[INFO] [stdout] [Epoch 344]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041174013
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891846365
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041254865
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838275
[INFO] [stdout] [Epoch 345]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249897990411773
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891846034
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041251712
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918385884
[INFO] [stdout] [Epoch 346]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041180324
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918457305
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041248806
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838877
[INFO] [stdout] [Epoch 347]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041183122
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918454496
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904124612
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918391446
[INFO] [stdout] [Epoch 348]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041185695
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891845189
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041243643
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989183939
[INFO] [stdout] [Epoch 349]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041188065
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918449516
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041241378
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891839614
[INFO] [stdout] [Epoch 350]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041190247
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891844732
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904123928
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891839825
[INFO] [stdout] [Epoch 351]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904119228
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891844528
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904123734
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918400167
[INFO] [stdout] [Epoch 352]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041194138
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184434
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041235553
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918401943
[INFO] [stdout] [Epoch 353]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041195854
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918441667
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041233898
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918403575
[INFO] [stdout] [Epoch 354]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041197439
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891844007
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041232377
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840508
[INFO] [stdout] [Epoch 355]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041198898
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184386
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041230978
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918406473
[INFO] [stdout] [Epoch 356]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041200247
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918437215
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904122968
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918407744
[INFO] [stdout] [Epoch 357]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904120149
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918435966
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904122848
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840893
[INFO] [stdout] [Epoch 358]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904120264
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184348
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904122738
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841001
[INFO] [stdout] [Epoch 359]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249897990412037
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918433723
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904122636
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918411014
[INFO] [stdout] [Epoch 360]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041204677
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843273
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041225427
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918411935
[INFO] [stdout] [Epoch 361]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041205576
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918431825
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904122456
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918412785
[INFO] [stdout] [Epoch 362]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041206404
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918430987
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041223776
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918413573
[INFO] [stdout] [Epoch 363]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041207175
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918430193
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041223032
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841429
[INFO] [stdout] [Epoch 364]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904120788
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918429466
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041222355
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918414955
[INFO] [stdout] [Epoch 365]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041208535
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842881
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041221733
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841556
[INFO] [stdout] [Epoch 366]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041209135
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842819
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041221156
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918416126
[INFO] [stdout] [Epoch 367]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041209684
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842761
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041220623
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841663
[INFO] [stdout] [Epoch 368]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041210195
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842709
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041220134
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841711
[INFO] [stdout] [Epoch 369]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041210659
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842661
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121968
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841754
[INFO] [stdout] [Epoch 370]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041211097
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842616
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041219268
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918417936
[INFO] [stdout] [Epoch 371]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121149
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842575
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121889
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841831
[INFO] [stdout] [Epoch 372]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121187
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842537
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041218536
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841865
[INFO] [stdout] [Epoch 373]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041212202
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918425014
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041218214
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841897
[INFO] [stdout] [Epoch 374]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041212518
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918424686
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041217903
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841925
[INFO] [stdout] [Epoch 375]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041212812
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842437
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041217625
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918419524
[INFO] [stdout] [Epoch 376]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041213084
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918424087
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121736
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841976
[INFO] [stdout] [Epoch 377]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121333
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918423826
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041217126
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918419984
[INFO] [stdout] [Epoch 378]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041213556
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918423576
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216915
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842018
[INFO] [stdout] [Epoch 379]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041213767
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842335
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216715
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918420373
[INFO] [stdout] [Epoch 380]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041213961
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842315
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216526
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842054
[INFO] [stdout] [Epoch 381]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121414
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918422954
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216348
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184207
[INFO] [stdout] [Epoch 382]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041214306
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842277
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216193
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918420834
[INFO] [stdout] [Epoch 383]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041214455
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918422605
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121605
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842097
[INFO] [stdout] [Epoch 384]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249897990412146
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918422444
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041215904
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918421084
[INFO] [stdout] [Epoch 385]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041214722
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842231
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041215804
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842119
[INFO] [stdout] [Epoch 386]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041214833
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842218
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041215693
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842128
[INFO] [stdout] [Epoch 387]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041214944
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842206
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041215582
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842138
[INFO] [stdout] [Epoch 388]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121505
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918421944
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041215482
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842146
[INFO] [stdout] [Epoch 389]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215144
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918421833
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041215383
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842155
[INFO] [stdout] [Epoch 390]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215244
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842172
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041215305
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918421605
[INFO] [stdout] [Epoch 391]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215321
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842163
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041215227
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842167
[INFO] [stdout] [Epoch 392]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215388
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842154
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121516
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842172
[INFO] [stdout] [Epoch 393]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215455
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842146
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041215094
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842178
[INFO] [stdout] [Epoch 394]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121552
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842138
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041215027
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842182
[INFO] [stdout] [Epoch 395]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215582
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184213
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214972
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918421866
[INFO] [stdout] [Epoch 396]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215638
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918421233
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214927
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184219
[INFO] [stdout] [Epoch 397]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215693
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918421156
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214872
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918421944
[INFO] [stdout] [Epoch 398]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215738
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918421095
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214827
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918421966
[INFO] [stdout] [Epoch 399]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215782
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842104
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214783
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918421994
[INFO] [stdout] [Epoch 400]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215816
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918420995
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121475
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918422005
[INFO] [stdout] [Epoch 401]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215843
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842095
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214728
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918422016
[INFO] [stdout] [Epoch 402]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215877
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918420906
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214705
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842203
[INFO] [stdout] [Epoch 403]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249897990412159
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842087
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214683
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842203
[INFO] [stdout] [Epoch 404]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215915
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918420834
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121466
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842204
[INFO] [stdout] [Epoch 405]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215938
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842079
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121465
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842204
[INFO] [stdout] [Epoch 406]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215954
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918420767
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214628
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842205
[INFO] [stdout] [Epoch 407]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215977
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842072
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214605
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842205
[INFO] [stdout] [Epoch 408]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842069
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214583
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842206
[INFO] [stdout] [Epoch 409]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216015
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918420656
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121456
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842206
[INFO] [stdout] [Epoch 410]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216038
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918420623
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121455
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842207
[INFO] [stdout] [Epoch 411]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216054
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918420584
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214528
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842207
[INFO] [stdout] [Epoch 412]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216076
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842055
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214517
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842206
[INFO] [stdout] [Epoch 413]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216082
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842053
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214505
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842206
[INFO] [stdout] [Epoch 414]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216093
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918420506
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214505
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842205
[INFO] [stdout] [Epoch 415]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918420484
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214494
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842205
[INFO] [stdout] [Epoch 416]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121611
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842046
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214483
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842204
[INFO] [stdout] [Epoch 417]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121612
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842044
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214472
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842204
[INFO] [stdout] [Epoch 418]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216126
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918420406
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214472
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842203
[INFO] [stdout] [Epoch 419]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216137
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918420384
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121446
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842203
[INFO] [stdout] [Epoch 420]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216143
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842036
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121445
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918422016
[INFO] [stdout] [Epoch 421]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216154
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842034
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121444
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918422005
[INFO] [stdout] [Epoch 422]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121616
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842032
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121444
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918422005
[INFO] [stdout] [Epoch 423]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121617
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842029
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214428
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918421994
[INFO] [stdout] [Epoch 424]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216176
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842027
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214417
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918421994
[INFO] [stdout] [Epoch 425]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216187
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918420245
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214417
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842198
[INFO] [stdout] [Epoch 426]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216193
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918420223
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214406
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918421966
[INFO] [stdout] [Epoch 427]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216204
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184202
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214394
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918421966
[INFO] [stdout] [Epoch 428]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121621
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842018
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214383
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918421955
[INFO] [stdout] [Epoch 429]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121622
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918420157
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214383
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918421955
[INFO] [stdout] [Epoch 430]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216226
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918420134
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214372
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918421944
[INFO] [stdout] [Epoch 431]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216237
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842011
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121436
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918421933
[INFO] [stdout] [Epoch 432]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216243
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842009
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121436
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918421933
[INFO] [stdout] [Epoch 433]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216254
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842007
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121436
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842191
[INFO] [stdout] [Epoch 434]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216254
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918420046
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121435
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842191
[INFO] [stdout] [Epoch 435]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216265
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842002
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121434
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184219
[INFO] [stdout] [Epoch 436]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121627
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918419996
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121434
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842189
[INFO] [stdout] [Epoch 437]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216276
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918419973
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214328
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842189
[INFO] [stdout] [Epoch 438]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216287
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841995
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214317
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842188
[INFO] [stdout] [Epoch 439]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216293
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841993
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214317
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842188
[INFO] [stdout] [Epoch 440]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216304
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918419907
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214306
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918421866
[INFO] [stdout] [Epoch 441]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121631
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918419885
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214295
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918421855
[INFO] [stdout] [Epoch 442]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121632
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841986
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214295
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918421855
[INFO] [stdout] [Epoch 443]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216326
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841984
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214283
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918421844
[INFO] [stdout] [Epoch 444]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216332
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841982
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214272
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918421833
[INFO] [stdout] [Epoch 445]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216332
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918419807
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214283
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842182
[INFO] [stdout] [Epoch 446]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216337
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918419785
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214283
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184218
[INFO] [stdout] [Epoch 447]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216337
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918419774
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214283
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842179
[INFO] [stdout] [Epoch 448]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216332
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841976
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214295
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918421766
[INFO] [stdout] [Epoch 449]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216326
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841975
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214295
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918421744
[INFO] [stdout] [Epoch 450]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216326
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918419735
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214295
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918421733
[INFO] [stdout] [Epoch 451]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121632
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918419724
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214306
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918421705
[INFO] [stdout] [Epoch 452]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216315
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918419724
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214306
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918421683
[INFO] [stdout] [Epoch 453]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216315
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184197
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214306
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842167
[INFO] [stdout] [Epoch 454]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121631
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184197
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214317
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842165
[INFO] [stdout] [Epoch 455]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216304
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841968
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214317
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842163
[INFO] [stdout] [Epoch 456]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121631
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841967
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214306
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842163
[INFO] [stdout] [Epoch 457]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216315
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918419646
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214306
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918421616
[INFO] [stdout] [Epoch 458]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121632
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918419624
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214295
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918421616
[INFO] [stdout] [Epoch 459]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216332
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184196
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214295
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918421605
[INFO] [stdout] [Epoch 460]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216337
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841958
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214283
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918421594
[INFO] [stdout] [Epoch 461]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216343
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918419557
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214272
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918421594
[INFO] [stdout] [Epoch 462]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216354
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918419535
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214272
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918421583
[INFO] [stdout] [Epoch 463]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121636
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918419524
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214272
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842155
[INFO] [stdout] [Epoch 464]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216343
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918419524
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214272
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842155
[INFO] [stdout] [Epoch 465]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216348
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184195
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214283
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842153
[INFO] [stdout] [Epoch 466]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216348
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841948
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214283
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918421505
[INFO] [stdout] [Epoch 467]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216343
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841948
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214295
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918421483
[INFO] [stdout] [Epoch 468]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216337
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841945
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214295
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842147
[INFO] [stdout] [Epoch 469]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216337
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841945
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214295
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842145
[INFO] [stdout] [Epoch 470]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216332
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841944
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214306
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842142
[INFO] [stdout] [Epoch 471]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216326
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841943
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214306
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842141
[INFO] [stdout] [Epoch 472]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121632
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841942
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214317
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842139
[INFO] [stdout] [Epoch 473]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121632
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918419407
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214317
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918421367
[INFO] [stdout] [Epoch 474]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216315
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918419396
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214317
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918421356
[INFO] [stdout] [Epoch 475]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121631
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918419385
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214328
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918421333
[INFO] [stdout] [Epoch 476]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121631
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918419374
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214317
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842132
[INFO] [stdout] [Epoch 477]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216315
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841935
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214317
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842132
[INFO] [stdout] [Epoch 478]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216326
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841933
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214306
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842131
[INFO] [stdout] [Epoch 479]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216332
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918419307
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214306
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184213
[INFO] [stdout] [Epoch 480]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216337
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918419296
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214295
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184213
[INFO] [stdout] [Epoch 481]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216343
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918419274
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214283
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842129
[INFO] [stdout] [Epoch 482]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216354
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841925
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214295
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918421267
[INFO] [stdout] [Epoch 483]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216348
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841923
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214295
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918421245
[INFO] [stdout] [Epoch 484]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216343
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841923
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214295
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918421245
[INFO] [stdout] [Epoch 485]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216348
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841921
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214295
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842122
[INFO] [stdout] [Epoch 486]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216343
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918419196
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214306
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184212
[INFO] [stdout] [Epoch 487]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216337
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841918
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214306
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842119
[INFO] [stdout] [Epoch 488]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216337
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841918
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214306
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918421167
[INFO] [stdout] [Epoch 489]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216332
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841916
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214317
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842114
[INFO] [stdout] [Epoch 490]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216326
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841916
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214317
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918421117
[INFO] [stdout] [Epoch 491]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121632
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918419146
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214328
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918421106
[INFO] [stdout] [Epoch 492]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216315
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918419135
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214328
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918421084
[INFO] [stdout] [Epoch 493]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121631
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918419124
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121434
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842106
[INFO] [stdout] [Epoch 494]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121631
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918419113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214328
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842105
[INFO] [stdout] [Epoch 495]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121632
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841909
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214328
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842105
[INFO] [stdout] [Epoch 496]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216326
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841907
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214317
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842104
[INFO] [stdout] [Epoch 497]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216332
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918419046
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214317
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842103
[INFO] [stdout] [Epoch 498]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216337
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918419024
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214306
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918421017
[INFO] [stdout] [Epoch 499]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216343
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918419
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214306
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918421017
[INFO] [stdout] [Epoch 500]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216348
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841899
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214295
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918421006
[INFO] [stdout] [Epoch 501]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216354
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841897
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214295
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918420995
[INFO] [stdout] [Epoch 502]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121636
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841896
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214295
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842097
[INFO] [stdout] [Epoch 503]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216354
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918418935
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214295
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842096
[INFO] [stdout] [Epoch 504]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216348
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918418935
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214306
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842094
[INFO] [stdout] [Epoch 505]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216343
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918418924
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214306
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918420917
[INFO] [stdout] [Epoch 506]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216343
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918418913
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214317
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918420895
[INFO] [stdout] [Epoch 507]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216337
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918418896
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214317
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918420884
[INFO] [stdout] [Epoch 508]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216332
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918418885
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214328
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918420856
[INFO] [stdout] [Epoch 509]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216326
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918418885
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214328
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918420834
[INFO] [stdout] [Epoch 510]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121632
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918418874
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121434
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842081
[INFO] [stdout] [Epoch 511]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216315
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918418863
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121434
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184208
[INFO] [stdout] [Epoch 512]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121631
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918418863
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121435
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842078
[INFO] [stdout] [Epoch 513]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121631
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841884
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121435
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918420767
[INFO] [stdout] [Epoch 514]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216315
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841882
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121434
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918420756
[INFO] [stdout] [Epoch 515]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121632
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918418797
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121434
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918420745
[INFO] [stdout] [Epoch 516]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216326
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918418774
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214328
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918420745
[INFO] [stdout] [Epoch 517]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216332
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918418763
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214328
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918420734
[INFO] [stdout] [Epoch 518]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216337
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841874
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214317
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842072
[INFO] [stdout] [Epoch 519]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216343
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841873
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214317
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842071
[INFO] [stdout] [Epoch 520]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216348
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841871
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214306
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842071
[INFO] [stdout] [Epoch 521]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216354
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918418685
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214317
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842069
[INFO] [stdout] [Epoch 522]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216348
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918418674
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214306
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842068
[INFO] [stdout] [Epoch 523]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216354
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918418663
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214317
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918420656
[INFO] [stdout] [Epoch 524]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216348
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841864
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214317
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918420634
[INFO] [stdout] [Epoch 525]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216343
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841864
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214328
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918420623
[INFO] [stdout] [Epoch 526]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216337
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841863
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214328
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184206
[INFO] [stdout] [Epoch 527]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216332
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918418613
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121434
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918420573
[INFO] [stdout] [Epoch 528]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216326
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918418613
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121435
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842055
[INFO] [stdout] [Epoch 529]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121632
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184186
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121435
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842053
[INFO] [stdout] [Epoch 530]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121631
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841859
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121436
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842052
[INFO] [stdout] [Epoch 531]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216304
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841859
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121436
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918420495
[INFO] [stdout] [Epoch 532]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216304
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841857
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121436
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918420484
[INFO] [stdout] [Epoch 533]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121631
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918418547
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121435
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918420473
[INFO] [stdout] [Epoch 534]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216315
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918418536
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121435
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842046
[INFO] [stdout] [Epoch 535]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121632
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918418513
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121434
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842045
[INFO] [stdout] [Epoch 536]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216326
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184185
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121434
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842045
[INFO] [stdout] [Epoch 537]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216332
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841848
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121434
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842044
[INFO] [stdout] [Epoch 538]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216337
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841846
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214328
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842043
[INFO] [stdout] [Epoch 539]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216343
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918418436
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214328
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842042
[INFO] [stdout] [Epoch 540]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216348
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918418413
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214317
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918420406
[INFO] [stdout] [Epoch 541]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216348
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184184
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214317
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918420395
[INFO] [stdout] [Epoch 542]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216354
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841839
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214317
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918420395
[INFO] [stdout] [Epoch 543]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121636
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841837
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214317
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918420373
[INFO] [stdout] [Epoch 544]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216354
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841836
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214328
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842035
[INFO] [stdout] [Epoch 545]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216348
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841834
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214328
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842033
[INFO] [stdout] [Epoch 546]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216343
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841834
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121434
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184203
[INFO] [stdout] [Epoch 547]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216337
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841833
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121435
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842028
[INFO] [stdout] [Epoch 548]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216326
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841832
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121435
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842027
[INFO] [stdout] [Epoch 549]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121632
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841832
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121436
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918420245
[INFO] [stdout] [Epoch 550]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216315
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841831
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121436
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918420223
[INFO] [stdout] [Epoch 551]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121631
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918418297
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214372
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184202
[INFO] [stdout] [Epoch 552]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121631
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918418286
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214372
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842019
[INFO] [stdout] [Epoch 553]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216315
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918418275
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121436
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842018
[INFO] [stdout] [Epoch 554]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216315
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841825
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121436
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842017
[INFO] [stdout] [Epoch 555]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121632
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841823
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121435
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842017
[INFO] [stdout] [Epoch 556]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216326
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841821
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121435
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918420157
[INFO] [stdout] [Epoch 557]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216332
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918418197
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121435
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918420145
[INFO] [stdout] [Epoch 558]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216337
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918418186
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121434
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918420134
[INFO] [stdout] [Epoch 559]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216337
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918418164
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121434
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918420123
[INFO] [stdout] [Epoch 560]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216343
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841814
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121434
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842011
[INFO] [stdout] [Epoch 561]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216348
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841812
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214328
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184201
[INFO] [stdout] [Epoch 562]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216354
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841811
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121434
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842008
[INFO] [stdout] [Epoch 563]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216343
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918418097
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121434
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891842008
[INFO] [stdout] [Epoch 564]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216348
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918418075
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121434
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918420057
[INFO] [stdout] [Epoch 565]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216343
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918418075
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121435
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918420034
[INFO] [stdout] [Epoch 566]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216337
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918418075
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121435
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918420007
[INFO] [stdout] [Epoch 567]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216326
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918418047
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121436
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918419984
[INFO] [stdout] [Epoch 568]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121632
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918418047
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214372
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841996
[INFO] [stdout] [Epoch 569]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216315
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918418047
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214372
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841994
[INFO] [stdout] [Epoch 570]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121631
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918418036
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214383
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841992
[INFO] [stdout] [Epoch 571]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216304
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918418025
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214383
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918419907
[INFO] [stdout] [Epoch 572]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121631
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918418
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214372
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918419896
[INFO] [stdout] [Epoch 573]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216315
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841798
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214372
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918419896
[INFO] [stdout] [Epoch 574]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216315
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841797
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214372
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918419885
[INFO] [stdout] [Epoch 575]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121632
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841796
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121436
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918419873
[INFO] [stdout] [Epoch 576]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216326
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918417936
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121436
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841986
[INFO] [stdout] [Epoch 577]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216332
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918417914
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121436
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841985
[INFO] [stdout] [Epoch 578]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216332
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841789
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121436
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841984
[INFO] [stdout] [Epoch 579]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216337
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841788
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121435
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841983
[INFO] [stdout] [Epoch 580]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216343
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841787
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121435
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841982
[INFO] [stdout] [Epoch 581]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216343
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841785
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121435
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918419807
[INFO] [stdout] [Epoch 582]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216348
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918417825
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121434
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918419796
[INFO] [stdout] [Epoch 583]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216354
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918417814
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121435
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918419774
[INFO] [stdout] [Epoch 584]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216343
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918417803
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121436
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841976
[INFO] [stdout] [Epoch 585]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216337
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918417803
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121436
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918419735
[INFO] [stdout] [Epoch 586]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216332
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841779
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214372
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841971
[INFO] [stdout] [Epoch 587]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121632
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918417775
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214383
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841969
[INFO] [stdout] [Epoch 588]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216315
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918417775
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214394
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841967
[INFO] [stdout] [Epoch 589]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216304
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918417775
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214394
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918419646
[INFO] [stdout] [Epoch 590]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216304
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918417753
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214394
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918419635
[INFO] [stdout] [Epoch 591]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121631
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841774
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214394
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918419624
[INFO] [stdout] [Epoch 592]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121631
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841773
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214383
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841961
[INFO] [stdout] [Epoch 593]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216315
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841771
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214383
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184196
[INFO] [stdout] [Epoch 594]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121632
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918417686
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214383
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841959
[INFO] [stdout] [Epoch 595]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121632
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918417675
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214383
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841958
[INFO] [stdout] [Epoch 596]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216326
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918417664
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214372
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841957
[INFO] [stdout] [Epoch 597]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216326
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841764
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214372
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918419557
[INFO] [stdout] [Epoch 598]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216332
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841762
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214372
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918419546
[INFO] [stdout] [Epoch 599]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216332
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841761
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121436
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918419535
[INFO] [stdout] [Epoch 600]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216337
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184176
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121436
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918419524
[INFO] [stdout] [Epoch 601]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216343
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918417575
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121436
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841951
[INFO] [stdout] [Epoch 602]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216343
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918417553
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121436
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841951
[INFO] [stdout] [Epoch 603]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216348
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841754
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121435
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184195
[INFO] [stdout] [Epoch 604]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216348
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841753
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121435
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841949
[INFO] [stdout] [Epoch 605]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216354
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841751
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121436
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841946
[INFO] [stdout] [Epoch 606]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216343
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841751
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214372
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841944
[INFO] [stdout] [Epoch 607]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216337
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841749
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214372
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841942
[INFO] [stdout] [Epoch 608]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216326
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841748
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214383
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918419396
[INFO] [stdout] [Epoch 609]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121632
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841748
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214394
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918419374
[INFO] [stdout] [Epoch 610]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121631
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841748
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214406
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841935
[INFO] [stdout] [Epoch 611]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121631
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841746
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214406
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841934
[INFO] [stdout] [Epoch 612]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216315
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841745
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214394
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841933
[INFO] [stdout] [Epoch 613]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216315
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918417436
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214394
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841932
[INFO] [stdout] [Epoch 614]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121632
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918417414
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214394
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918419307
[INFO] [stdout] [Epoch 615]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121632
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841739
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214394
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918419296
[INFO] [stdout] [Epoch 616]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216326
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841738
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214383
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918419285
[INFO] [stdout] [Epoch 617]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216326
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841737
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214383
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918419274
[INFO] [stdout] [Epoch 618]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216326
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841735
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214383
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918419263
[INFO] [stdout] [Epoch 619]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216332
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918417337
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214383
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841925
[INFO] [stdout] [Epoch 620]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216332
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918417325
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214383
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841924
[INFO] [stdout] [Epoch 621]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216337
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918417303
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214372
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841923
[INFO] [stdout] [Epoch 622]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216337
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841728
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214372
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841922
[INFO] [stdout] [Epoch 623]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216343
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841727
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214372
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841921
[INFO] [stdout] [Epoch 624]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216343
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841726
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214372
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918419196
[INFO] [stdout] [Epoch 625]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216348
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918417237
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214383
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841917
[INFO] [stdout] [Epoch 626]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216337
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918417237
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214383
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918419146
[INFO] [stdout] [Epoch 627]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216332
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918417237
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214394
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918419124
[INFO] [stdout] [Epoch 628]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121632
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841722
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214406
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184191
[INFO] [stdout] [Epoch 629]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121631
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841721
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214417
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841908
[INFO] [stdout] [Epoch 630]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121631
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841721
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214417
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841907
[INFO] [stdout] [Epoch 631]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121631
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918417187
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214406
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841906
[INFO] [stdout] [Epoch 632]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216315
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918417176
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214406
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918419046
[INFO] [stdout] [Epoch 633]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216315
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918417164
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214406
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918419035
[INFO] [stdout] [Epoch 634]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216315
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841714
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214406
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918419024
[INFO] [stdout] [Epoch 635]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121632
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841712
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214406
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918419013
[INFO] [stdout] [Epoch 636]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121632
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841711
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214406
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918419
[INFO] [stdout] [Epoch 637]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216326
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184171
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214394
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841899
[INFO] [stdout] [Epoch 638]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216326
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918417076
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214394
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841898
[INFO] [stdout] [Epoch 639]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216326
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918417065
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214394
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841896
[INFO] [stdout] [Epoch 640]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216332
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918417053
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214394
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918418946
[INFO] [stdout] [Epoch 641]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216332
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841703
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214394
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918418935
[INFO] [stdout] [Epoch 642]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216332
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841702
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214394
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918418924
[INFO] [stdout] [Epoch 643]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216337
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841701
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214383
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918418913
[INFO] [stdout] [Epoch 644]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216337
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918416987
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214383
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918418896
[INFO] [stdout] [Epoch 645]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216343
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918416965
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214394
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918418874
[INFO] [stdout] [Epoch 646]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216332
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918416965
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214394
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918418863
[INFO] [stdout] [Epoch 647]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216332
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918416954
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214406
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841884
[INFO] [stdout] [Epoch 648]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216326
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918416937
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214417
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841882
[INFO] [stdout] [Epoch 649]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216315
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918416937
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214428
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918418797
[INFO] [stdout] [Epoch 650]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121631
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918416937
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214417
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918418785
[INFO] [stdout] [Epoch 651]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216315
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918416915
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214417
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918418774
[INFO] [stdout] [Epoch 652]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216315
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841689
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214417
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918418763
[INFO] [stdout] [Epoch 653]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216315
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841689
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214417
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841875
[INFO] [stdout] [Epoch 654]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216315
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841687
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214417
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841874
[INFO] [stdout] [Epoch 655]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121632
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841685
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214417
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841873
[INFO] [stdout] [Epoch 656]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121632
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841685
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214417
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841872
[INFO] [stdout] [Epoch 657]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121632
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918416826
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214417
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918418697
[INFO] [stdout] [Epoch 658]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216326
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918416804
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214406
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918418685
[INFO] [stdout] [Epoch 659]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216326
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918416804
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214406
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918418674
[INFO] [stdout] [Epoch 660]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216326
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841678
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214406
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918418663
[INFO] [stdout] [Epoch 661]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216326
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841676
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214406
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841865
[INFO] [stdout] [Epoch 662]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216332
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841676
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214406
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841864
[INFO] [stdout] [Epoch 663]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216332
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918416737
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214406
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841863
[INFO] [stdout] [Epoch 664]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216332
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918416726
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214406
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918418613
[INFO] [stdout] [Epoch 665]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216332
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918416715
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214406
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184186
[INFO] [stdout] [Epoch 666]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216337
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841669
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214406
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841859
[INFO] [stdout] [Epoch 667]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216337
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841668
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214417
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841857
[INFO] [stdout] [Epoch 668]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216326
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841667
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214417
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918418547
[INFO] [stdout] [Epoch 669]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216315
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841667
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214428
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918418513
[INFO] [stdout] [Epoch 670]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216315
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841667
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214428
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184185
[INFO] [stdout] [Epoch 671]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216315
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841664
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214428
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841849
[INFO] [stdout] [Epoch 672]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216315
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841663
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214428
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841848
[INFO] [stdout] [Epoch 673]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216315
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841662
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214428
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841847
[INFO] [stdout] [Epoch 674]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216315
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184166
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214428
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841846
[INFO] [stdout] [Epoch 675]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121632
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918416587
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214428
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918418447
[INFO] [stdout] [Epoch 676]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121632
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918416576
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214428
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918418436
[INFO] [stdout] [Epoch 677]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121632
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918416554
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214428
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918418425
[INFO] [stdout] [Epoch 678]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121632
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841654
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214428
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184184
[INFO] [stdout] [Epoch 679]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121632
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841653
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214428
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841839
[INFO] [stdout] [Epoch 680]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121632
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841651
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214428
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841838
[INFO] [stdout] [Epoch 681]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216326
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841651
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214428
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841837
[INFO] [stdout] [Epoch 682]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216326
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841649
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214417
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841836
[INFO] [stdout] [Epoch 683]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216326
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918416476
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214417
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841834
[INFO] [stdout] [Epoch 684]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216326
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918416465
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214417
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841833
[INFO] [stdout] [Epoch 685]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216326
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918416443
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214417
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841832
[INFO] [stdout] [Epoch 686]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216326
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841643
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214417
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918418297
[INFO] [stdout] [Epoch 687]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216326
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841642
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214417
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918418286
[INFO] [stdout] [Epoch 688]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216332
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184164
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214417
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918418275
[INFO] [stdout] [Epoch 689]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216332
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184164
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214417
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918418264
[INFO] [stdout] [Epoch 690]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216332
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841637
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214428
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841824
[INFO] [stdout] [Epoch 691]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121632
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841637
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121444
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841822
[INFO] [stdout] [Epoch 692]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216315
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841636
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121444
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841821
[INFO] [stdout] [Epoch 693]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216315
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841635
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121444
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918418186
[INFO] [stdout] [Epoch 694]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216315
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841634
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121444
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918418175
[INFO] [stdout] [Epoch 695]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216315
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918416326
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121444
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918418164
[INFO] [stdout] [Epoch 696]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216315
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918416304
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121444
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841815
[INFO] [stdout] [Epoch 697]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121632
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918416293
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121444
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841814
[INFO] [stdout] [Epoch 698]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121632
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841628
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121444
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841813
[INFO] [stdout] [Epoch 699]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121632
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841626
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121444
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841812
[INFO] [stdout] [Epoch 700]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121632
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841626
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121444
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918418097
[INFO] [stdout] [Epoch 701]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121632
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841624
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121444
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918418086
[INFO] [stdout] [Epoch 702]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121632
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918416226
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121444
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918418075
[INFO] [stdout] [Epoch 703]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121632
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918416215
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121444
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841806
[INFO] [stdout] [Epoch 704]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121632
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918416193
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121444
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918418047
[INFO] [stdout] [Epoch 705]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121632
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918416193
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121444
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918418036
[INFO] [stdout] [Epoch 706]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121632
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841617
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121444
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918418014
[INFO] [stdout] [Epoch 707]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121632
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841616
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121444
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918418
[INFO] [stdout] [Epoch 708]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121632
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841615
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121444
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841799
[INFO] [stdout] [Epoch 709]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121632
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841614
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121444
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841798
[INFO] [stdout] [Epoch 710]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121632
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918416126
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121444
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841797
[INFO] [stdout] [Epoch 711]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121632
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184161
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121445
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918417936
[INFO] [stdout] [Epoch 712]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216315
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184161
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121445
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918417925
[INFO] [stdout] [Epoch 713]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216315
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918416076
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121445
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918417914
[INFO] [stdout] [Epoch 714]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216315
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918416076
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121445
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918417903
[INFO] [stdout] [Epoch 715]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216315
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918416054
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121445
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841789
[INFO] [stdout] [Epoch 716]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216315
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918416043
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121445
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841788
[INFO] [stdout] [Epoch 717]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216315
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841603
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121445
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841786
[INFO] [stdout] [Epoch 718]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216315
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841602
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121445
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841785
[INFO] [stdout] [Epoch 719]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216315
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841601
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121445
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918417836
[INFO] [stdout] [Epoch 720]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216315
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841599
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121445
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918417825
[INFO] [stdout] [Epoch 721]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216315
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841599
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121445
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918417814
[INFO] [stdout] [Epoch 722]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216315
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918415965
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121445
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841779
[INFO] [stdout] [Epoch 723]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216315
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918415954
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121445
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918417775
[INFO] [stdout] [Epoch 724]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216315
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918415943
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121445
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918417764
[INFO] [stdout] [Epoch 725]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216315
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841593
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121445
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918417753
[INFO] [stdout] [Epoch 726]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216315
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841592
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121446
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841774
[INFO] [stdout] [Epoch 727]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216315
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184159
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121446
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841772
[INFO] [stdout] [Epoch 728]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216315
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184159
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121446
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841771
[INFO] [stdout] [Epoch 729]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216315
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918415877
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121446
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184177
[INFO] [stdout] [Epoch 730]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216315
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918415866
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121446
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918417686
[INFO] [stdout] [Epoch 731]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121631
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918415854
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121446
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918417664
[INFO] [stdout] [Epoch 732]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216315
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841583
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121446
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918417653
[INFO] [stdout] [Epoch 733]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216315
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841583
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121446
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841764
[INFO] [stdout] [Epoch 734]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216315
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918415804
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121446
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841763
[INFO] [stdout] [Epoch 735]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216315
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918415793
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121446
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841762
[INFO] [stdout] [Epoch 736]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216315
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841578
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121446
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184176
[INFO] [stdout] [Epoch 737]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216315
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841577
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121446
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918417586
[INFO] [stdout] [Epoch 738]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216315
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841576
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121446
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918417575
[INFO] [stdout] [Epoch 739]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216315
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841574
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121446
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918417564
[INFO] [stdout] [Epoch 740]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216315
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841574
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214472
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841754
[INFO] [stdout] [Epoch 741]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121631
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918415716
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214472
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841753
[INFO] [stdout] [Epoch 742]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121631
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918415716
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214472
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841752
[INFO] [stdout] [Epoch 743]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121631
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918415693
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214472
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841751
[INFO] [stdout] [Epoch 744]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121631
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918415693
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214472
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841748
[INFO] [stdout] [Epoch 745]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121631
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841567
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214472
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841747
[INFO] [stdout] [Epoch 746]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121631
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841566
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214472
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841746
[INFO] [stdout] [Epoch 747]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121631
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841565
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214472
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841745
[INFO] [stdout] [Epoch 748]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216304
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841564
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214472
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918417425
[INFO] [stdout] [Epoch 749]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216304
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918415627
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214472
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918417414
[INFO] [stdout] [Epoch 750]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216304
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918415616
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214483
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918417403
[INFO] [stdout] [Epoch 751]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216304
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918415605
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214483
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841739
[INFO] [stdout] [Epoch 752]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216304
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918415594
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214483
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841737
[INFO] [stdout] [Epoch 753]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216304
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841558
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214483
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841736
[INFO] [stdout] [Epoch 754]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216304
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841556
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214483
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841735
[INFO] [stdout] [Epoch 755]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216304
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841555
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214483
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918417337
[INFO] [stdout] [Epoch 756]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216304
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841553
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214483
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918417314
[INFO] [stdout] [Epoch 757]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216304
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841552
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214483
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918417303
[INFO] [stdout] [Epoch 758]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216298
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841551
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214494
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841729
[INFO] [stdout] [Epoch 759]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216298
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184155
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214494
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841727
[INFO] [stdout] [Epoch 760]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216298
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841549
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214494
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841726
[INFO] [stdout] [Epoch 761]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216298
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918415477
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214494
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841725
[INFO] [stdout] [Epoch 762]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216298
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918415466
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214494
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918417237
[INFO] [stdout] [Epoch 763]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216293
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918415455
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214494
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841721
[INFO] [stdout] [Epoch 764]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216293
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918415444
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214494
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184172
[INFO] [stdout] [Epoch 765]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216293
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841543
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214505
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918417187
[INFO] [stdout] [Epoch 766]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216293
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841542
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214505
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918417176
[INFO] [stdout] [Epoch 767]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216287
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841541
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214505
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918417153
[INFO] [stdout] [Epoch 768]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216287
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184154
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214505
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841714
[INFO] [stdout] [Epoch 769]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216287
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841539
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214505
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841713
[INFO] [stdout] [Epoch 770]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216287
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918415377
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214505
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841711
[INFO] [stdout] [Epoch 771]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216282
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918415366
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214505
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184171
[INFO] [stdout] [Epoch 772]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216287
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918415355
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214517
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918417087
[INFO] [stdout] [Epoch 773]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216287
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841533
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214517
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918417065
[INFO] [stdout] [Epoch 774]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216282
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841533
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214517
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918417053
[INFO] [stdout] [Epoch 775]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216282
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841531
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214517
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841704
[INFO] [stdout] [Epoch 776]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216282
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841531
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214517
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841703
[INFO] [stdout] [Epoch 777]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216282
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841529
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214517
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841701
[INFO] [stdout] [Epoch 778]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216276
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841529
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214528
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918417
[INFO] [stdout] [Epoch 779]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216276
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918415266
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214528
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918416987
[INFO] [stdout] [Epoch 780]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216276
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918415266
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214528
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918416965
[INFO] [stdout] [Epoch 781]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121627
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841524
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214528
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918416954
[INFO] [stdout] [Epoch 782]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121627
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841524
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214528
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918416937
[INFO] [stdout] [Epoch 783]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121627
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918415216
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121454
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918416915
[INFO] [stdout] [Epoch 784]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216265
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918415216
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121454
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918416904
[INFO] [stdout] [Epoch 785]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216265
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918415194
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121454
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841689
[INFO] [stdout] [Epoch 786]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216265
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918415194
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121454
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841687
[INFO] [stdout] [Epoch 787]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121626
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841517
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121454
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841686
[INFO] [stdout] [Epoch 788]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121626
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841517
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121455
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841685
[INFO] [stdout] [Epoch 789]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216254
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841516
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121455
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918416826
[INFO] [stdout] [Epoch 790]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216254
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841515
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121455
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918416815
[INFO] [stdout] [Epoch 791]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121626
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918415127
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121455
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918416804
[INFO] [stdout] [Epoch 792]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216254
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918415127
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121455
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841678
[INFO] [stdout] [Epoch 793]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216254
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918415105
[INFO] [stderr] error: test failed, to rerun pass `--lib`
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121456
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841677
[INFO] [stdout] [Epoch 794]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216254
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918415105
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121456
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841676
[INFO] [stdout] [Epoch 795]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216249
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918415083
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121456
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918416737
[INFO] [stdout] [Epoch 796]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216249
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918415083
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121456
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918416726
[INFO] [stdout] [Epoch 797]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216243
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841507
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121456
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918416715
[INFO] [stdout] [Epoch 798]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216243
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841506
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214572
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841669
[INFO] [stdout] [Epoch 799]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216243
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841505
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214572
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841668
[INFO] [stdout] [Epoch 800]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216237
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841504
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214572
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841667
[INFO] [stdout] [Epoch 801]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216237
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841503
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214572
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841664
[INFO] [stdout] [Epoch 802]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216232
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918415016
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214583
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841663
[INFO] [stdout] [Epoch 803]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216232
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918415016
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214583
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841661
[INFO] [stdout] [Epoch 804]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216232
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918414994
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214583
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184166
[INFO] [stdout] [Epoch 805]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216226
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918414994
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214583
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918416587
[INFO] [stdout] [Epoch 806]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216226
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918414966
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214594
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918416565
[INFO] [stdout] [Epoch 807]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121622
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918414966
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214594
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918416554
[INFO] [stdout] [Epoch 808]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121622
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918414955
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214594
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841654
[INFO] [stdout] [Epoch 809]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216215
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918414944
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214594
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841652
[INFO] [stdout] [Epoch 810]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121622
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841492
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214605
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841651
[INFO] [stdout] [Epoch 811]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216215
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841492
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214605
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184165
[INFO] [stdout] [Epoch 812]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216215
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841491
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214605
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918416476
[INFO] [stdout] [Epoch 813]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121621
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184149
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214605
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918416465
[INFO] [stdout] [Epoch 814]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121621
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841489
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214616
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918416443
[INFO] [stdout] [Epoch 815]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216204
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841488
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214616
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841643
[INFO] [stdout] [Epoch 816]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216204
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918414866
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214616
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841642
[INFO] [stdout] [Epoch 817]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216199
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918414855
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214616
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184164
[INFO] [stdout] [Epoch 818]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216199
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918414855
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214628
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841639
[INFO] [stdout] [Epoch 819]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216193
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918414833
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214628
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841636
[INFO] [stdout] [Epoch 820]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216193
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918414833
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214628
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841635
[INFO] [stdout] [Epoch 821]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216187
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841482
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121464
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841634
[INFO] [stdout] [Epoch 822]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216187
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841481
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121464
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918416315
[INFO] [stdout] [Epoch 823]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216182
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184148
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121464
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918416304
[INFO] [stdout] [Epoch 824]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216182
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841479
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121464
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918416293
[INFO] [stdout] [Epoch 825]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216176
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841479
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121465
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841627
[INFO] [stdout] [Epoch 826]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216176
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918414766
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121465
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841626
[INFO] [stdout] [Epoch 827]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121617
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918414766
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121465
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841624
[INFO] [stdout] [Epoch 828]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216176
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918414744
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121465
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841624
[INFO] [stdout] [Epoch 829]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216182
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841472
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121464
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841624
[INFO] [stdout] [Epoch 830]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216193
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841471
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214628
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918416226
[INFO] [stdout] [Epoch 831]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216199
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918414683
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214628
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918416215
[INFO] [stdout] [Epoch 832]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216199
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841467
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121464
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918416193
[INFO] [stdout] [Epoch 833]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216193
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841467
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121464
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841618
[INFO] [stdout] [Epoch 834]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216187
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841465
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121464
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841616
[INFO] [stdout] [Epoch 835]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216187
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841465
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121465
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841615
[INFO] [stdout] [Epoch 836]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216182
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841464
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121465
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841614
[INFO] [stdout] [Epoch 837]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216182
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841463
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121465
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918416115
[INFO] [stdout] [Epoch 838]
[WARN] too many lines in the log, truncating it
