[INFO] fetching crate easynn 0.1.7-beta...
[INFO] testing easynn-0.1.7-beta against try#8de4c7234dd9b97c9d76b58671343fdbbc9a433e+target=x86_64-unknown-linux-musl for musl_upgrade_1_2_5_with_getrandom_patch_0
[INFO] extracting crate easynn 0.1.7-beta into /workspace/builds/worker-3-tc1/source
[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-3-tc1/source/Cargo.toml
[INFO] validating manifest of crates.io crate easynn 0.1.7-beta on toolchain 8de4c7234dd9b97c9d76b58671343fdbbc9a433e
[INFO] running `Command { std: CARGO_HOME="/workspace/cargo-home" RUSTUP_HOME="/workspace/rustup-home" "/workspace/cargo-home/bin/cargo" "+8de4c7234dd9b97c9d76b58671343fdbbc9a433e" "metadata" "--manifest-path" "Cargo.toml" "--no-deps", kill_on_drop: false }`
[INFO] running `Command { std: CARGO_HOME="/workspace/cargo-home" RUSTUP_HOME="/workspace/rustup-home" "/workspace/cargo-home/bin/cargo" "+8de4c7234dd9b97c9d76b58671343fdbbc9a433e" "generate-lockfile" "--manifest-path" "Cargo.toml", kill_on_drop: false }`
[INFO] [stderr]     Blocking waiting for file lock on package cache
[INFO] [stderr]     Updating crates.io index
[INFO] [stderr]      Locking 28 packages to latest compatible versions
[INFO] [stderr]       Adding itertools v0.10.5 (available: v0.14.0)
[INFO] [stderr]       Adding rand v0.8.5 (available: v0.9.1)
[INFO] running `Command { std: CARGO_HOME="/workspace/cargo-home" RUSTUP_HOME="/workspace/rustup-home" "/workspace/cargo-home/bin/cargo" "+8de4c7234dd9b97c9d76b58671343fdbbc9a433e" "fetch" "--manifest-path" "Cargo.toml", kill_on_drop: false }`
[INFO] [stderr]     Blocking waiting for file lock on package cache
[INFO] running `Command { std: "docker" "create" "-v" "/var/lib/crater-agent-workspace/builds/worker-3-tc1/target:/opt/rustwide/target:rw,Z" "-v" "/var/lib/crater-agent-workspace/builds/worker-3-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@sha256:90999bfc7ae267e83380e433d8e61a7c072ca6729e92edbae886d3423b3a6f4c" "/opt/rustwide/cargo-home/bin/cargo" "+8de4c7234dd9b97c9d76b58671343fdbbc9a433e" "metadata" "--no-deps" "--format-version=1", kill_on_drop: false }`
[INFO] [stdout] 928191c650b518dfdf3f0a52edd89c86a1fc897fcbbc3e6d4a70e93c53d5f00b
[INFO] running `Command { std: "docker" "start" "-a" "928191c650b518dfdf3f0a52edd89c86a1fc897fcbbc3e6d4a70e93c53d5f00b", kill_on_drop: false }`
[INFO] running `Command { std: "docker" "inspect" "928191c650b518dfdf3f0a52edd89c86a1fc897fcbbc3e6d4a70e93c53d5f00b", kill_on_drop: false }`
[INFO] running `Command { std: "docker" "rm" "-f" "928191c650b518dfdf3f0a52edd89c86a1fc897fcbbc3e6d4a70e93c53d5f00b", kill_on_drop: false }`
[INFO] [stdout] 928191c650b518dfdf3f0a52edd89c86a1fc897fcbbc3e6d4a70e93c53d5f00b
[INFO] running `Command { std: "docker" "create" "-v" "/var/lib/crater-agent-workspace/builds/worker-3-tc1/target:/opt/rustwide/target:rw,Z" "-v" "/var/lib/crater-agent-workspace/builds/worker-3-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=forbid" "-e" "RUSTDOCFLAGS=--cap-lints=forbid" "-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@sha256:90999bfc7ae267e83380e433d8e61a7c072ca6729e92edbae886d3423b3a6f4c" "/opt/rustwide/cargo-home/bin/cargo" "+8de4c7234dd9b97c9d76b58671343fdbbc9a433e" "build" "--frozen" "--message-format=json" "--target" "x86_64-unknown-linux-musl", kill_on_drop: false }`
[INFO] [stdout] c262d021868ec00fd8c64b8a252082e38a21ae67a28caccea6441debaa4e81e5
[INFO] running `Command { std: "docker" "start" "-a" "c262d021868ec00fd8c64b8a252082e38a21ae67a28caccea6441debaa4e81e5", kill_on_drop: false }`
[INFO] [stderr]    Compiling crossbeam-utils v0.8.21
[INFO] [stderr]    Compiling rayon-core v1.12.1
[INFO] [stderr]    Compiling either v1.15.0
[INFO] [stderr]    Compiling num-traits v0.2.19
[INFO] [stderr]    Compiling getrandom v0.2.16
[INFO] [stderr]    Compiling ppv-lite86 v0.2.21
[INFO] [stderr]    Compiling rand_core v0.6.4
[INFO] [stderr]    Compiling itertools v0.10.5
[INFO] [stderr]    Compiling crossbeam-epoch v0.9.18
[INFO] [stderr]    Compiling crossbeam-channel v0.5.15
[INFO] [stderr]    Compiling crossbeam-queue v0.3.12
[INFO] [stderr]    Compiling rand_chacha v0.3.1
[INFO] [stderr]    Compiling rand v0.8.5
[INFO] [stderr]    Compiling crossbeam-deque v0.8.6
[INFO] [stderr]    Compiling crossbeam v0.8.4
[INFO] [stderr]    Compiling rayon v1.10.0
[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] [stderr]     Finished `dev` profile [unoptimized + debuginfo] target(s) in 6.93s
[INFO] running `Command { std: "docker" "inspect" "c262d021868ec00fd8c64b8a252082e38a21ae67a28caccea6441debaa4e81e5", kill_on_drop: false }`
[INFO] running `Command { std: "docker" "rm" "-f" "c262d021868ec00fd8c64b8a252082e38a21ae67a28caccea6441debaa4e81e5", kill_on_drop: false }`
[INFO] [stdout] c262d021868ec00fd8c64b8a252082e38a21ae67a28caccea6441debaa4e81e5
[INFO] running `Command { std: "docker" "create" "-v" "/var/lib/crater-agent-workspace/builds/worker-3-tc1/target:/opt/rustwide/target:rw,Z" "-v" "/var/lib/crater-agent-workspace/builds/worker-3-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=forbid" "-e" "RUSTDOCFLAGS=--cap-lints=forbid" "-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@sha256:90999bfc7ae267e83380e433d8e61a7c072ca6729e92edbae886d3423b3a6f4c" "/opt/rustwide/cargo-home/bin/cargo" "+8de4c7234dd9b97c9d76b58671343fdbbc9a433e" "test" "--frozen" "--no-run" "--message-format=json" "--target" "x86_64-unknown-linux-musl", kill_on_drop: false }`
[INFO] [stdout] dae15ae3e8d42f47b203c07e87c97ed855cb24947ce7db1f4375819a5f3285b6
[INFO] running `Command { std: "docker" "start" "-a" "dae15ae3e8d42f47b203c07e87c97ed855cb24947ce7db1f4375819a5f3285b6", kill_on_drop: false }`
[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: 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] [stderr]     Finished `test` profile [unoptimized + debuginfo] target(s) in 1.38s
[INFO] running `Command { std: "docker" "inspect" "dae15ae3e8d42f47b203c07e87c97ed855cb24947ce7db1f4375819a5f3285b6", kill_on_drop: false }`
[INFO] running `Command { std: "docker" "rm" "-f" "dae15ae3e8d42f47b203c07e87c97ed855cb24947ce7db1f4375819a5f3285b6", kill_on_drop: false }`
[INFO] [stdout] dae15ae3e8d42f47b203c07e87c97ed855cb24947ce7db1f4375819a5f3285b6
[INFO] running `Command { std: "docker" "create" "-v" "/var/lib/crater-agent-workspace/builds/worker-3-tc1/target:/opt/rustwide/target:rw,Z" "-v" "/var/lib/crater-agent-workspace/builds/worker-3-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=forbid" "-e" "RUSTDOCFLAGS=--cap-lints=forbid" "-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@sha256:90999bfc7ae267e83380e433d8e61a7c072ca6729e92edbae886d3423b3a6f4c" "/opt/rustwide/cargo-home/bin/cargo" "+8de4c7234dd9b97c9d76b58671343fdbbc9a433e" "test" "--frozen" "--target" "x86_64-unknown-linux-musl", kill_on_drop: false }`
[INFO] [stdout] 086b1c87f0cf87897b988f0ede44e926caddf81db5897898cb64f5dd89a257d3
[INFO] running `Command { std: "docker" "start" "-a" "086b1c87f0cf87897b988f0ede44e926caddf81db5897898cb64f5dd89a257d3", 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 2 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` profile [unoptimized + debuginfo] target(s) in 0.08s
[INFO] [stderr]      Running unittests src/lib.rs (/opt/rustwide/target/x86_64-unknown-linux-musl/debug/deps/easynn-0a71949ca8c08f0d)
[INFO] [stdout] 
[INFO] [stdout] running 7 tests
[INFO] [stdout] test layers::dense::test_dense_activate ... ok
[INFO] [stdout] test layers::dense::test_add_weight_delta_to ... ok
[INFO] [stdout] test layers::dense::test_dense_forward ... ok
[INFO] [stdout] test layers::dense::test_dense_descend ... 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): 0.00000000008909289543744957
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.9999814998351052
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.9603822324390692
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.0015688636776869428
[INFO] [stdout] [Epoch 1]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.0015067366760468186
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.925366325857055
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.8887218193509111
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.005796381889445223
[INFO] [stdout] [Epoch 2]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.0055668451666103244
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.8591082621963138
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.8250875750114282
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.012061212763235652
[INFO] [stdout] [Epoch 3]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.0115835887377864
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.800175798286849
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.7684888366730174
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.019852175498445097
[INFO] [stdout] [Epoch 4]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.01906602934866769
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.7476746194994163
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.718066704565764
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.02875045690850625
[INFO] [stdout] [Epoch 5]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.02761193881487596
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.7008284322697329
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.6730756263505397
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.03841457839573057
[INFO] [stdout] [Epoch 6]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.03689336109119171
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.6589625499179772
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.6328676329400502
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.04856770332198994
[INFO] [stdout] [Epoch 7]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.04664442227035713
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.6214898349961017
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.5968788375291958
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.05898692797814059
[INFO] [stdout] [Epoch 8]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.056651045630110745
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.5878986537074468
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.5646178670196691
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.06949425318646696
[INFO] [stdout] [Epoch 9]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.06674228076017467
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.557742548787706
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.535655943854833
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.07994897932283467
[INFO] [stdout] [Epoch 10]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.07678299974153027
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.5306313805365123
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.5096183778664575
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.09024130642787703
[INFO] [stdout] [Epoch 11]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.08666775069320189
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.5062237225673148
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.4861772631529016
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.10028695411787612
[INFO] [stdout] [Epoch 12]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.09631559073466671
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.48422033025503575
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.46504520517624154
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.11002264408076155
[INFO] [stdout] [Epoch 13]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.10566574737501237
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.46435852661917826
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.4459699289644101
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.11940231179480619
[INFO] [stdout] [Epoch 14]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.11467398024757208
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.4464073731764643
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.42872964119806767
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.1283939343698562
[INFO] [stdout] [Epoch 15]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.123309534568642
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.4301635127204701
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.4131290376161661
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.13697687862139316
[INFO] [stdout] [Epoch 16]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.13155259422781074
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.4154475875372662
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.3989958630702479
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.14513968810399774
[INFO] [stdout] [Epoch 17]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.13939215645489736
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.40210115067221935
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.3861779451050844
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.15287824024201044
[INFO] [stdout] [Epoch 18]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.14682426192823844
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.38998399988693566
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.37454063349092204
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.16019421523229213
[INFO] [stdout] [Epoch 19]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.15385052430889923
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.37897187419577705
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.3639645879771546
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.16709382733835437
[INFO] [stdout] [Epoch 20]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.16047691177555615
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.36895446161150186
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.3543438649312359
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.1735867767860305
[INFO] [stdout] [Epoch 21]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.16671274042509943
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.35983367418325873
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.34558426068516834
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.17968538691172006
[INFO] [stdout] [Epoch 22]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.1725698455898072
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.35152215276797355
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.3376018755179436
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.18540389667803148
[INFO] [stdout] [Epoch 23]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.17806190236936859
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.34394196940040794
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.33032186741174746
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.19075788330540822
[INFO] [stdout] [Epoch 24]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.1832038711262975
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.3370234997559442
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.32367736916521683
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.19576379369706925
[INFO] [stdout] [Epoch 25]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.18801154746644524
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.3307044421511059
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.3176085462415411
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.20043856666457724
[INFO] [stdout] [Epoch 26]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.19250119942443686
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.32492896290009904
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.31206177596888435
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.20479933078290083
[INFO] [stdout] [Epoch 27]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.19668927728367191
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.3196469507265454
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.3069889314774126
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2088631650937162
[INFO] [stdout] [Epoch 28]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2005921837557763
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.31481336539068994
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.3023467561208652
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.21264691189915672
[INFO] [stdout] [Epoch 29]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.20422609418771906
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.31038766779557314
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.29809631615052234
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.21616703260069273
[INFO] [stdout] [Epoch 30]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.20760681810947193
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.30633332063364016
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2942025211362086
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.21943949898642967
[INFO] [stdout] [Epoch 31]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.21074969482633168
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.30261735017288593
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.29063370310570635
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.22247971359486973
[INFO] [stdout] [Epoch 32]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2136695169362756
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.29920996109716586
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2873612466373905
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.22530245381808695
[INFO] [stdout] [Epoch 33]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.21638047664665166
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.29608419744127695
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2843592632222799
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.22792183528121499
[INFO] [stdout] [Epoch 34]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2188961306038383
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2932156436256285
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.28160430413773574
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.23035129077264613
[INFO] [stdout] [Epoch 35]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.22122937965780737
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.29058216042140705
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2790751068684058
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.23260356162123932
[INFO] [stdout] [Epoch 36]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.22339246058079495
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2881636513852697
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.27675237079010345
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.23469069894080402
[INFO] [stdout] [Epoch 37]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.22539694726250367
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2859418559099662
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2746185584156256
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.23662407260318258
[INFO] [stdout] [Epoch 38]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.22725375932785094
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.28390016555854763
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.27265771900212626
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.23841438617213448
[INFO] [stdout] [Epoch 39]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.22897317647947132
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.28202346079745977
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2708553317495806
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.24007169634174524
[INFO] [stdout] [Epoch 40]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2305648571663646
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.28029796562852216
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2691981661893356
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2416054356844058
[INFO] [stdout] [Epoch 41]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.23203786043105498
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.27871111795062276
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.26767415767948377
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.24302443773232024
[INFO] [stdout] [Epoch 42]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.23340066999787124
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2772514537667214
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2662722961972669
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.24433696359958873
[INFO] [stdout] [Epoch 43]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.23466121984079516
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.27590850359701513
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2649825268542835
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.24555072950477497
[INFO] [stdout] [Epoch 44]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.23582692061613542
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.27467269967060043
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2637956607633566
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.24667293468123505
[INFO] [stdout] [Epoch 45]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2369046864676071
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2735352926504282
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.262703295061185
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.24771028926837213
[INFO] [stdout] [Epoch 46]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.23790096181309306
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2724882768039884
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.26169774104226595
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.24866904186478903
[INFO] [stdout] [Epoch 47]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.23882174780669135
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2715243226684316
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2607719594904787
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.24955500649690743
[INFO] [stdout] [Epoch 48]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.23967262823937743
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.27063671637681364
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2599195024080103
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2503735888164486
[INFO] [stdout] [Epoch 49]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2404587946990644
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.269819304914375
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2591344604394856
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2511298113892761
[INFO] [stdout] [Epoch 50]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2411850708580076
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26906644666245044
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2584114153743384
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2518283379782311
[INFO] [stdout] [Epoch 51]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24185593579403966
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2683729666646469
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2577453971844491
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25247349675520603
[INFO] [stdout] [Epoch 52]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24247554628344606
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.267734116116932
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25713184511842474
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2530693024040501
[INFO] [stdout] [Epoch 53]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24304775802859568
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2671455356416213
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2565665724299374
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2536194770970174
[INFO] [stdout] [Epoch 54]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2435761458037212
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2666032219561482
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2560457343664098
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25412747034424127
[INFO] [stdout] [Epoch 55]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24406402251835485
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26610349759193497
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25556579908702026
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2545964777288877
[INFO] [stdout] [Epoch 56]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24451445721056916
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26564298335755393
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25512352121632154
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2550294585508246
[INFO] [stdout] [Epoch 57]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24493029199195712
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2652185732744059
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25471591777246694
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25542915240937336
[INFO] [stdout] [Epoch 58]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24531415797370726
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2648274117430109
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25434024623771584
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2557980947614222
[INFO] [stdout] [Epoch 59]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24566849020861486
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.264466872724236
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25399398456408506
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2561386314952379
[INFO] [stdout] [Epoch 60]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2459955416877714
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.264134540742876
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2536748129291876
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25645293256303403
[INFO] [stdout] [Epoch 61]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24629739643328272
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2638281935413493
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25338059707684196
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2567430047169911
[INFO] [stdout] [Epoch 62]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.246575981729943
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2635457862292306
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2531093730942836
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25701070339418336
[INFO] [stdout] [Epoch 63]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24683307953951844
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2632854367902314
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25285933349306927
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2572577437959491
[INFO] [stdout] [Epoch 64]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24707033714137425
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26304541282229865
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25262881447426716
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2574857112067631
[INFO] [stdout] [Epoch 65]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2472892770427199
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2628241193989879
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2524162842705199
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25769607059678584
[INFO] [stdout] [Epoch 66]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24749130620089774
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2626200879513493
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25222033246820813
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25789017555105176
[INFO] [stdout] [Epoch 67]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24767772459897475
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2624319660794386
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2520396602224254
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2580692765668088
[INFO] [stdout] [Epoch 68]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24784973321450776
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2622585082113578
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.251873071285921
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25823452875890746
[INFO] [stdout] [Epoch 69]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24800844141979927
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26209856703558826
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2517194637807122
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2583869990114051
[INFO] [stdout] [Epoch 70]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24815487385029805
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2619510856393989
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25157782264781237
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25852767261175097
[INFO] [stdout] [Epoch 71]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2482899767760703
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26181509029240513
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2514472127165598
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2586574594020834
[INFO] [stdout] [Epoch 72]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2484146240095056
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26168968381999375
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2513267723404564
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25877719948032957
[INFO] [stdout] [Epoch 73]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24852962238065324
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2615740395164002
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2512157075512853
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25888766848197897
[INFO] [stdout] [Epoch 74]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2486357168098374
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2614673955517759
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2511132866876604
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2589895824716111
[INFO] [stdout] [Epoch 75]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24873359500548015
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.261369049831701
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25101883545810083
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2590836024715205
[INFO] [stdout] [Epoch 76]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24882389181339318
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2612783552712889
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25093173240228134
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25917033865309935
[INFO] [stdout] [Epoch 77]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24890719324218155
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26119471544938233
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2508514047173223
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2592503542150204
[INFO] [stdout] [Epoch 78]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24898404018785056
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2611175806113612
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25077732441888706
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25932416897071653
[INFO] [stdout] [Epoch 79]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2490549318792212
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26104644399182275
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25070900480948255
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25939226266617765
[INFO] [stdout] [Epoch 80]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2491203290643421
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26098083843087366
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2506459972287472
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25945507804768364
[INFO] [stdout] [Epoch 81]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2491806569567406
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2609203332600256
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2505878880626649
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2595130236977687
[INFO] [stdout] [Epoch 82]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24923630795908225
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2608645314357343
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25053429599061583
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25956647665645366
[INFO] [stdout] [Epoch 83]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24928764418060342
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26081306690047545
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25048486945095333
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25961578484361
[INFO] [stdout] [Epoch 84]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2493349997635484
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26076560215294714
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2504392843074273
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25966126929720007
[INFO] [stdout] [Epoch 85]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2493786830327764
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2607218260105282
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2503972417002483
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25970322624110476
[INFO] [stdout] [Epoch 86]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24941897848170258
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26068145154852546
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25035846606694107
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25974192899526594
[INFO] [stdout] [Epoch 87]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2494561486067991
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26064421420202494
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25032270331936224
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25977762973996216
[INFO] [stdout] [Epoch 88]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2494904356020054
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26060987001733393
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2502897191643849
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25981056114517953
[INFO] [stdout] [Epoch 89]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24952206292357618
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2605781940410642
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2502592975567756
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2598409378752437
[INFO] [stdout] [Epoch 90]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2495512367351299
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2605489788358916
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2502312392737281
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2598689579781353
[INFO] [stdout] [Epoch 91]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24957814724194707
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2605220331129142
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2502053606013808
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25989480416821775
[INFO] [stdout] [Epoch 92]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24960296992290235
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26049718047135206
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2501814921244246
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2599186450104639
[INFO] [stdout] [Epoch 93]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24962586666779568
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2604742582370822
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.250159477610632
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25994063601366735
[INFO] [stdout] [Epoch 94]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24964698682727235
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2604531163921866
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25013917298279437
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25996092063956483
[INFO] [stdout] [Epoch 95]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24966646818198449
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26043361658832165
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25012044537116274
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25997963123428547
[INFO] [stdout] [Epoch 96]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24968443783715413
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26041563123729516
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25010317224003686
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25999688988805264
[INFO] [stdout] [Epoch 97]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24970101304823228
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26039904267276226
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2500872405826597
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2600128092286284
[INFO] [stdout] [Epoch 98]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2497163019829213
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2603837423774478
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2500725461790398
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26002749315356904
[INFO] [stdout] [Epoch 99]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24973040442443445
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26036963027073456
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25005899291175254
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26004103750598345
[INFO] [stdout] [Epoch 100]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24974341242049336
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26035661405188076
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2500464921351655
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26005353069812637
[INFO] [stdout] [Epoch 101]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24975541088222747
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26034460859449676
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25003496209389414
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26006505428683313
[INFO] [stdout] [Epoch 102]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2497664781368215
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26033353538826254
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25002432738662683
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260075683504497
[INFO] [stdout] [Epoch 103]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24977668643746598
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26032332202418274
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25001451847176465
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2600854877490075
[INFO] [stdout] [Epoch 104]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24978610243389401
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26031390171997104
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2500054712115999
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2600945310358098
[INFO] [stdout] [Epoch 105]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24979478760653906
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26030521288242303
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24999712645201883
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26010287241500096
[INFO] [stdout] [Epoch 106]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498027986671143
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26029719870388374
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24998942963494988
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601105663561593
[INFO] [stdout] [Epoch 107]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498101879282028
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602898067901466
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24998233044099694
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601176631033937
[INFO] [stdout] [Epoch 108]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24981700364424683
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602829888173273
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24997578245990132
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26012420900291156
[INFO] [stdout] [Epoch 109]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24982329032614392
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602767002154493
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24996974288665785
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601302468052257
[INFO] [stdout] [Epoch 110]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24982908903148646
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260270899876658
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24996417224128267
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601358159439597
[INFO] [stdout] [Epoch 111]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24983443763232674
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602655498861397
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24995903411038917
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26014095279305854
[INFO] [stdout] [Epoch 112]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24983937106220147
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26026061527397854
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499542949088697
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601456909040745
[INFO] [stdout] [Epoch 113]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24984392154402127
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26025606378631305
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499499236601158
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26015006122506845
[INFO] [stdout] [Epoch 114]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24984811880030394
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26025186567429404
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24994589179333276
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26015409230254744
[INFO] [stdout] [Epoch 115]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498519902471148
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602479934994542
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24994217295661675
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26015781046775277
[INFO] [stdout] [Epoch 116]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498555611729781
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26024442195421094
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499387428445652
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601612400085079
[INFO] [stdout] [Epoch 117]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24985885490391943
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260241127696324
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24993557903929076
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26016440332774443
[INFO] [stdout] [Epoch 118]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24986189295571434
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602380891962232
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499326608637939
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26016732108973817
[INFO] [stdout] [Epoch 119]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24986469517433318
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26023528659620215
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24992996924673397
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260170012355005
[INFO] [stdout] [Epoch 120]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24986727986549556
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260232701580557
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24992748659770844
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26017249470473786
[INFO] [stdout] [Epoch 121]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24986966391417914
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602303172558155
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499251966922268
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26017478435559244
[INFO] [stdout] [Epoch 122]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24987186289486002
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602281180402747
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24992308456562148
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601768962655705
[INFO] [stdout] [Epoch 123]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498738911732029
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602260895621216
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24992113641520344
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26017884423168997
[INFO] [stdout] [Epoch 124]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24987576199986425
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602242185654691
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24991933951001838
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601806409800804
[INFO] [stdout] [Epoch 125]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24987748759701855
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602224928236926
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24991768210761633
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26018229824908756
[INFO] [stdout] [Epoch 126]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24987907923817312
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602209010594987
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499161533772848
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601838268659318
[INFO] [stdout] [Epoch 127]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498805473217903
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26021943287120436
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24991474332924696
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26018523681741873
[INFO] [stdout] [Epoch 128]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498819014391985
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26021807866474056
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24991344274935917
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26018653731516445
[INFO] [stdout] [Epoch 129]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24988315043723358
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602168295909404
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499122431388816
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601877368557586
[INFO] [stdout] [Epoch 130]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24988430247602034
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26021567748769536
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24991113665892525
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26018884327626074
[INFO] [stdout] [Epoch 131]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498853650822707
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26021461482660635
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24991011607921546
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260189863805389
[INFO] [stdout] [Epoch 132]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498863451984456
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602136346637766
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990917473083385
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601908051107378
[INFO] [stdout] [Epoch 133]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24988724922810268
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26021273059442757
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990830646263112
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601916733423299
[INFO] [stdout] [Epoch 134]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24988808307772384
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602118967110377
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499075056010237
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601924741727902
[INFO] [stdout] [Epoch 135]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498888521952979
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602111275647345
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990676691291416
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601932128344007
[INFO] [stdout] [Epoch 136]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24988956160590892
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602104181296818
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990608557148963
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26019389415328104
[INFO] [stdout] [Epoch 137]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989021594456162
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260209763770235
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990545712467702
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26019452258091386
[INFO] [stdout] [Epoch 138]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989081948646036
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020916021064544
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990487746604736
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26019510222322606
[INFO] [stdout] [Epoch 139]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249891376174937
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020860350711794
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990434280797963
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601956368674115
[INFO] [stdout] [Epoch 140]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989188964721276
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602080900220376
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990384965690865
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26019613000667186
[INFO] [stdout] [Epoch 141]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989236325815864
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020761640019807
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990339479049398
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601965848630385
[INFO] [stdout] [Epoch 142]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989280010221315
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602071795468755
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499029752365631
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601970044084208
[INFO] [stdout] [Epoch 143]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989320303359847
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020677660760533
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499025882536881
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26019739138402304
[INFO] [stdout] [Epoch 144]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989357468496698
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602064049495286
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990223131327138
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601977483182523
[INFO] [stdout] [Epoch 145]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989391748460085
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602060621441876
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990190208302207
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601980775432377
[INFO] [stdout] [Epoch 146]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498942336722769
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602057459516562
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990159841171491
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601983812100663
[INFO] [stdout] [Epoch 147]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989452531389925
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602054543059031
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499013183151337
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601986613028374
[INFO] [stdout] [Epoch 148]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498947943149967
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602051853012913
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499010599631047
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601989196516248
[INFO] [stdout] [Epoch 149]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989504243317226
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602049371801259
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499008216675375
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601991579444343
[INFO] [stdout] [Epoch 150]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989527128958652
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020470832116777
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990060187139426
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601993777382312
[INFO] [stdout] [Epoch 151]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989548237954926
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260204497229041
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990039913851575
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601995804691137
[INFO] [stdout] [Epoch 152]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498956770822888
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020430252446036
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990021214423644
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26019976746169465
[INFO] [stdout] [Epoch 153]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989585666996372
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260204122935219
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990003966672936
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260199939937757
[INFO] [stdout] [Epoch 154]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989602231597405
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260203957287876
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989988057902127
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602000990242359
[INFO] [stdout] [Epoch 155]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989617510262854
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020380450008773
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989973384162945
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602002457605819
[INFO] [stdout] [Epoch 156]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989631602821533
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020366357353625
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498995984957696
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020038110555205
[INFO] [stdout] [Epoch 157]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989644601352473
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020353358740633
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989947365709042
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602005059434744
[INFO] [stdout] [Epoch 158]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989656590786538
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602034136923673
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989935850989528
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020062109002545
[INFO] [stdout] [Epoch 159]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498966764946132
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020330310502554
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989925230181215
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602007272975605
[INFO] [stdout] [Epoch 160]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989677849633005
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020320110280337
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989915433887805
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602008252600285
[INFO] [stdout] [Epoch 161]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498968725794843
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020310701921906
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498990639810039
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602009156175061
[INFO] [stdout] [Epoch 162]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989695935880596
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602030202395316
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989898063779217
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602009989603804
[INFO] [stdout] [Epoch 163]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989703940130256
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602029401967238
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989890376467958
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020107583320584
[INFO] [stdout] [Epoch 164]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989711322996405
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602028663677973
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989883285937878
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602011467382625
[INFO] [stdout] [Epoch 165]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989718132718064
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020279827035553
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498987674585958
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602012121388376
[INFO] [stdout] [Epoch 166]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989724413789313
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020273545945133
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989870713500345
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602012724622532
[INFO] [stdout] [Epoch 167]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498973020725015
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020267752467996
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989865149444915
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020132810265706
[INFO] [stdout] [Epoch 168]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989735550954545
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020262408749717
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989860017337884
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020137942359933
[INFO] [stdout] [Epoch 169]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989740479817854
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020257479874603
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989855283646242
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602014267604068
[INFO] [stdout] [Epoch 170]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989745026044857
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602025293363755
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989850917440173
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602014704223746
[INFO] [stdout] [Epoch 171]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989749219340254
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020248740333596
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989846890191073
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602015106947869
[INFO] [stdout] [Epoch 172]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989753087102742
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020244872563847
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989843175585005
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602015478407804
[INFO] [stdout] [Epoch 173]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498975665460397
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020241305056424
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498983974935089
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602015821030645
[INFO] [stdout] [Epoch 174]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498975994515374
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020238014501385
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989836589101838
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602016137055064
[INFO] [stdout] [Epoch 175]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989762980252267
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020234979398377
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989833674188922
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020164285459424
[INFO] [stdout] [Epoch 176]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989765779730677
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602023217991615
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989830985566197
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602016697407863
[INFO] [stdout] [Epoch 177]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498976836188057
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020229597762995
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989828505666326
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020169453975495
[INFO] [stdout] [Epoch 178]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498977074357353
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602022721606729
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989826218285774
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602017174135349
[INFO] [stdout] [Epoch 179]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989772940371377
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602022501926708
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498982410847887
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020173851158224
[INFO] [stdout] [Epoch 180]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989774966627853
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020222993008607
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989822162460237
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602017579717501
[INFO] [stdout] [Epoch 181]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498977683558238
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602022112405237
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989820367514687
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602017759211899
[INFO] [stdout] [Epoch 182]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989778559446593
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260202194001867
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498981871191411
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602017924771823
[INFO] [stdout] [Epoch 183]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989780149484106
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602021781014795
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989817184840896
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201807747903
[INFO] [stdout] [Epoch 184]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989781616084136
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020216343546865
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989815776317234
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020182183312984
[INFO] [stdout] [Epoch 185]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989782968829333
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602021499080076
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989814477139888
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020183482489506
[INFO] [stdout] [Epoch 186]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989784216558475
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020213743070864
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989813278820094
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602018468080859
[INFO] [stdout] [Epoch 187]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498978536742413
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020212592204545
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989812173528095
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602018578609998
[INFO] [stdout] [Epoch 188]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989786428945995
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020211530682125
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989811154041977
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201868055856
[INFO] [stdout] [Epoch 189]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989787408059988
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602021055156766
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989810213700459
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020187745926676
[INFO] [stdout] [Epoch 190]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498978831116357
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020964846367
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498980934635939
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020188613267364
[INFO] [stdout] [Epoch 191]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498978914415757
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020881546932
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989808546351636
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201894132748
[INFO] [stdout] [Epoch 192]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498978991248472
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020208047141874
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989807808449951
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020190151176203
[INFO] [stdout] [Epoch 193]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989790621165242
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020207338461104
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989807127832964
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019083179296
[INFO] [stdout] [Epoch 194]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989791274829581
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020668479655
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989806500053524
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020191459572195
[INFO] [stdout] [Epoch 195]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979187774878
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020608187716
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498980592100976
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020192038615786
[INFO] [stdout] [Epoch 196]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989792433862246
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020205525763535
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989805386918248
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020192572707157
[INFO] [stdout] [Epoch 197]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979294680361
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020501282204
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989804894289236
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019306533604
[INFO] [stdout] [Epoch 198]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989793419924392
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020204539701136
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498980443990393
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019351972123
[INFO] [stdout] [Epoch 199]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989793856315937
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020410330948
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989804020793405
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019393883166
[INFO] [stdout] [Epoch 200]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979425882962
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020203700795713
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989803634219185
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201943254058
[INFO] [stdout] [Epoch 201]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979463009543
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020332952982
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989803277655434
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019468196948
[INFO] [stdout] [Epoch 202]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989794972539192
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020202987085983
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989802948772394
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019501085247
[INFO] [stdout] [Epoch 203]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989795288398417
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020202671226705
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989802645421139
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019531420365
[INFO] [stdout] [Epoch 204]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989795579736915
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020202379888163
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498980236561962
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020195594005135
[INFO] [stdout] [Epoch 205]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989795848458268
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020202111166757
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498980210753959
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020195852085115
[INFO] [stdout] [Epoch 206]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989796096318284
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260202018633067
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989801869494813
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020196090129855
[INFO] [stdout] [Epoch 207]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979632493647
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020201634688483
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498980164992988
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019630969476
[INFO] [stdout] [Epoch 208]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979653580661
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020142381831
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989801447410176
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019651221444
[INFO] [stdout] [Epoch 209]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989796730306513
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020201229318374
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989801260612454
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020196699012127
[INFO] [stdout] [Epoch 210]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979690970703
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020201049917835
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989801088316183
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019687130838
[INFO] [stdout] [Epoch 211]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989797075180356
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020088444449
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498980092939558
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019703022895
[INFO] [stdout] [Epoch 212]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989797227807695
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020200731817134
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498980078281228
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019717681224
[INFO] [stdout] [Epoch 213]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989797368586292
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020200591038517
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989800647608507
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019731201598
[INFO] [stdout] [Epoch 214]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989797498435978
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020200461188814
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989800522900862
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019743672361
[INFO] [stdout] [Epoch 215]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989797618205187
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020200341419586
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.249898004078745
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019755174996
[INFO] [stdout] [Epoch 216]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989797728676497
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020023094826
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989800301777845
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020197657846594
[INFO] [stdout] [Epoch 217]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979783057172
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020012905302
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989800203917678
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019775570676
[INFO] [stdout] [Epoch 218]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989797924556628
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020200035068103
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498980011365456
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019784596985
[INFO] [stdout] [Epoch 219]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798011245318
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019994837939
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989800030398748
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019792922564
[INFO] [stdout] [Epoch 220]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979809120419
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199868420496
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799953606237
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019800601815
[INFO] [stdout] [Epoch 221]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798164955726
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199794668963
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979988277526
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198076849105
[INFO] [stdout] [Epoch 222]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798232981786
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199726642884
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979981744303
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198142181333
[INFO] [stdout] [Epoch 223]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979829572686
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019966389779
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799757182662
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019820244169
[INFO] [stdout] [Epoch 224]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798353600925
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019960602373
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979970160041
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019825802393
[INFO] [stdout] [Epoch 225]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979840698211
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199552642526
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799650333122
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201983092912
[INFO] [stdout] [Epoch 226]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798456219212
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199503405417
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799603045806
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019835657851
[INFO] [stdout] [Epoch 227]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798501633959
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199457990656
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799559429493
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198400194827
[INFO] [stdout] [Epoch 228]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979854352307
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019941610154
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979951919919
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019844042511
[INFO] [stdout] [Epoch 229]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798582160244
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199377464354
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979948209203
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198477532247
[INFO] [stdout] [Epoch 230]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798617797954
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199341826633
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799447865585
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019851175869
[INFO] [stdout] [Epoch 231]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798650669034
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019930895554
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.249897994162962
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019854332807
[INFO] [stdout] [Epoch 232]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798680988276
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199278636286
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799387177605
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019857244665
[INFO] [stdout] [Epoch 233]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798708953778
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019925067078
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799360319528
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198599304717
[INFO] [stdout] [Epoch 234]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798734748275
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199224876267
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799335546495
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198624077745
[INFO] [stdout] [Epoch 235]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979875854029
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199201084243
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979931269665
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019864692758
[INFO] [stdout] [Epoch 236]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979878048528
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199179139236
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979929162067
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019866800354
[INFO] [stdout] [Epoch 237]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798800726656
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019915889786
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799272180857
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019868744334
[INFO] [stdout] [Epoch 238]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798819396647
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199140227857
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799254250195
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198705374
[INFO] [stdout] [Epoch 239]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979883661725
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199123007237
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799237711524
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198721912663
[INFO] [stdout] [Epoch 240]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798852500994
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019910712348
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799222456777
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201987371674
[INFO] [stdout] [Epoch 241]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979886715166
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199092472807
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799208386282
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019875123788
[INFO] [stdout] [Epoch 242]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979888066495
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199078959505
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799195408116
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198764216035
[INFO] [stdout] [Epoch 243]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798893129192
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199066495264
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979918343747
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198776186665
[INFO] [stdout] [Epoch 244]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798904625796
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199054998655
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799172396124
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198787228005
[INFO] [stdout] [Epoch 245]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798915229905
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019904439452
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979916221194
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019879741219
[INFO] [stdout] [Epoch 246]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798925010798
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199034613617
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979915281836
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019880680576
[INFO] [stdout] [Epoch 247]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798934032395
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199025592017
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799144154026
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198815470075
[INFO] [stdout] [Epoch 248]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979894235361
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019901727078
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979913616233
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019882346177
[INFO] [stdout] [Epoch 249]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798950028844
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019900959554
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799128791043
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019883083305
[INFO] [stdout] [Epoch 250]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798957108234
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019900251614
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799121992004
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198837632075
[INFO] [stdout] [Epoch 251]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798963638027
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019899598635
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979911572079
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019884390328
[INFO] [stdout] [Epoch 252]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249897989696609
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019898996346
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979910993641
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198849687654
[INFO] [stdout] [Epoch 253]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798975216224
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019898440812
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979910460108
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019885502297
[INFO] [stdout] [Epoch 254]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798980340272
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019897928407
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799099679952
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019885994408
[INFO] [stdout] [Epoch 255]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798985066516
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198974557823
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799095140866
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019886448316
[INFO] [stdout] [Epoch 256]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979898942586
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198970198455
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979909095416
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198868669864
[INFO] [stdout] [Epoch 257]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798993446777
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198966177527
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979908709246
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019887253154
[INFO] [stdout] [Epoch 258]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979899715555
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019896246875
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979908353056
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019887609343
[INFO] [stdout] [Epoch 259]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799000576396
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198959047897
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799080245179
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198879378803
[INFO] [stdout] [Epoch 260]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799003731678
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989558926
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799077214844
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019888240914
[INFO] [stdout] [Epoch 261]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799006642022
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198952982254
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799074419752
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198885204215
[INFO] [stdout] [Epoch 262]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799009326416
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019895029784
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979907184165
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198887782303
[INFO] [stdout] [Epoch 263]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979901180243
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019894782182
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799069463706
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198890160257
[INFO] [stdout] [Epoch 264]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979901408622
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198945538026
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979906727034
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019889235361
[INFO] [stdout] [Epoch 265]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799016192726
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198943431505
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799065247262
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198894376656
[INFO] [stdout] [Epoch 266]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799018135675
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019894148856
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799063381257
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198896242663
[INFO] [stdout] [Epoch 267]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799019927791
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019893969642
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799061660098
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198897963814
[INFO] [stdout] [Epoch 268]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249897990215808
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019893804341
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979906007256
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019889955135
[INFO] [stdout] [Epoch 269]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799023105475
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019893651872
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979905860826
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019890101563
[INFO] [stdout] [Epoch 270]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799024511783
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989351124
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799057257642
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198902366243
[INFO] [stdout] [Epoch 271]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979902580892
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198933815253
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799056011858
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198903612013
[INFO] [stdout] [Epoch 272]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799027005363
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989326188
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799054862813
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019890476106
[INFO] [stdout] [Epoch 273]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799028108917
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198931515237
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799053802944
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989058209
[INFO] [stdout] [Epoch 274]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979902912681
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198930497324
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799052825368
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019890679847
[INFO] [stdout] [Epoch 275]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799030065668
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198929558475
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799051923686
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019890770015
[INFO] [stdout] [Epoch 276]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799030931648
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198928692473
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799051092
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019890853182
[INFO] [stdout] [Epoch 277]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799031730403
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892789372
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979905032488
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019890929893
[INFO] [stdout] [Epoch 278]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979903246714
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892715696
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799049617323
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198910006487
[INFO] [stdout] [Epoch 279]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799033146678
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198926477417
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799048964678
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198910659104
[INFO] [stdout] [Epoch 280]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799033773472
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892585061
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799048362718
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891126108
[INFO] [stdout] [Epoch 281]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979903435161
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892527247
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799047807473
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891181629
[INFO] [stdout] [Epoch 282]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799034884858
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892473921
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799047295344
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891232842
[INFO] [stdout] [Epoch 283]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799035376706
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198924247356
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799046822975
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198912800774
[INFO] [stdout] [Epoch 284]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799035830373
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892379367
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799046387268
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198913236475
[INFO] [stdout] [Epoch 285]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979903624882
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198923375215
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799045985392
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198913638337
[INFO] [stdout] [Epoch 286]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799036634788
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892298924
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904561471
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891400902
[INFO] [stdout] [Epoch 287]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799036990798
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198922633225
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799045272798
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891435091
[INFO] [stdout] [Epoch 288]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799037319158
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892230485
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799044957448
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891466626
[INFO] [stdout] [Epoch 289]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979903762203
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892200197
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799044666572
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198914957127
[INFO] [stdout] [Epoch 290]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799037901395
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989217226
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799044398264
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891522542
[INFO] [stdout] [Epoch 291]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799038159058
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892146492
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.249897990441508
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198915472875
[INFO] [stdout] [Epoch 292]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799038396734
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198921227233
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799043922542
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891570113
[INFO] [stdout] [Epoch 293]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799038615954
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892100801
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799043712
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891591166
[INFO] [stdout] [Epoch 294]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799038818156
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198920805787
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799043517805
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198916105847
[INFO] [stdout] [Epoch 295]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799039004665
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198920619275
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799043338684
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198916284953
[INFO] [stdout] [Epoch 296]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799039176686
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198920447235
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799043173472
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891645016
[INFO] [stdout] [Epoch 297]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979903933537
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892028855
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904302107
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891660255
[INFO] [stdout] [Epoch 298]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799039481725
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892014219
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799042880523
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198916743087
[INFO] [stdout] [Epoch 299]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799039616706
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892000719
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799042750885
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198916872705
[INFO] [stdout] [Epoch 300]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799039741215
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891988268
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799042631305
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198916992293
[INFO] [stdout] [Epoch 301]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979903985606
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891976783
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799042521016
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198917102566
[INFO] [stdout] [Epoch 302]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799039961985
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891966188
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799042419286
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891720429
[INFO] [stdout] [Epoch 303]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040059693
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198919564164
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799042325447
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891729813
[INFO] [stdout] [Epoch 304]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040149813
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891947404
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799042238878
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891738468
[INFO] [stdout] [Epoch 305]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040232946
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891939089
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799042159047
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198917464493
[INFO] [stdout] [Epoch 306]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040309615
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198919314214
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904208541
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891753812
[INFO] [stdout] [Epoch 307]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904038034
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891924349
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.249897990420175
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891760602
[INFO] [stdout] [Epoch 308]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040445562
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891917826
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041954852
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891766867
[INFO] [stdout] [Epoch 309]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040505728
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891911807
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041897076
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198917726434
[INFO] [stdout] [Epoch 310]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040561217
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891906257
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041843785
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198917779713
[INFO] [stdout] [Epoch 311]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040612393
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891901139
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041794625
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891782886
[INFO] [stdout] [Epoch 312]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040659608
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918964166
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904174928
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891787419
[INFO] [stdout] [Epoch 313]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040703153
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918920606
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041707467
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198917915993
[INFO] [stdout] [Epoch 314]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040743313
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918880444
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041668895
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891795457
[INFO] [stdout] [Epoch 315]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040780364
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918843385
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041633307
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198917990145
[INFO] [stdout] [Epoch 316]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040814542
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989188092
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041600486
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918022946
[INFO] [stdout] [Epoch 317]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040846053
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918777665
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041570226
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989180532
[INFO] [stdout] [Epoch 318]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040875124
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989187486
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904154231
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891808112
[INFO] [stdout] [Epoch 319]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040901942
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891872176
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041516547
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891810686
[INFO] [stdout] [Epoch 320]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040926677
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891869702
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041492794
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918130605
[INFO] [stdout] [Epoch 321]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904094949
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989186742
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041470892
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989181525
[INFO] [stdout] [Epoch 322]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040970528
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918653153
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904145068
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918172693
[INFO] [stdout] [Epoch 323]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040989932
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891863373
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041432043
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891819133
[INFO] [stdout] [Epoch 324]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041007835
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891861582
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041414848
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891820851
[INFO] [stdout] [Epoch 325]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041024346
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891859931
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904139901
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918224346
[INFO] [stdout] [Epoch 326]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904103956
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918584075
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904138438
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891823897
[INFO] [stdout] [Epoch 327]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041053617
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918570003
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041370883
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891825246
[INFO] [stdout] [Epoch 328]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041066582
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918557047
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904135844
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891826488
[INFO] [stdout] [Epoch 329]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904107852
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891854509
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041346972
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918276355
[INFO] [stdout] [Epoch 330]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041089553
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918534054
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041336383
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918286935
[INFO] [stdout] [Epoch 331]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041099717
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918523874
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041326616
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891829668
[INFO] [stdout] [Epoch 332]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041109095
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891851448
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041317604
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891830568
[INFO] [stdout] [Epoch 333]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041117747
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891850583
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.249897990413093
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891831398
[INFO] [stdout] [Epoch 334]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904112573
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918497833
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904130163
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891832164
[INFO] [stdout] [Epoch 335]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041133093
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891849046
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041294558
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918328696
[INFO] [stdout] [Epoch 336]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041139876
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891848368
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041288055
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918335197
[INFO] [stdout] [Epoch 337]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041146124
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184774
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041282038
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989183412
[INFO] [stdout] [Epoch 338]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041151914
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891847161
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041276478
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891834675
[INFO] [stdout] [Epoch 339]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041157246
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918466275
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904127137
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918351845
[INFO] [stdout] [Epoch 340]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041162147
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891846136
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041266653
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891835656
[INFO] [stdout] [Epoch 341]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041166677
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891845681
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041262314
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891836089
[INFO] [stdout] [Epoch 342]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041170854
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891845264
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041258295
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918364895
[INFO] [stdout] [Epoch 343]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041174712
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918448767
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041254598
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918368587
[INFO] [stdout] [Epoch 344]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041178265
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891844521
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041251182
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918371995
[INFO] [stdout] [Epoch 345]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041181546
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918441917
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904124803
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891837512
[INFO] [stdout] [Epoch 346]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041184563
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843889
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904124513
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918378024
[INFO] [stdout] [Epoch 347]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041187355
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843608
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041242444
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918380694
[INFO] [stdout] [Epoch 348]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041189925
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184335
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904123998
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918383153
[INFO] [stdout] [Epoch 349]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249897990411923
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843112
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041237704
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838543
[INFO] [stdout] [Epoch 350]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041194494
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842892
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041235597
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918387527
[INFO] [stdout] [Epoch 351]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041196514
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918426884
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041233665
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918389437
[INFO] [stdout] [Epoch 352]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041198366
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918425025
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041231878
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891839122
[INFO] [stdout] [Epoch 353]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904120008
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918423304
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041230235
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891839285
[INFO] [stdout] [Epoch 354]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041201663
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891842172
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041228714
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918394366
[INFO] [stdout] [Epoch 355]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041203123
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918420245
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041227315
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891839575
[INFO] [stdout] [Epoch 356]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041204466
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918418885
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041226027
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891839704
[INFO] [stdout] [Epoch 357]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904120571
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841764
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041224828
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891839823
[INFO] [stdout] [Epoch 358]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904120686
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918416476
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041223731
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891839932
[INFO] [stdout] [Epoch 359]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041207914
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841541
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904122272
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840032
[INFO] [stdout] [Epoch 360]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904120889
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841443
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041221777
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840124
[INFO] [stdout] [Epoch 361]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904120979
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841352
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041220923
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918402077
[INFO] [stdout] [Epoch 362]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041210603
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841269
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041220134
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840287
[INFO] [stdout] [Epoch 363]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121137
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918411924
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041219402
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184036
[INFO] [stdout] [Epoch 364]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041212074
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841121
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041218713
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918404275
[INFO] [stdout] [Epoch 365]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041212735
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841053
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121808
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918404885
[INFO] [stdout] [Epoch 366]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041213334
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918409915
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041217514
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918405457
[INFO] [stdout] [Epoch 367]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041213884
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840937
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216992
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840596
[INFO] [stdout] [Epoch 368]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041214383
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918408854
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216493
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840644
[INFO] [stdout] [Epoch 369]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041214855
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918408377
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121605
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918406895
[INFO] [stdout] [Epoch 370]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215294
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840792
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041215627
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184073
[INFO] [stdout] [Epoch 371]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249897990412157
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918407517
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121525
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840766
[INFO] [stdout] [Epoch 372]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121606
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918407145
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041214894
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840803
[INFO] [stdout] [Epoch 373]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121641
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840679
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121456
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918408344
[INFO] [stdout] [Epoch 374]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121672
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840646
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121426
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918408616
[INFO] [stdout] [Epoch 375]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041216998
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840618
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041213995
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840889
[INFO] [stdout] [Epoch 376]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041217264
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918405907
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121374
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840914
[INFO] [stdout] [Epoch 377]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041217514
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918405635
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041213506
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840935
[INFO] [stdout] [Epoch 378]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121773
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918405407
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041213284
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918409565
[INFO] [stdout] [Epoch 379]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041217947
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918405185
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041213084
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840976
[INFO] [stdout] [Epoch 380]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218136
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918405
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212907
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918409926
[INFO] [stdout] [Epoch 381]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218314
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840481
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121274
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918410087
[INFO] [stdout] [Epoch 382]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218475
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840464
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212585
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918410226
[INFO] [stdout] [Epoch 383]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121862
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840448
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212452
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841035
[INFO] [stdout] [Epoch 384]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218747
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918404347
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212318
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841048
[INFO] [stdout] [Epoch 385]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121888
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840421
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212196
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184106
[INFO] [stdout] [Epoch 386]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041218996
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918404075
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041212085
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184107
[INFO] [stdout] [Epoch 387]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219102
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840396
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211974
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841079
[INFO] [stdout] [Epoch 388]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219202
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840385
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211885
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841087
[INFO] [stdout] [Epoch 389]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121929
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918403753
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211797
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841095
[INFO] [stdout] [Epoch 390]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219374
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918403664
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121172
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918411025
[INFO] [stdout] [Epoch 391]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219463
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918403564
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121164
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918411097
[INFO] [stdout] [Epoch 392]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121954
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840348
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211563
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918411164
[INFO] [stdout] [Epoch 393]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219607
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840339
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211497
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841122
[INFO] [stdout] [Epoch 394]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219674
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918403326
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121143
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918411264
[INFO] [stdout] [Epoch 395]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121973
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840326
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211386
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918411297
[INFO] [stdout] [Epoch 396]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219773
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918403215
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121134
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841133
[INFO] [stdout] [Epoch 397]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219812
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918403153
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211308
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841137
[INFO] [stdout] [Epoch 398]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219857
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184031
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211264
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184114
[INFO] [stdout] [Epoch 399]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219896
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918403054
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121122
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918411436
[INFO] [stdout] [Epoch 400]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121994
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918403
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211175
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841147
[INFO] [stdout] [Epoch 401]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219973
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918402965
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211153
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841149
[INFO] [stdout] [Epoch 402]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220007
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918402915
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121112
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918411513
[INFO] [stdout] [Epoch 403]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220034
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840287
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211086
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918411536
[INFO] [stdout] [Epoch 404]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220068
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918402837
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211053
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841156
[INFO] [stdout] [Epoch 405]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220095
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918402804
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211042
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841157
[INFO] [stdout] [Epoch 406]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220118
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840277
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121102
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841158
[INFO] [stdout] [Epoch 407]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220134
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918402737
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210997
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841158
[INFO] [stdout] [Epoch 408]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904122015
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918402715
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210986
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841159
[INFO] [stdout] [Epoch 409]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220173
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840269
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210964
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184116
[INFO] [stdout] [Epoch 410]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904122019
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840266
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210953
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918411613
[INFO] [stdout] [Epoch 411]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220206
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840262
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121093
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918411613
[INFO] [stdout] [Epoch 412]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220212
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840261
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121092
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184116
[INFO] [stdout] [Epoch 413]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220223
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184026
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121092
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184116
[INFO] [stdout] [Epoch 414]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904122023
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918402576
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210908
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184116
[INFO] [stdout] [Epoch 415]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220234
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918402565
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210908
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184116
[INFO] [stdout] [Epoch 416]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904122024
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918402554
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210897
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841159
[INFO] [stdout] [Epoch 417]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220245
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840253
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210897
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841159
[INFO] [stdout] [Epoch 418]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904122025
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840253
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210886
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841159
[INFO] [stdout] [Epoch 419]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220256
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840251
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210897
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841157
[INFO] [stdout] [Epoch 420]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904122025
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840251
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210897
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841156
[INFO] [stdout] [Epoch 421]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220245
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840251
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210908
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918411547
[INFO] [stdout] [Epoch 422]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904122024
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184025
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210908
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918411525
[INFO] [stdout] [Epoch 423]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220234
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840249
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210908
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918411525
[INFO] [stdout] [Epoch 424]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904122024
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840249
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210908
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918411513
[INFO] [stdout] [Epoch 425]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220234
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918402465
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210908
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184115
[INFO] [stdout] [Epoch 426]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904122024
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918402465
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121092
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841149
[INFO] [stdout] [Epoch 427]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220234
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918402465
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210908
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841149
[INFO] [stdout] [Epoch 428]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220234
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918402443
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210908
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841148
[INFO] [stdout] [Epoch 429]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904122024
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918402443
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210908
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841147
[INFO] [stdout] [Epoch 430]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220234
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840242
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210908
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841146
[INFO] [stdout] [Epoch 431]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904122024
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840242
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121092
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918411447
[INFO] [stdout] [Epoch 432]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904122023
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840242
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210908
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918411436
[INFO] [stdout] [Epoch 433]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904122024
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184024
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210908
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918411436
[INFO] [stdout] [Epoch 434]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220245
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840239
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210908
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918411425
[INFO] [stdout] [Epoch 435]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220245
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918402376
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210908
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918411425
[INFO] [stdout] [Epoch 436]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904122025
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840236
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210897
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918411414
[INFO] [stdout] [Epoch 437]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904122025
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840235
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210897
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918411414
[INFO] [stdout] [Epoch 438]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220256
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918402326
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210897
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184114
[INFO] [stdout] [Epoch 439]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220256
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918402326
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210897
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841139
[INFO] [stdout] [Epoch 440]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220262
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918402304
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210886
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841139
[INFO] [stdout] [Epoch 441]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220262
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918402304
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210886
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841138
[INFO] [stdout] [Epoch 442]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220268
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840228
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210886
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841138
[INFO] [stdout] [Epoch 443]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220268
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840228
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210886
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841137
[INFO] [stdout] [Epoch 444]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220268
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840226
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210886
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841136
[INFO] [stdout] [Epoch 445]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220273
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840226
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210886
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841136
[INFO] [stdout] [Epoch 446]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220273
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840224
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210886
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841133
[INFO] [stdout] [Epoch 447]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220262
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840224
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210897
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841131
[INFO] [stdout] [Epoch 448]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904122025
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840225
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210897
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841131
[INFO] [stdout] [Epoch 449]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220256
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840224
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210897
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918411297
[INFO] [stdout] [Epoch 450]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220256
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918402226
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210908
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918411275
[INFO] [stdout] [Epoch 451]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220245
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918402226
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121092
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841125
[INFO] [stdout] [Epoch 452]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220234
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918402215
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121092
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841125
[INFO] [stdout] [Epoch 453]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220234
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918402215
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121092
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841124
[INFO] [stdout] [Epoch 454]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220234
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918402204
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121092
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841123
[INFO] [stdout] [Epoch 455]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220234
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918402193
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121092
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841122
[INFO] [stdout] [Epoch 456]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220234
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918402193
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121092
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841122
[INFO] [stdout] [Epoch 457]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220234
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840217
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121092
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841121
[INFO] [stdout] [Epoch 458]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220234
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840217
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121092
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918411197
[INFO] [stdout] [Epoch 459]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220234
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840216
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121092
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918411186
[INFO] [stdout] [Epoch 460]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220234
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840215
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121092
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918411175
[INFO] [stdout] [Epoch 461]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220234
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840215
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121092
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918411164
[INFO] [stdout] [Epoch 462]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220234
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918402127
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121092
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841115
[INFO] [stdout] [Epoch 463]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904122024
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918402127
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121092
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841115
[INFO] [stdout] [Epoch 464]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904122024
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918402127
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121092
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841114
[INFO] [stdout] [Epoch 465]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904122024
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918402104
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121093
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841113
[INFO] [stdout] [Epoch 466]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220234
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918402104
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121093
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841112
[INFO] [stdout] [Epoch 467]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220234
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918402077
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121093
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841111
[INFO] [stdout] [Epoch 468]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220234
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918402077
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121093
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918411097
[INFO] [stdout] [Epoch 469]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904122023
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918402077
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121093
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918411086
[INFO] [stdout] [Epoch 470]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904122023
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918402065
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121093
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918411075
[INFO] [stdout] [Epoch 471]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904122023
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918402054
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210942
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918411064
[INFO] [stdout] [Epoch 472]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220223
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918402054
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210942
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918411064
[INFO] [stdout] [Epoch 473]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220234
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840203
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121093
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918411047
[INFO] [stdout] [Epoch 474]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904122023
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840203
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121093
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918411036
[INFO] [stdout] [Epoch 475]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904122023
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840202
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210942
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918411036
[INFO] [stdout] [Epoch 476]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220234
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840201
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121093
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918411025
[INFO] [stdout] [Epoch 477]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220234
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918402
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121093
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918411014
[INFO] [stdout] [Epoch 478]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904122023
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840199
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121093
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918411
[INFO] [stdout] [Epoch 479]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904122023
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840199
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210942
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841098
[INFO] [stdout] [Epoch 480]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220223
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840199
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210942
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841098
[INFO] [stdout] [Epoch 481]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904122023
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918401966
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121093
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841097
[INFO] [stdout] [Epoch 482]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904122023
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918401966
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210942
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841096
[INFO] [stdout] [Epoch 483]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220223
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918401954
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210942
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841096
[INFO] [stdout] [Epoch 484]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904122023
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918401943
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210942
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841096
[INFO] [stdout] [Epoch 485]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904122024
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840192
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210942
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918410936
[INFO] [stdout] [Epoch 486]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220223
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840192
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210942
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918410925
[INFO] [stdout] [Epoch 487]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904122023
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840192
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210942
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918410925
[INFO] [stdout] [Epoch 488]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220234
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184019
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121093
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918410914
[INFO] [stdout] [Epoch 489]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904122023
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184019
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210942
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918410903
[INFO] [stdout] [Epoch 490]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904122023
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840189
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210942
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841089
[INFO] [stdout] [Epoch 491]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220234
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918401877
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210942
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841089
[INFO] [stdout] [Epoch 492]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904122024
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918401866
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210942
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841087
[INFO] [stdout] [Epoch 493]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904122023
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918401855
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210953
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841087
[INFO] [stdout] [Epoch 494]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220234
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918401855
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210942
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841086
[INFO] [stdout] [Epoch 495]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904122024
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840183
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210942
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841086
[INFO] [stdout] [Epoch 496]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220245
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840183
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121093
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841085
[INFO] [stdout] [Epoch 497]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904122024
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840181
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210942
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918410825
[INFO] [stdout] [Epoch 498]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220234
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840181
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210942
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918410825
[INFO] [stdout] [Epoch 499]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220234
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918401793
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210942
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918410825
[INFO] [stdout] [Epoch 500]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904122024
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840178
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210942
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918410814
[INFO] [stdout] [Epoch 501]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220245
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840177
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121093
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918410803
[INFO] [stdout] [Epoch 502]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904122024
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840176
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210942
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841078
[INFO] [stdout] [Epoch 503]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220234
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840176
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210942
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841078
[INFO] [stdout] [Epoch 504]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220234
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840175
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210942
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841078
[INFO] [stdout] [Epoch 505]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904122024
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840174
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210942
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918410764
[INFO] [stdout] [Epoch 506]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220245
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840174
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121093
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918410753
[INFO] [stdout] [Epoch 507]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904122024
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918401716
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210942
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841073
[INFO] [stdout] [Epoch 508]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904122023
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918401716
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210953
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841073
[INFO] [stdout] [Epoch 509]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220234
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918401716
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210942
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841072
[INFO] [stdout] [Epoch 510]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220234
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918401694
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210942
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841072
[INFO] [stdout] [Epoch 511]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904122024
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918401694
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210942
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841071
[INFO] [stdout] [Epoch 512]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904122024
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840167
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210942
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841071
[INFO] [stdout] [Epoch 513]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220245
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840167
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210942
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918410675
[INFO] [stdout] [Epoch 514]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220223
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840167
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210953
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918410675
[INFO] [stdout] [Epoch 515]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904122023
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840166
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210953
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918410664
[INFO] [stdout] [Epoch 516]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904122023
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840165
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210953
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918410664
[INFO] [stdout] [Epoch 517]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220234
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840164
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210953
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918410653
[INFO] [stdout] [Epoch 518]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220234
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918401627
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210942
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841064
[INFO] [stdout] [Epoch 519]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904122024
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918401627
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210942
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841064
[INFO] [stdout] [Epoch 520]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904122024
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918401605
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210942
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841063
[INFO] [stdout] [Epoch 521]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904122024
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918401605
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210942
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841062
[INFO] [stdout] [Epoch 522]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904122024
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840158
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210942
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841062
[INFO] [stdout] [Epoch 523]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220245
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840158
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210953
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184106
[INFO] [stdout] [Epoch 524]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220234
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840158
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210953
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918410586
[INFO] [stdout] [Epoch 525]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904122024
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840157
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210953
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918410586
[INFO] [stdout] [Epoch 526]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904122024
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840156
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210953
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918410575
[INFO] [stdout] [Epoch 527]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904122024
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840155
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210953
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918410564
[INFO] [stdout] [Epoch 528]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220245
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840154
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210953
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918410564
[INFO] [stdout] [Epoch 529]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220245
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840154
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210942
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841054
[INFO] [stdout] [Epoch 530]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220234
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840152
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210953
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841053
[INFO] [stdout] [Epoch 531]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220234
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840151
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210953
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841052
[INFO] [stdout] [Epoch 532]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220234
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840151
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210964
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841051
[INFO] [stdout] [Epoch 533]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220234
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840149
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210964
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841051
[INFO] [stdout] [Epoch 534]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220234
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840149
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210964
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841049
[INFO] [stdout] [Epoch 535]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220234
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918401477
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210964
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841048
[INFO] [stdout] [Epoch 536]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220234
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918401466
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210964
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841047
[INFO] [stdout] [Epoch 537]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904122023
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918401466
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210964
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841046
[INFO] [stdout] [Epoch 538]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904122023
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918401455
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210964
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841045
[INFO] [stdout] [Epoch 539]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904122023
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918401444
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210964
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841045
[INFO] [stdout] [Epoch 540]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904122023
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918401444
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210964
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918410437
[INFO] [stdout] [Epoch 541]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904122023
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840143
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210964
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918410425
[INFO] [stdout] [Epoch 542]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220223
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840142
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210964
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918410414
[INFO] [stdout] [Epoch 543]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220223
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840142
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210975
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918410403
[INFO] [stdout] [Epoch 544]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220223
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840141
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210975
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841039
[INFO] [stdout] [Epoch 545]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220223
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184014
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210975
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841038
[INFO] [stdout] [Epoch 546]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220218
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184014
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210975
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841037
[INFO] [stdout] [Epoch 547]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220218
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184014
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210975
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841036
[INFO] [stdout] [Epoch 548]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220212
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918401377
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210986
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841035
[INFO] [stdout] [Epoch 549]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220212
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918401377
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210986
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918410337
[INFO] [stdout] [Epoch 550]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220212
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918401377
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210986
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918410326
[INFO] [stdout] [Epoch 551]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220206
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918401366
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210986
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918410314
[INFO] [stdout] [Epoch 552]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220206
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918401355
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210997
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918410303
[INFO] [stdout] [Epoch 553]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220206
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918401355
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210997
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841029
[INFO] [stdout] [Epoch 554]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249897990412202
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918401344
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210997
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841028
[INFO] [stdout] [Epoch 555]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249897990412202
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840133
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041210997
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841027
[INFO] [stdout] [Epoch 556]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220195
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840133
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211008
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841026
[INFO] [stdout] [Epoch 557]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220195
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840133
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211008
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841025
[INFO] [stdout] [Epoch 558]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904122019
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840132
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211008
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918410237
[INFO] [stdout] [Epoch 559]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220184
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840132
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121102
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841021
[INFO] [stdout] [Epoch 560]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220184
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840131
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121102
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184102
[INFO] [stdout] [Epoch 561]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904122018
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840131
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121103
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918410187
[INFO] [stdout] [Epoch 562]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220173
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840131
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121103
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918410176
[INFO] [stdout] [Epoch 563]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220168
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184013
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121103
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918410165
[INFO] [stdout] [Epoch 564]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220162
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184013
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211042
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918410153
[INFO] [stdout] [Epoch 565]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220162
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840129
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211042
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841013
[INFO] [stdout] [Epoch 566]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220156
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840129
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211053
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841012
[INFO] [stdout] [Epoch 567]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904122015
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840129
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211053
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841011
[INFO] [stdout] [Epoch 568]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220145
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840129
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211064
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184101
[INFO] [stdout] [Epoch 569]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904122014
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840129
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211064
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918410076
[INFO] [stdout] [Epoch 570]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220134
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918401277
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211075
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918410065
[INFO] [stdout] [Epoch 571]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904122013
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918401266
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211075
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918410054
[INFO] [stdout] [Epoch 572]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220123
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918401266
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211086
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841004
[INFO] [stdout] [Epoch 573]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220118
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918401266
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211086
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841002
[INFO] [stdout] [Epoch 574]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220118
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918401266
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211086
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841002
[INFO] [stdout] [Epoch 575]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220123
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840124
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211075
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841002
[INFO] [stdout] [Epoch 576]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904122013
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840123
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211075
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841001
[INFO] [stdout] [Epoch 577]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220134
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918401216
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211075
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841001
[INFO] [stdout] [Epoch 578]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220134
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918401194
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211064
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841001
[INFO] [stdout] [Epoch 579]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904122014
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918401194
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211064
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841
[INFO] [stdout] [Epoch 580]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220145
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840117
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211053
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891841
[INFO] [stdout] [Epoch 581]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904122015
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840116
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211053
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918409987
[INFO] [stdout] [Epoch 582]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904122015
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840115
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211053
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918409987
[INFO] [stdout] [Epoch 583]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220156
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840114
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211042
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918409987
[INFO] [stdout] [Epoch 584]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220162
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211042
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918409976
[INFO] [stdout] [Epoch 585]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220162
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918401116
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211042
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918409976
[INFO] [stdout] [Epoch 586]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220168
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918401105
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211042
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918409965
[INFO] [stdout] [Epoch 587]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220173
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918401083
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211042
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918409954
[INFO] [stdout] [Epoch 588]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220162
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918401083
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211053
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918409926
[INFO] [stdout] [Epoch 589]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220156
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918401083
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211064
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918409915
[INFO] [stdout] [Epoch 590]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220145
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918401083
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211075
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840989
[INFO] [stdout] [Epoch 591]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904122014
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918401083
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211075
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840988
[INFO] [stdout] [Epoch 592]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904122013
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918401083
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211086
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840986
[INFO] [stdout] [Epoch 593]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220123
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918401083
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211097
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840985
[INFO] [stdout] [Epoch 594]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220112
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918401083
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211108
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918409826
[INFO] [stdout] [Epoch 595]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249897990412201
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918401083
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121112
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918409804
[INFO] [stdout] [Epoch 596]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220095
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918401083
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121112
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840979
[INFO] [stdout] [Epoch 597]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220084
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918401094
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121113
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840977
[INFO] [stdout] [Epoch 598]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220073
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918401094
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211141
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840976
[INFO] [stdout] [Epoch 599]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220068
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918401094
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211153
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918409737
[INFO] [stdout] [Epoch 600]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220062
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918401083
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211153
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918409726
[INFO] [stdout] [Epoch 601]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220062
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918401083
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211153
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918409726
[INFO] [stdout] [Epoch 602]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220062
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840107
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211153
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918409715
[INFO] [stdout] [Epoch 603]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220068
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840106
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211153
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918409704
[INFO] [stdout] [Epoch 604]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220068
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840105
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211153
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918409704
[INFO] [stdout] [Epoch 605]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220068
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840104
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211153
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840969
[INFO] [stdout] [Epoch 606]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220068
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840104
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211141
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840968
[INFO] [stdout] [Epoch 607]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220068
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918401016
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211141
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840968
[INFO] [stdout] [Epoch 608]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220068
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918401016
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211141
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840967
[INFO] [stdout] [Epoch 609]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220068
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918401005
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211141
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840966
[INFO] [stdout] [Epoch 610]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220068
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918400994
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211141
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840964
[INFO] [stdout] [Epoch 611]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220068
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918400994
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211153
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840963
[INFO] [stdout] [Epoch 612]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220068
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918400983
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211153
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840963
[INFO] [stdout] [Epoch 613]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220068
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840097
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211153
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840962
[INFO] [stdout] [Epoch 614]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220068
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840097
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211153
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840961
[INFO] [stdout] [Epoch 615]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220068
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918400955
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211153
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184096
[INFO] [stdout] [Epoch 616]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220068
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918400944
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211153
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918409587
[INFO] [stdout] [Epoch 617]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220068
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918400944
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211153
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918409587
[INFO] [stdout] [Epoch 618]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220062
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918400933
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211153
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918409576
[INFO] [stdout] [Epoch 619]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220062
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840092
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211153
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918409565
[INFO] [stdout] [Epoch 620]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220062
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840092
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211153
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918409554
[INFO] [stdout] [Epoch 621]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220062
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840091
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211164
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918409543
[INFO] [stdout] [Epoch 622]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220057
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184009
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211164
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840953
[INFO] [stdout] [Epoch 623]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220057
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184009
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211164
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840952
[INFO] [stdout] [Epoch 624]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220057
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840089
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211164
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840951
[INFO] [stdout] [Epoch 625]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904122005
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840088
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211164
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184095
[INFO] [stdout] [Epoch 626]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904122005
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840088
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211164
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840949
[INFO] [stdout] [Epoch 627]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904122005
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840088
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211175
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918409476
[INFO] [stdout] [Epoch 628]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220045
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918400866
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211175
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918409465
[INFO] [stdout] [Epoch 629]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220045
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918400855
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211175
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918409454
[INFO] [stdout] [Epoch 630]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220045
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918400855
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211175
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918409443
[INFO] [stdout] [Epoch 631]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220045
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918400844
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211186
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840943
[INFO] [stdout] [Epoch 632]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904122004
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918400833
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211186
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840942
[INFO] [stdout] [Epoch 633]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904122004
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918400833
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211186
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840941
[INFO] [stdout] [Epoch 634]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220034
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918400833
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211197
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184094
[INFO] [stdout] [Epoch 635]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904122003
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840082
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211197
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840939
[INFO] [stdout] [Epoch 636]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904122003
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840081
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211197
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840937
[INFO] [stdout] [Epoch 637]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220023
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840081
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211208
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840936
[INFO] [stdout] [Epoch 638]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220018
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840081
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211208
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840935
[INFO] [stdout] [Epoch 639]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220018
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840081
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211208
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840934
[INFO] [stdout] [Epoch 640]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220012
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184008
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121122
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918409326
[INFO] [stdout] [Epoch 641]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041220007
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184008
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121122
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918409304
[INFO] [stdout] [Epoch 642]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904122
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840079
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121123
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918409293
[INFO] [stdout] [Epoch 643]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219996
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840079
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121123
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840928
[INFO] [stdout] [Epoch 644]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219996
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840079
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211241
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840927
[INFO] [stdout] [Epoch 645]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121999
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840079
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211241
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840926
[INFO] [stdout] [Epoch 646]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219984
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840078
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211241
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840925
[INFO] [stdout] [Epoch 647]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121998
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840078
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211253
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918409226
[INFO] [stdout] [Epoch 648]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219973
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918400766
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211253
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918409215
[INFO] [stdout] [Epoch 649]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219968
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918400766
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211264
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918409204
[INFO] [stdout] [Epoch 650]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219962
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918400766
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211264
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918409193
[INFO] [stdout] [Epoch 651]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219957
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918400766
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211275
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840917
[INFO] [stdout] [Epoch 652]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219957
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918400744
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211275
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840917
[INFO] [stdout] [Epoch 653]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219962
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918400744
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211275
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840916
[INFO] [stdout] [Epoch 654]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219957
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918400733
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211275
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840915
[INFO] [stdout] [Epoch 655]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219962
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840072
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211275
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840914
[INFO] [stdout] [Epoch 656]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121995
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840072
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211275
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840914
[INFO] [stdout] [Epoch 657]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219957
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184007
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211264
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918409126
[INFO] [stdout] [Epoch 658]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219962
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184007
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211275
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918409115
[INFO] [stdout] [Epoch 659]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219957
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184007
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211275
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918409115
[INFO] [stdout] [Epoch 660]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219962
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840069
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211275
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840909
[INFO] [stdout] [Epoch 661]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121995
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840067
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211275
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840909
[INFO] [stdout] [Epoch 662]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219957
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840066
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211275
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840909
[INFO] [stdout] [Epoch 663]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219962
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840065
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211275
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918409065
[INFO] [stdout] [Epoch 664]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121995
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840065
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211275
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918409065
[INFO] [stdout] [Epoch 665]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219957
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840064
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211275
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918409054
[INFO] [stdout] [Epoch 666]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219962
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840063
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211275
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918409043
[INFO] [stdout] [Epoch 667]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121995
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840063
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211275
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840903
[INFO] [stdout] [Epoch 668]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219957
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918400617
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211275
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840903
[INFO] [stdout] [Epoch 669]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219957
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918400606
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211275
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840902
[INFO] [stdout] [Epoch 670]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219962
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918400606
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211275
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840901
[INFO] [stdout] [Epoch 671]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121995
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918400594
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211275
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918409
[INFO] [stdout] [Epoch 672]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219957
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918400583
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211275
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918409
[INFO] [stdout] [Epoch 673]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219957
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840057
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211275
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840899
[INFO] [stdout] [Epoch 674]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219962
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840056
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211286
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918408977
[INFO] [stdout] [Epoch 675]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121995
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840056
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211286
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918408965
[INFO] [stdout] [Epoch 676]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121995
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840055
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211275
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918408965
[INFO] [stdout] [Epoch 677]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219957
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840054
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211275
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918408954
[INFO] [stdout] [Epoch 678]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219957
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840054
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211275
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918408954
[INFO] [stdout] [Epoch 679]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219957
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918400517
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211286
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840893
[INFO] [stdout] [Epoch 680]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121995
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918400517
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211297
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840891
[INFO] [stdout] [Epoch 681]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121994
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840053
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211308
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184089
[INFO] [stdout] [Epoch 682]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121993
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840053
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121132
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918408877
[INFO] [stdout] [Epoch 683]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219923
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918400517
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121132
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918408866
[INFO] [stdout] [Epoch 684]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219923
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918400517
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121132
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918408866
[INFO] [stdout] [Epoch 685]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121993
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918400506
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211308
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918408854
[INFO] [stdout] [Epoch 686]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121993
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918400494
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211308
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918408843
[INFO] [stdout] [Epoch 687]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121993
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918400494
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211308
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918408843
[INFO] [stdout] [Epoch 688]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121993
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840047
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211308
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840883
[INFO] [stdout] [Epoch 689]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121993
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840047
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211308
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840882
[INFO] [stdout] [Epoch 690]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121993
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840046
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211308
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840882
[INFO] [stdout] [Epoch 691]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121993
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840045
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211308
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918408805
[INFO] [stdout] [Epoch 692]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121993
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840045
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211308
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918408793
[INFO] [stdout] [Epoch 693]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121993
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840044
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121132
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840878
[INFO] [stdout] [Epoch 694]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121993
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840043
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121132
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840877
[INFO] [stdout] [Epoch 695]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121993
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840043
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121132
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840877
[INFO] [stdout] [Epoch 696]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219923
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918400417
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121132
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840876
[INFO] [stdout] [Epoch 697]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219923
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184004
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121132
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840875
[INFO] [stdout] [Epoch 698]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219923
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184004
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121132
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840874
[INFO] [stdout] [Epoch 699]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219923
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840039
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121132
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918408727
[INFO] [stdout] [Epoch 700]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219923
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840038
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121132
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918408727
[INFO] [stdout] [Epoch 701]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219918
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840038
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121132
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918408716
[INFO] [stdout] [Epoch 702]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219918
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840038
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121133
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918408705
[INFO] [stdout] [Epoch 703]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219918
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918400356
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121133
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918408693
[INFO] [stdout] [Epoch 704]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219918
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918400356
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121133
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840868
[INFO] [stdout] [Epoch 705]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219912
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918400356
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121133
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840867
[INFO] [stdout] [Epoch 706]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219912
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918400345
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121133
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840866
[INFO] [stdout] [Epoch 707]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219907
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918400333
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121133
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840865
[INFO] [stdout] [Epoch 708]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219907
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918400333
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121134
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840864
[INFO] [stdout] [Epoch 709]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219907
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918400333
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121134
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918408627
[INFO] [stdout] [Epoch 710]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249897990412199
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840032
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121134
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918408616
[INFO] [stdout] [Epoch 711]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249897990412199
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840031
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121134
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918408605
[INFO] [stdout] [Epoch 712]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249897990412199
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840031
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211352
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918408594
[INFO] [stdout] [Epoch 713]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249897990412199
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184003
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211352
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840858
[INFO] [stdout] [Epoch 714]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219896
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840029
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211352
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840857
[INFO] [stdout] [Epoch 715]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121989
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840029
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211364
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840856
[INFO] [stdout] [Epoch 716]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121989
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840029
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211364
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840855
[INFO] [stdout] [Epoch 717]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219884
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840029
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211364
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840854
[INFO] [stdout] [Epoch 718]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121988
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840028
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211375
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840852
[INFO] [stdout] [Epoch 719]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121988
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918400267
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211375
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840851
[INFO] [stdout] [Epoch 720]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219873
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918400267
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211375
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184085
[INFO] [stdout] [Epoch 721]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219868
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918400267
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211386
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840849
[INFO] [stdout] [Epoch 722]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219868
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918400267
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211386
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918408477
[INFO] [stdout] [Epoch 723]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219862
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918400267
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211397
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918408466
[INFO] [stdout] [Epoch 724]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219857
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918400256
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211397
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918408444
[INFO] [stdout] [Epoch 725]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121985
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918400245
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211408
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840843
[INFO] [stdout] [Epoch 726]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219846
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918400245
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211408
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840842
[INFO] [stdout] [Epoch 727]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121984
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918400245
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211408
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840841
[INFO] [stdout] [Epoch 728]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219835
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918400245
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121142
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184084
[INFO] [stdout] [Epoch 729]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219835
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918400245
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121142
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840839
[INFO] [stdout] [Epoch 730]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121983
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918400245
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121143
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918408366
[INFO] [stdout] [Epoch 731]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219823
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918400234
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121143
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918408355
[INFO] [stdout] [Epoch 732]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219818
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918400234
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121144
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918408344
[INFO] [stdout] [Epoch 733]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219812
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840022
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121144
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840833
[INFO] [stdout] [Epoch 734]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219807
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840022
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211452
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840831
[INFO] [stdout] [Epoch 735]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249897990412198
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840022
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211463
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184083
[INFO] [stdout] [Epoch 736]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219796
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840022
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211463
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840829
[INFO] [stdout] [Epoch 737]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121979
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840022
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211475
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918408277
[INFO] [stdout] [Epoch 738]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219785
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840022
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211475
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840825
[INFO] [stdout] [Epoch 739]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121978
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840021
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211486
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840824
[INFO] [stdout] [Epoch 740]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219773
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840021
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211497
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840824
[INFO] [stdout] [Epoch 741]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121978
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184002
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211486
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918408216
[INFO] [stdout] [Epoch 742]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219768
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184002
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211497
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918408205
[INFO] [stdout] [Epoch 743]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219762
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184002
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211497
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918408194
[INFO] [stdout] [Epoch 744]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219757
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184002
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211508
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840818
[INFO] [stdout] [Epoch 745]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219757
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840018
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211508
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840818
[INFO] [stdout] [Epoch 746]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219762
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840018
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211497
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840816
[INFO] [stdout] [Epoch 747]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219757
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840018
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211508
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840816
[INFO] [stdout] [Epoch 748]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219757
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918400156
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211508
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840816
[INFO] [stdout] [Epoch 749]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219762
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918400156
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211508
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840815
[INFO] [stdout] [Epoch 750]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219762
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918400134
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211497
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840815
[INFO] [stdout] [Epoch 751]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219768
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918400134
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211497
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840813
[INFO] [stdout] [Epoch 752]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219762
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918400134
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211508
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840813
[INFO] [stdout] [Epoch 753]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219762
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918400106
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211508
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918408116
[INFO] [stdout] [Epoch 754]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219762
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918400106
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211497
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918408105
[INFO] [stdout] [Epoch 755]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219757
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918400106
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211508
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918408094
[INFO] [stdout] [Epoch 756]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219757
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918400095
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211508
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918408094
[INFO] [stdout] [Epoch 757]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219762
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918400084
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211508
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918408083
[INFO] [stdout] [Epoch 758]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219762
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840007
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211508
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918408083
[INFO] [stdout] [Epoch 759]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219762
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840006
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211508
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840807
[INFO] [stdout] [Epoch 760]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219768
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840006
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211497
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840807
[INFO] [stdout] [Epoch 761]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219768
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840004
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211497
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840805
[INFO] [stdout] [Epoch 762]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219757
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840004
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211508
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840804
[INFO] [stdout] [Epoch 763]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219768
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840003
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211497
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840804
[INFO] [stdout] [Epoch 764]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219768
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918400017
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211497
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840804
[INFO] [stdout] [Epoch 765]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121978
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918399995
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211486
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840804
[INFO] [stdout] [Epoch 766]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121978
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918399995
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211486
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840803
[INFO] [stdout] [Epoch 767]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121978
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918399984
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211486
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918408016
[INFO] [stdout] [Epoch 768]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219785
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839997
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211497
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918408016
[INFO] [stdout] [Epoch 769]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219785
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839997
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211486
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918408005
[INFO] [stdout] [Epoch 770]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219785
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839995
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211497
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918407994
[INFO] [stdout] [Epoch 771]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219785
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839995
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211497
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918407983
[INFO] [stdout] [Epoch 772]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219773
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839995
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211508
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918407966
[INFO] [stdout] [Epoch 773]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219773
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839995
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211508
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918407955
[INFO] [stdout] [Epoch 774]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219773
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839994
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211508
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918407955
[INFO] [stdout] [Epoch 775]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219773
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839993
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211508
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918407944
[INFO] [stdout] [Epoch 776]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219773
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839993
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211508
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918407933
[INFO] [stdout] [Epoch 777]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219773
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918399917
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211508
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840792
[INFO] [stdout] [Epoch 778]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219773
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918399906
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211508
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840792
[INFO] [stdout] [Epoch 779]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219773
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918399906
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211508
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840791
[INFO] [stdout] [Epoch 780]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219768
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918399895
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211508
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184079
[INFO] [stdout] [Epoch 781]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219768
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918399884
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211508
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840789
[INFO] [stdout] [Epoch 782]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219768
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918399884
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211508
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840788
[INFO] [stdout] [Epoch 783]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219768
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839987
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211508
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840788
[INFO] [stdout] [Epoch 784]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219768
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839986
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121152
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918407866
[INFO] [stdout] [Epoch 785]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219762
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839986
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121152
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918407855
[INFO] [stdout] [Epoch 786]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219762
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839986
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121152
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918407844
[INFO] [stdout] [Epoch 787]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219762
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839985
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121152
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918407833
[INFO] [stdout] [Epoch 788]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219757
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918399834
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121152
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840782
[INFO] [stdout] [Epoch 789]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219757
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918399834
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121152
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840781
[INFO] [stdout] [Epoch 790]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219757
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918399823
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121153
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184078
[INFO] [stderr] error: test failed, to rerun pass `--lib`
[INFO] [stdout] [Epoch 791]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121975
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839981
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121153
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184078
[INFO] [stdout] [Epoch 792]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121975
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839981
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121153
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840779
[INFO] [stdout] [Epoch 793]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219746
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839981
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121153
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840778
[INFO] [stdout] [Epoch 794]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219746
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839981
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121154
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918407766
[INFO] [stdout] [Epoch 795]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121974
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989183998
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121154
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918407755
[INFO] [stdout] [Epoch 796]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219746
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839979
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121153
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918407755
[INFO] [stdout] [Epoch 797]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121975
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839977
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121152
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918407755
[INFO] [stdout] [Epoch 798]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219762
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918399756
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121152
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918407755
[INFO] [stdout] [Epoch 799]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219768
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918399745
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211508
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918407755
[INFO] [stdout] [Epoch 800]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219773
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918399723
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211497
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918407755
[INFO] [stdout] [Epoch 801]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219785
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839971
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211497
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918407755
[INFO] [stdout] [Epoch 802]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121979
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989183997
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211486
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918407744
[INFO] [stdout] [Epoch 803]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219785
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839969
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211497
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918407733
[INFO] [stdout] [Epoch 804]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219785
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839969
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211497
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918407733
[INFO] [stdout] [Epoch 805]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121979
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839968
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211497
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840771
[INFO] [stdout] [Epoch 806]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219773
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839968
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121152
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918407683
[INFO] [stdout] [Epoch 807]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219773
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839968
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121152
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840767
[INFO] [stdout] [Epoch 808]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219768
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839968
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121152
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840766
[INFO] [stdout] [Epoch 809]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219762
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839968
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121153
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840765
[INFO] [stdout] [Epoch 810]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219757
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839967
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121153
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840764
[INFO] [stdout] [Epoch 811]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121975
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918399656
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121154
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840763
[INFO] [stdout] [Epoch 812]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219746
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918399656
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121154
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918407617
[INFO] [stdout] [Epoch 813]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121974
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918399656
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211552
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918407594
[INFO] [stdout] [Epoch 814]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219735
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918399656
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211552
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918407583
[INFO] [stdout] [Epoch 815]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121973
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918399656
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211563
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840757
[INFO] [stdout] [Epoch 816]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219724
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918399656
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211563
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840756
[INFO] [stdout] [Epoch 817]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219718
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918399656
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211574
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840755
[INFO] [stdout] [Epoch 818]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219712
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918399656
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211574
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840753
[INFO] [stdout] [Epoch 819]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219707
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918399645
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211586
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918407517
[INFO] [stdout] [Epoch 820]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249897990412197
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918399645
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211586
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918407506
[INFO] [stdout] [Epoch 821]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219696
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918399645
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211597
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918407494
[INFO] [stdout] [Epoch 822]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121969
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918399634
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211608
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840747
[INFO] [stdout] [Epoch 823]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121969
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918399634
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211597
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840747
[INFO] [stdout] [Epoch 824]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219696
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918399623
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211597
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840747
[INFO] [stdout] [Epoch 825]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219696
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839961
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211586
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840747
[INFO] [stdout] [Epoch 826]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249897990412197
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989183996
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211586
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840746
[INFO] [stdout] [Epoch 827]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219707
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839959
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211586
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840746
[INFO] [stdout] [Epoch 828]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219712
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839957
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211574
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840746
[INFO] [stdout] [Epoch 829]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219712
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839957
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211574
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840745
[INFO] [stdout] [Epoch 830]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219718
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839954
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211574
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840745
[INFO] [stdout] [Epoch 831]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219724
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839954
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211563
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840745
[INFO] [stdout] [Epoch 832]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219724
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839952
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211563
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840744
[INFO] [stdout] [Epoch 833]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121973
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839952
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211563
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840744
[INFO] [stdout] [Epoch 834]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219735
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918399495
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211552
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840743
[INFO] [stdout] [Epoch 835]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219735
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918399495
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211552
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840743
[INFO] [stdout] [Epoch 836]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121974
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918399473
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211552
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918407417
[INFO] [stdout] [Epoch 837]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121974
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918399473
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211552
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918407417
[INFO] [stdout] [Epoch 838]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219746
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839945
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211552
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918407417
[INFO] [stdout] [Epoch 839]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219746
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839945
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121154
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184074
[INFO] [stdout] [Epoch 840]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121975
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839944
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121154
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184074
[INFO] [stdout] [Epoch 841]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121975
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839943
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121154
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840739
[INFO] [stdout] [Epoch 842]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219757
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839942
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121154
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840739
[INFO] [stdout] [Epoch 843]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219757
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918399406
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211552
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918407367
[INFO] [stdout] [Epoch 844]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219746
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918399406
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121154
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918407367
[INFO] [stdout] [Epoch 845]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121975
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918399406
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121154
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918407356
[INFO] [stdout] [Epoch 846]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121975
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918399384
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211552
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918407345
[INFO] [stdout] [Epoch 847]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121974
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918399395
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211563
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840732
[INFO] [stdout] [Epoch 848]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219735
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918399395
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211574
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184073
[INFO] [stdout] [Epoch 849]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219724
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918399395
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211586
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840729
[INFO] [stdout] [Epoch 850]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219712
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918399406
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211586
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840728
[INFO] [stdout] [Epoch 851]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219712
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918399384
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211597
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918407267
[INFO] [stdout] [Epoch 852]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249897990412197
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918399395
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211597
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918407245
[INFO] [stdout] [Epoch 853]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219696
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918399395
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211608
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840722
[INFO] [stdout] [Epoch 854]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219685
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918399395
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211608
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840722
[INFO] [stdout] [Epoch 855]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219685
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918399384
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211608
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840721
[INFO] [stdout] [Epoch 856]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219685
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918399384
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211608
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840721
[INFO] [stdout] [Epoch 857]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219685
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839936
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211608
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184072
[INFO] [stdout] [Epoch 858]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219685
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839936
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211608
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840719
[INFO] [stdout] [Epoch 859]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121969
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839936
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211608
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840719
[INFO] [stdout] [Epoch 860]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121969
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839934
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211608
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840718
[INFO] [stdout] [Epoch 861]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121969
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839934
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211608
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918407167
[INFO] [stdout] [Epoch 862]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121969
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839934
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211608
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918407156
[INFO] [stdout] [Epoch 863]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121969
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839932
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211608
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918407156
[INFO] [stdout] [Epoch 864]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121969
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839932
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121162
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918407145
[INFO] [stdout] [Epoch 865]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121969
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839932
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121162
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840713
[INFO] [stdout] [Epoch 866]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219685
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918399307
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121162
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918407117
[INFO] [stdout] [Epoch 867]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219685
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918399295
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121162
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918407117
[INFO] [stdout] [Epoch 868]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219685
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918399295
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121162
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918407106
[INFO] [stdout] [Epoch 869]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219685
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839928
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121162
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918407095
[INFO] [stdout] [Epoch 870]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219685
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839927
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121162
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918407084
[INFO] [stdout] [Epoch 871]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121968
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839927
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121162
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840707
[INFO] [stdout] [Epoch 872]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121968
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918399257
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121163
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840707
[INFO] [stdout] [Epoch 873]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121968
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918399245
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121163
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840706
[INFO] [stdout] [Epoch 874]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219674
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918399245
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121163
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840705
[INFO] [stdout] [Epoch 875]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219674
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918399245
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121163
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840704
[INFO] [stdout] [Epoch 876]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219674
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918399234
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121163
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840703
[INFO] [stdout] [Epoch 877]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219668
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918399223
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121163
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918407017
[INFO] [stdout] [Epoch 878]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219668
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918399223
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121164
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918407006
[INFO] [stdout] [Epoch 879]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219668
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918399223
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121164
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918406995
[INFO] [stdout] [Epoch 880]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219662
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839921
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121164
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918406995
[INFO] [stdout] [Epoch 881]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219662
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989183992
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121164
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918406984
[INFO] [stdout] [Epoch 882]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219657
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989183992
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211652
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840697
[INFO] [stdout] [Epoch 883]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219657
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989183992
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211652
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840696
[INFO] [stdout] [Epoch 884]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121965
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989183992
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211652
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840695
[INFO] [stdout] [Epoch 885]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219646
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839919
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211663
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840694
[INFO] [stdout] [Epoch 886]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219646
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839918
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211663
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840693
[INFO] [stdout] [Epoch 887]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121964
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839918
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211663
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918406917
[INFO] [stdout] [Epoch 888]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121964
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839918
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211674
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918406906
[INFO] [stdout] [Epoch 889]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121964
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839917
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211663
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918406906
[INFO] [stdout] [Epoch 890]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219646
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918399157
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211652
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918406906
[INFO] [stdout] [Epoch 891]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219657
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918399134
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211652
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918406906
[INFO] [stdout] [Epoch 892]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219662
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918399123
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211652
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918406895
[INFO] [stdout] [Epoch 893]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219657
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839911
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211652
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918406884
[INFO] [stdout] [Epoch 894]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121965
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839911
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211663
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840687
[INFO] [stdout] [Epoch 895]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219646
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839911
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211663
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918406845
[INFO] [stdout] [Epoch 896]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219646
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839911
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211674
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918406834
[INFO] [stdout] [Epoch 897]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121964
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839911
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211674
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840682
[INFO] [stdout] [Epoch 898]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219635
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839911
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211685
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840681
[INFO] [stdout] [Epoch 899]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121963
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989183991
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211685
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184068
[INFO] [stdout] [Epoch 900]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219624
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989183991
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211697
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840679
[INFO] [stdout] [Epoch 901]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219618
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839909
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211697
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840678
[INFO] [stdout] [Epoch 902]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219612
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839909
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211708
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840677
[INFO] [stdout] [Epoch 903]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219607
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839909
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211708
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918406745
[INFO] [stdout] [Epoch 904]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249897990412196
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839909
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121172
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918406734
[INFO] [stdout] [Epoch 905]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219596
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839909
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121172
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918406723
[INFO] [stdout] [Epoch 906]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121959
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839909
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121173
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840671
[INFO] [stdout] [Epoch 907]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219585
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839909
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121173
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184067
[INFO] [stdout] [Epoch 908]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121958
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839909
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121174
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840668
[INFO] [stdout] [Epoch 909]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219574
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839909
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121174
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840667
[INFO] [stdout] [Epoch 910]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219568
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839909
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211752
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918406656
[INFO] [stdout] [Epoch 911]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219563
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839909
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211763
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918406645
[INFO] [stdout] [Epoch 912]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219551
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839909
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211763
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918406623
[INFO] [stdout] [Epoch 913]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219551
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839907
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211763
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918406623
[INFO] [stdout] [Epoch 914]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219557
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839907
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211752
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918406623
[INFO] [stdout] [Epoch 915]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219563
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918399046
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211752
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840661
[INFO] [stdout] [Epoch 916]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219563
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918399046
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211752
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840661
[INFO] [stdout] [Epoch 917]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219568
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918399023
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121174
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840661
[INFO] [stdout] [Epoch 918]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219574
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918399023
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121174
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840661
[INFO] [stdout] [Epoch 919]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121958
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918398996
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121174
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184066
[INFO] [stdout] [Epoch 920]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121958
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918398996
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121173
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184066
[INFO] [stdout] [Epoch 921]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219585
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918398973
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121173
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840659
[INFO] [stdout] [Epoch 922]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219585
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918398973
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121173
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840659
[INFO] [stdout] [Epoch 923]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121959
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839895
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121173
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840659
[INFO] [stdout] [Epoch 924]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219596
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839895
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121172
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840658
[INFO] [stdout] [Epoch 925]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219596
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839894
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121172
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840658
[INFO] [stdout] [Epoch 926]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249897990412196
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839893
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121172
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840656
[INFO] [stdout] [Epoch 927]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249897990412196
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839892
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121172
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840656
[INFO] [stdout] [Epoch 928]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249897990412196
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918398907
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211708
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840656
[INFO] [stdout] [Epoch 929]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219607
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918398896
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211708
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840655
[INFO] [stdout] [Epoch 930]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219607
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918398885
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211708
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840655
[INFO] [stdout] [Epoch 931]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219612
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918398885
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211708
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840654
[INFO] [stdout] [Epoch 932]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219612
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839886
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211708
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840654
[INFO] [stdout] [Epoch 933]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219612
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839886
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211708
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840653
[INFO] [stdout] [Epoch 934]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219618
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839885
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211697
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840653
[INFO] [stdout] [Epoch 935]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219618
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839884
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211708
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918406506
[INFO] [stdout] [Epoch 936]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219607
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839884
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121172
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918406495
[INFO] [stdout] [Epoch 937]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249897990412196
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839884
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121173
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918406473
[INFO] [stdout] [Epoch 938]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121959
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839885
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121173
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918406473
[INFO] [stdout] [Epoch 939]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121959
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839884
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121173
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840646
[INFO] [stdout] [Epoch 940]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121959
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839883
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121174
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840644
[INFO] [stdout] [Epoch 941]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121958
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839884
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211752
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840643
[INFO] [stdout] [Epoch 942]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219574
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839884
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211763
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918406406
[INFO] [stdout] [Epoch 943]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219563
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839884
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211774
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918406395
[INFO] [stdout] [Epoch 944]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219551
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839884
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211785
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918406373
[INFO] [stdout] [Epoch 945]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121954
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839884
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211785
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840635
[INFO] [stdout] [Epoch 946]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121953
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839885
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211785
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840635
[INFO] [stdout] [Epoch 947]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121953
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839884
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211785
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840634
[INFO] [stdout] [Epoch 948]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219535
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839883
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211785
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840633
[INFO] [stdout] [Epoch 949]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219535
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839882
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211797
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840633
[INFO] [stdout] [Epoch 950]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219535
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839882
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211797
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840632
[INFO] [stdout] [Epoch 951]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219535
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918398807
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211797
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918406306
[INFO] [stdout] [Epoch 952]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219535
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918398796
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211797
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918406306
[INFO] [stdout] [Epoch 953]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219535
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918398796
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211797
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918406295
[INFO] [stdout] [Epoch 954]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219535
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918398796
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211797
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840628
[INFO] [stdout] [Epoch 955]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219535
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918398774
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211797
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840627
[INFO] [stdout] [Epoch 956]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121953
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918398774
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211797
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840627
[INFO] [stdout] [Epoch 957]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121953
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918398774
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211797
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918406257
[INFO] [stdout] [Epoch 958]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121953
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839876
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211797
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918406245
[INFO] [stdout] [Epoch 959]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121953
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839875
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211797
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918406234
[INFO] [stdout] [Epoch 960]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121953
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839875
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211797
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918406234
[INFO] [stdout] [Epoch 961]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219524
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839875
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211808
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918406223
[INFO] [stdout] [Epoch 962]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219524
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839874
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211808
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840621
[INFO] [stdout] [Epoch 963]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219524
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839873
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211808
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184062
[INFO] [stdout] [Epoch 964]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219518
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839873
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211808
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840619
[INFO] [stdout] [Epoch 965]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219518
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839873
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211808
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840619
[INFO] [stdout] [Epoch 966]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219518
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839871
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121182
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840618
[INFO] [stdout] [Epoch 967]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219513
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989183987
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121182
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840617
[INFO] [stdout] [Epoch 968]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219513
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989183987
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121182
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918406157
[INFO] [stdout] [Epoch 969]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219507
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989183987
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121182
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918406146
[INFO] [stdout] [Epoch 970]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219507
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839869
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121182
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918406134
[INFO] [stdout] [Epoch 971]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219501
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839869
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121183
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918406123
[INFO] [stdout] [Epoch 972]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219501
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839868
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121183
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840611
[INFO] [stdout] [Epoch 973]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219496
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839868
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121183
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989184061
[INFO] [stdout] [Epoch 974]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219496
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839868
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121184
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840609
[INFO] [stdout] [Epoch 975]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121949
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839868
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121184
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840609
[INFO] [stdout] [Epoch 976]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121949
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839867
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121184
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840608
[INFO] [stdout] [Epoch 977]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219485
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918398657
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211852
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840607
[INFO] [stdout] [Epoch 978]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121948
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918398657
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211852
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918406057
[INFO] [stdout] [Epoch 979]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219485
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918398657
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211852
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918406046
[INFO] [stdout] [Epoch 980]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121948
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918398646
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211863
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918406034
[INFO] [stdout] [Epoch 981]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219474
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918398646
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211863
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918406023
[INFO] [stdout] [Epoch 982]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219474
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918398635
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211863
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918406007
[INFO] [stdout] [Epoch 983]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219468
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918398635
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211874
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918405996
[INFO] [stdout] [Epoch 984]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219463
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918398635
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211874
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918405985
[INFO] [stdout] [Epoch 985]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219457
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918398635
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211885
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840596
[INFO] [stdout] [Epoch 986]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219457
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918398635
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211885
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840595
[INFO] [stdout] [Epoch 987]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219452
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918398635
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211885
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840594
[INFO] [stdout] [Epoch 988]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219446
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918398624
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211896
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840593
[INFO] [stdout] [Epoch 989]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121944
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918398624
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211896
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840592
[INFO] [stdout] [Epoch 990]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219435
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839861
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211908
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918405907
[INFO] [stdout] [Epoch 991]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121943
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839861
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041211908
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918405896
[INFO] [stdout] [Epoch 992]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219424
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839861
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121192
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918405885
[INFO] [stdout] [Epoch 993]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219418
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839861
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121192
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918405874
[INFO] [stdout] [Epoch 994]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219413
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839861
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121193
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840585
[INFO] [stdout] [Epoch 995]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219407
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839861
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121192
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840585
[INFO] [stdout] [Epoch 996]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219413
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839859
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121193
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840584
[INFO] [stdout] [Epoch 997]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219407
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839859
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121192
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840584
[INFO] [stdout] [Epoch 998]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219413
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839859
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121193
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840583
[INFO] [stdout] [Epoch 999]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041219407
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839858
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121192
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891840583
[INFO] [stdout] 
[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:     0x7d88034816b2 - std::backtrace_rs::backtrace::libunwind::trace::h559918daaaf51ab7
[INFO] [stdout]                                at /rustc/8de4c7234dd9b97c9d76b58671343fdbbc9a433e/library/std/src/../../backtrace/src/backtrace/libunwind.rs:117:9
[INFO] [stdout]    1:     0x7d88034816b2 - std::backtrace_rs::backtrace::trace_unsynchronized::hb04fbcf80d07af8b
[INFO] [stdout]                                at /rustc/8de4c7234dd9b97c9d76b58671343fdbbc9a433e/library/std/src/../../backtrace/src/backtrace/mod.rs:66:14
[INFO] [stdout]    2:     0x7d88034816b2 - std::sys::backtrace::_print_fmt::h7c0bbbbfac0065d4
[INFO] [stdout]                                at /rustc/8de4c7234dd9b97c9d76b58671343fdbbc9a433e/library/std/src/sys/backtrace.rs:66:9
[INFO] [stdout]    3:     0x7d88034816b2 - <std::sys::backtrace::BacktraceLock::print::DisplayBacktrace as core::fmt::Display>::fmt::hb62c8ed31943daa5
[INFO] [stdout]                                at /rustc/8de4c7234dd9b97c9d76b58671343fdbbc9a433e/library/std/src/sys/backtrace.rs:39:26
[INFO] [stdout]    4:     0x7d88034bc613 - core::fmt::rt::Argument::fmt::he5eaa7dd607ed4c9
[INFO] [stdout]                                at /rustc/8de4c7234dd9b97c9d76b58671343fdbbc9a433e/library/core/src/fmt/rt.rs:173:76
[INFO] [stdout]    5:     0x7d88034bc613 - core::fmt::write::h375399f8cb90b45a
[INFO] [stdout]                                at /rustc/8de4c7234dd9b97c9d76b58671343fdbbc9a433e/library/core/src/fmt/mod.rs:1460:25
[INFO] [stdout]    6:     0x7d880347e9a3 - std::io::default_write_fmt::hdc1b4dd565dd0099
[INFO] [stdout]                                at /rustc/8de4c7234dd9b97c9d76b58671343fdbbc9a433e/library/std/src/io/mod.rs:639:11
[INFO] [stdout]    7:     0x7d880347e9a3 - std::io::Write::write_fmt::h29f6044e9bc43c23
[INFO] [stdout]                                at /rustc/8de4c7234dd9b97c9d76b58671343fdbbc9a433e/library/std/src/io/mod.rs:1954:13
[INFO] [stdout]    8:     0x7d8803481502 - std::sys::backtrace::BacktraceLock::print::ha189c586374f916a
[INFO] [stdout]                                at /rustc/8de4c7234dd9b97c9d76b58671343fdbbc9a433e/library/std/src/sys/backtrace.rs:42:9
[INFO] [stdout]    9:     0x7d8803482c7c - std::panicking::default_hook::{{closure}}::ha3a26c98ff210e12
[INFO] [stdout]                                at /rustc/8de4c7234dd9b97c9d76b58671343fdbbc9a433e/library/std/src/panicking.rs:300:27
[INFO] [stdout]   10:     0x7d8803482ad2 - std::panicking::default_hook::h8c8a86b4390ab794
[INFO] [stdout]                                at /rustc/8de4c7234dd9b97c9d76b58671343fdbbc9a433e/library/std/src/panicking.rs:324:9
[INFO] [stdout]   11:     0x7d88033ed0f4 - <alloc::boxed::Box<F,A> as core::ops::function::Fn<Args>>::call::ha39ae63ed1e9a130
[INFO] [stdout]                                at /rustc/8de4c7234dd9b97c9d76b58671343fdbbc9a433e/library/alloc/src/boxed.rs:1980:9
[INFO] [stdout]   12:     0x7d88033ed0f4 - test::test_main_with_exit_callback::{{closure}}::h9df7c328ebe18c28
[INFO] [stdout]                                at /rustc/8de4c7234dd9b97c9d76b58671343fdbbc9a433e/library/test/src/lib.rs:145:21
[INFO] [stdout]   13:     0x7d8803483603 - <alloc::boxed::Box<F,A> as core::ops::function::Fn<Args>>::call::h96b7201b552e9069
[INFO] [stdout]                                at /rustc/8de4c7234dd9b97c9d76b58671343fdbbc9a433e/library/alloc/src/boxed.rs:1980:9
[INFO] [stdout]   14:     0x7d8803483603 - std::panicking::rust_panic_with_hook::hb3b66c2e80efa371
[INFO] [stdout]                                at /rustc/8de4c7234dd9b97c9d76b58671343fdbbc9a433e/library/std/src/panicking.rs:841:13
[INFO] [stdout]   15:     0x7d88034833ca - std::panicking::begin_panic_handler::{{closure}}::h9c68d0f839e62070
[INFO] [stdout]                                at /rustc/8de4c7234dd9b97c9d76b58671343fdbbc9a433e/library/std/src/panicking.rs:706:13
[INFO] [stdout]   16:     0x7d8803481ba9 - std::sys::backtrace::__rust_end_short_backtrace::h68d22ddde4a73ad6
[INFO] [stdout]                                at /rustc/8de4c7234dd9b97c9d76b58671343fdbbc9a433e/library/std/src/sys/backtrace.rs:168:18
[INFO] [stdout]   17:     0x7d880348305d - __rustc[f4ffc7196a45a630]::rust_begin_unwind
[INFO] [stdout]                                at /rustc/8de4c7234dd9b97c9d76b58671343fdbbc9a433e/library/std/src/panicking.rs:697:5
[INFO] [stdout]   18:     0x7d880337a550 - core::panicking::panic_fmt::h8cdd4c81eb9069aa
[INFO] [stdout]                                at /rustc/8de4c7234dd9b97c9d76b58671343fdbbc9a433e/library/core/src/panicking.rs:75:14
[INFO] [stdout]   19:     0x7d880337a8a3 - core::panicking::assert_failed_inner::h483faaa3ae844fd5
[INFO] [stdout]                                at /rustc/8de4c7234dd9b97c9d76b58671343fdbbc9a433e/library/core/src/panicking.rs:432:17
[INFO] [stdout]   20:     0x7d880339ba83 - core::panicking::assert_failed::h7bab51bddb0c9735
[INFO] [stdout]                                at /rustc/8de4c7234dd9b97c9d76b58671343fdbbc9a433e/library/core/src/panicking.rs:387:5
[INFO] [stdout]   21:     0x7d8803384ff5 - easynn::models::sequential::test_sequential_xor1::ha1f1dbf3ca8df640
[INFO] [stdout]                                at /opt/rustwide/workdir/src/models/sequential.rs:242:5
[INFO] [stdout]   22:     0x7d8803383a77 - easynn::models::sequential::test_sequential_xor1::{{closure}}::h928d3f0e0502a8a3
[INFO] [stdout]                                at /opt/rustwide/workdir/src/models/sequential.rs:205:26
[INFO] [stdout]   23:     0x7d8803394de6 - core::ops::function::FnOnce::call_once::hc70e2de8c906e3ef
[INFO] [stdout]                                at /rustc/8de4c7234dd9b97c9d76b58671343fdbbc9a433e/library/core/src/ops/function.rs:250:5
[INFO] [stdout]   24:     0x7d88033f27eb - core::ops::function::FnOnce::call_once::h9633dcf760ae81b2
[INFO] [stdout]                                at /rustc/8de4c7234dd9b97c9d76b58671343fdbbc9a433e/library/core/src/ops/function.rs:250:5
[INFO] [stdout]   25:     0x7d88033f27eb - test::__rust_begin_short_backtrace::h12ad5e04c8d7b4a5
[INFO] [stdout]                                at /rustc/8de4c7234dd9b97c9d76b58671343fdbbc9a433e/library/test/src/lib.rs:648:18
[INFO] [stdout]   26:     0x7d88033f1a5e - test::run_test_in_process::{{closure}}::he596b4fd3fa4893c
[INFO] [stdout]                                at /rustc/8de4c7234dd9b97c9d76b58671343fdbbc9a433e/library/test/src/lib.rs:671:74
[INFO] [stdout]   27:     0x7d88033f1a5e - <core::panic::unwind_safe::AssertUnwindSafe<F> as core::ops::function::FnOnce<()>>::call_once::hc746bc3fa75190e0
[INFO] [stdout]                                at /rustc/8de4c7234dd9b97c9d76b58671343fdbbc9a433e/library/core/src/panic/unwind_safe.rs:272:9
[INFO] [stdout]   28:     0x7d88033f1a5e - std::panicking::catch_unwind::do_call::hf4843906108d6299
[INFO] [stdout]                                at /rustc/8de4c7234dd9b97c9d76b58671343fdbbc9a433e/library/std/src/panicking.rs:589:40
[INFO] [stdout]   29:     0x7d88033f1a5e - std::panicking::catch_unwind::h849d4e8b03577bb9
[INFO] [stdout]                                at /rustc/8de4c7234dd9b97c9d76b58671343fdbbc9a433e/library/std/src/panicking.rs:552:19
[INFO] [stdout]   30:     0x7d88033f1a5e - std::panic::catch_unwind::ha1f814c1dec025d2
[INFO] [stdout]                                at /rustc/8de4c7234dd9b97c9d76b58671343fdbbc9a433e/library/std/src/panic.rs:359:14
[INFO] [stdout]   31:     0x7d88033f1a5e - test::run_test_in_process::h542aad3fe61255e7
[INFO] [stdout]                                at /rustc/8de4c7234dd9b97c9d76b58671343fdbbc9a433e/library/test/src/lib.rs:671:27
[INFO] [stdout]   32:     0x7d88033f1a5e - test::run_test::{{closure}}::h612788d75908cb63
[INFO] [stdout]                                at /rustc/8de4c7234dd9b97c9d76b58671343fdbbc9a433e/library/test/src/lib.rs:592:43
[INFO] [stdout]   33:     0x7d88033b68e4 - test::run_test::{{closure}}::hbfeea3bcb7245123
[INFO] [stdout]                                at /rustc/8de4c7234dd9b97c9d76b58671343fdbbc9a433e/library/test/src/lib.rs:622:41
[INFO] [stdout]   34:     0x7d88033b68e4 - std::sys::backtrace::__rust_begin_short_backtrace::h72542b83b4f87d5b
[INFO] [stdout]                                at /rustc/8de4c7234dd9b97c9d76b58671343fdbbc9a433e/library/std/src/sys/backtrace.rs:152:18
[INFO] [stdout]   35:     0x7d88033ba0da - std::thread::Builder::spawn_unchecked_::{{closure}}::{{closure}}::hff26a7547ea762c9
[INFO] [stdout]                                at /rustc/8de4c7234dd9b97c9d76b58671343fdbbc9a433e/library/std/src/thread/mod.rs:559:17
[INFO] [stdout]   36:     0x7d88033ba0da - <core::panic::unwind_safe::AssertUnwindSafe<F> as core::ops::function::FnOnce<()>>::call_once::hbee49bc759312884
[INFO] [stdout]                                at /rustc/8de4c7234dd9b97c9d76b58671343fdbbc9a433e/library/core/src/panic/unwind_safe.rs:272:9
[INFO] [stdout]   37:     0x7d88033ba0da - std::panicking::catch_unwind::do_call::h61de12ce5e04e28f
[INFO] [stdout]                                at /rustc/8de4c7234dd9b97c9d76b58671343fdbbc9a433e/library/std/src/panicking.rs:589:40
[INFO] [stdout]   38:     0x7d88033ba0da - std::panicking::catch_unwind::h454dd49873d22e18
[INFO] [stdout]                                at /rustc/8de4c7234dd9b97c9d76b58671343fdbbc9a433e/library/std/src/panicking.rs:552:19
[INFO] [stdout]   39:     0x7d88033ba0da - std::panic::catch_unwind::h5c15187324f8cebb
[INFO] [stdout]                                at /rustc/8de4c7234dd9b97c9d76b58671343fdbbc9a433e/library/std/src/panic.rs:359:14
[INFO] [stdout]   40:     0x7d88033ba0da - std::thread::Builder::spawn_unchecked_::{{closure}}::hbc9d9c7427673d42
[INFO] [stdout]                                at /rustc/8de4c7234dd9b97c9d76b58671343fdbbc9a433e/library/std/src/thread/mod.rs:557:30
[INFO] [stdout]   41:     0x7d88033ba0da - core::ops::function::FnOnce::call_once{{vtable.shim}}::hefd458a129ddca13
[INFO] [stdout]                                at /rustc/8de4c7234dd9b97c9d76b58671343fdbbc9a433e/library/core/src/ops/function.rs:250:5
[INFO] [stdout]   42:     0x7d88034860a7 - <alloc::boxed::Box<F,A> as core::ops::function::FnOnce<Args>>::call_once::h2d6e2e526b02c3da
[INFO] [stdout]                                at /rustc/8de4c7234dd9b97c9d76b58671343fdbbc9a433e/library/alloc/src/boxed.rs:1966:9
[INFO] [stdout]   43:     0x7d88034860a7 - <alloc::boxed::Box<F,A> as core::ops::function::FnOnce<Args>>::call_once::h1aefced482b33c72
[INFO] [stdout]                                at /rustc/8de4c7234dd9b97c9d76b58671343fdbbc9a433e/library/alloc/src/boxed.rs:1966:9
[INFO] [stdout]   44:     0x7d88034860a7 - std::sys::pal::unix::thread::Thread::new::thread_start::h7c9c6951b48f721d
[INFO] [stdout]                                at /rustc/8de4c7234dd9b97c9d76b58671343fdbbc9a433e/library/std/src/sys/pal/unix/thread.rs:97:17
[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 1.86s
[INFO] [stdout] 
[INFO] running `Command { std: "docker" "inspect" "086b1c87f0cf87897b988f0ede44e926caddf81db5897898cb64f5dd89a257d3", kill_on_drop: false }`
[INFO] running `Command { std: "docker" "rm" "-f" "086b1c87f0cf87897b988f0ede44e926caddf81db5897898cb64f5dd89a257d3", kill_on_drop: false }`
[INFO] [stdout] 086b1c87f0cf87897b988f0ede44e926caddf81db5897898cb64f5dd89a257d3