[INFO] fetching crate easynn 0.1.7-beta...
[INFO] testing easynn-0.1.7-beta against try#b6ae10aa7319b7ebb6c7b3331dd71a9d6c9c5b4b for pr-145330-1
[INFO] extracting crate easynn 0.1.7-beta into /workspace/builds/worker-7-tc2/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-7-tc2/source/Cargo.toml
[INFO] validating manifest of crates.io crate easynn 0.1.7-beta on toolchain b6ae10aa7319b7ebb6c7b3331dd71a9d6c9c5b4b
[INFO] running `Command { std: CARGO_HOME="/workspace/cargo-home" RUSTUP_HOME="/workspace/rustup-home" "/workspace/cargo-home/bin/cargo" "+b6ae10aa7319b7ebb6c7b3331dd71a9d6c9c5b4b" "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" "+b6ae10aa7319b7ebb6c7b3331dd71a9d6c9c5b4b" "generate-lockfile" "--manifest-path" "Cargo.toml", kill_on_drop: false }`
[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: v0.9.2)
[INFO] running `Command { std: CARGO_HOME="/workspace/cargo-home" RUSTUP_HOME="/workspace/rustup-home" "/workspace/cargo-home/bin/cargo" "+b6ae10aa7319b7ebb6c7b3331dd71a9d6c9c5b4b" "fetch" "--manifest-path" "Cargo.toml", kill_on_drop: false }`
[INFO] running `Command { std: "docker" "create" "-v" "/var/lib/crater-agent-workspace/builds/worker-7-tc2/target:/opt/rustwide/target:rw,Z" "-v" "/var/lib/crater-agent-workspace/builds/worker-7-tc2/source:/opt/rustwide/workdir:ro,Z" "-v" "/var/lib/crater-agent-workspace/cargo-home:/opt/rustwide/cargo-home:ro,Z" "-v" "/var/lib/crater-agent-workspace/rustup-home:/opt/rustwide/rustup-home:ro,Z" "-e" "SOURCE_DIR=/opt/rustwide/workdir" "-e" "CARGO_TARGET_DIR=/opt/rustwide/target" "-e" "CARGO_HOME=/opt/rustwide/cargo-home" "-e" "RUSTUP_HOME=/opt/rustwide/rustup-home" "-w" "/opt/rustwide/workdir" "-m" "1610612736" "--user" "0:0" "--network" "none" "ghcr.io/rust-lang/crates-build-env/linux@sha256:e90291280db7d1fac5b66fc6dad9f9662629e7365a55743daf9bdf73ebc4ea79" "/opt/rustwide/cargo-home/bin/cargo" "+b6ae10aa7319b7ebb6c7b3331dd71a9d6c9c5b4b" "metadata" "--no-deps" "--format-version=1", kill_on_drop: false }`
[INFO] [stdout] 42f46780a6c02dc6ae21179e8fd8ec1c5a1043e9d4ce6c98e35e60347d32280a
[INFO] running `Command { std: "docker" "start" "-a" "42f46780a6c02dc6ae21179e8fd8ec1c5a1043e9d4ce6c98e35e60347d32280a", kill_on_drop: false }`
[INFO] running `Command { std: "docker" "inspect" "42f46780a6c02dc6ae21179e8fd8ec1c5a1043e9d4ce6c98e35e60347d32280a", kill_on_drop: false }`
[INFO] running `Command { std: "docker" "rm" "-f" "42f46780a6c02dc6ae21179e8fd8ec1c5a1043e9d4ce6c98e35e60347d32280a", kill_on_drop: false }`
[INFO] [stdout] 42f46780a6c02dc6ae21179e8fd8ec1c5a1043e9d4ce6c98e35e60347d32280a
[INFO] running `Command { std: "docker" "create" "-v" "/var/lib/crater-agent-workspace/builds/worker-7-tc2/target:/opt/rustwide/target:rw,Z" "-v" "/var/lib/crater-agent-workspace/builds/worker-7-tc2/source:/opt/rustwide/workdir:ro,Z" "-v" "/var/lib/crater-agent-workspace/cargo-home:/opt/rustwide/cargo-home:ro,Z" "-v" "/var/lib/crater-agent-workspace/rustup-home:/opt/rustwide/rustup-home:ro,Z" "-e" "SOURCE_DIR=/opt/rustwide/workdir" "-e" "CARGO_TARGET_DIR=/opt/rustwide/target" "-e" "CARGO_INCREMENTAL=0" "-e" "RUST_BACKTRACE=full" "-e" "RUSTFLAGS=--cap-lints=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:e90291280db7d1fac5b66fc6dad9f9662629e7365a55743daf9bdf73ebc4ea79" "/opt/rustwide/cargo-home/bin/cargo" "+b6ae10aa7319b7ebb6c7b3331dd71a9d6c9c5b4b" "build" "--frozen" "--message-format=json", kill_on_drop: false }`
[INFO] [stdout] f7b37dc7a74816cc9d408b0caf779daebc1d60e8810ba61ccda5bee816b10ebc
[INFO] running `Command { std: "docker" "start" "-a" "f7b37dc7a74816cc9d408b0caf779daebc1d60e8810ba61ccda5bee816b10ebc", kill_on_drop: false }`
[INFO] [stderr]    Compiling crossbeam-utils v0.8.21
[INFO] [stderr]    Compiling zerocopy v0.8.27
[INFO] [stderr]    Compiling autocfg v1.5.0
[INFO] [stderr]    Compiling rayon-core v1.13.0
[INFO] [stderr]    Compiling itertools v0.10.5
[INFO] [stderr]    Compiling getrandom v0.2.16
[INFO] [stderr]    Compiling num_cpus v1.17.0
[INFO] [stderr]    Compiling rand_core v0.6.4
[INFO] [stderr]    Compiling num-traits v0.2.19
[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 crossbeam-deque v0.8.6
[INFO] [stderr]    Compiling crossbeam v0.8.4
[INFO] [stderr]    Compiling rayon v1.11.0
[INFO] [stderr]    Compiling ppv-lite86 v0.2.21
[INFO] [stderr]    Compiling rand_chacha v0.3.1
[INFO] [stderr]    Compiling rand v0.8.5
[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 11.87s
[INFO] running `Command { std: "docker" "inspect" "f7b37dc7a74816cc9d408b0caf779daebc1d60e8810ba61ccda5bee816b10ebc", kill_on_drop: false }`
[INFO] running `Command { std: "docker" "rm" "-f" "f7b37dc7a74816cc9d408b0caf779daebc1d60e8810ba61ccda5bee816b10ebc", kill_on_drop: false }`
[INFO] [stdout] f7b37dc7a74816cc9d408b0caf779daebc1d60e8810ba61ccda5bee816b10ebc
[INFO] running `Command { std: "docker" "create" "-v" "/var/lib/crater-agent-workspace/builds/worker-7-tc2/target:/opt/rustwide/target:rw,Z" "-v" "/var/lib/crater-agent-workspace/builds/worker-7-tc2/source:/opt/rustwide/workdir:ro,Z" "-v" "/var/lib/crater-agent-workspace/cargo-home:/opt/rustwide/cargo-home:ro,Z" "-v" "/var/lib/crater-agent-workspace/rustup-home:/opt/rustwide/rustup-home:ro,Z" "-e" "SOURCE_DIR=/opt/rustwide/workdir" "-e" "CARGO_TARGET_DIR=/opt/rustwide/target" "-e" "CARGO_INCREMENTAL=0" "-e" "RUST_BACKTRACE=full" "-e" "RUSTFLAGS=--cap-lints=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:e90291280db7d1fac5b66fc6dad9f9662629e7365a55743daf9bdf73ebc4ea79" "/opt/rustwide/cargo-home/bin/cargo" "+b6ae10aa7319b7ebb6c7b3331dd71a9d6c9c5b4b" "test" "--frozen" "--no-run" "--message-format=json", kill_on_drop: false }`
[INFO] [stdout] 41518bb9628fd909198754119dea78a05bdc78cd9a560b5fd71cc345371b4679
[INFO] running `Command { std: "docker" "start" "-a" "41518bb9628fd909198754119dea78a05bdc78cd9a560b5fd71cc345371b4679", kill_on_drop: false }`
[INFO] [stdout] warning: unused variable: `olen`
[INFO] [stdout]   --> src/layers/dense.rs:96:13
[INFO] [stdout]    |
[INFO] [stdout] 96 |         let olen = output.flattened.len();
[INFO] [stdout]    |             ^^^^ help: if this is intentional, prefix it with an underscore: `_olen`
[INFO] [stdout]    |
[INFO] [stdout]    = note: `#[warn(unused_variables)]` on by default
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: unused variable: `dlen`
[INFO] [stdout]    --> src/layers/dense.rs:148:13
[INFO] [stdout]     |
[INFO] [stdout] 148 |         let dlen = delta.flattened.len();
[INFO] [stdout]     |             ^^^^ help: if this is intentional, prefix it with an underscore: `_dlen`
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: unused variable: `dlen`
[INFO] [stdout]    --> src/layers/dense.rs:205:13
[INFO] [stdout]     |
[INFO] [stdout] 205 |         let dlen = delta.flattened.len();
[INFO] [stdout]     |             ^^^^ help: if this is intentional, prefix it with an underscore: `_dlen`
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: variable does not need to be mutable
[INFO] [stdout]    --> src/models/sequential.rs:137:17
[INFO] [stdout]     |
[INFO] [stdout] 137 |             let mut cum_dw = &dws.par_chunks_mut(1).reduce_with(|dw1, dw2| {
[INFO] [stdout]     |                 ----^^^^^^
[INFO] [stdout]     |                 |
[INFO] [stdout]     |                 help: remove this `mut`
[INFO] [stdout]     |
[INFO] [stdout]     = note: `#[warn(unused_mut)]` on by default
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stderr]    Compiling easynn v0.1.7-beta (/opt/rustwide/workdir)
[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 0.97s
[INFO] running `Command { std: "docker" "inspect" "41518bb9628fd909198754119dea78a05bdc78cd9a560b5fd71cc345371b4679", kill_on_drop: false }`
[INFO] running `Command { std: "docker" "rm" "-f" "41518bb9628fd909198754119dea78a05bdc78cd9a560b5fd71cc345371b4679", kill_on_drop: false }`
[INFO] [stdout] 41518bb9628fd909198754119dea78a05bdc78cd9a560b5fd71cc345371b4679
[INFO] running `Command { std: "docker" "create" "-v" "/var/lib/crater-agent-workspace/builds/worker-7-tc2/target:/opt/rustwide/target:rw,Z" "-v" "/var/lib/crater-agent-workspace/builds/worker-7-tc2/source:/opt/rustwide/workdir:ro,Z" "-v" "/var/lib/crater-agent-workspace/cargo-home:/opt/rustwide/cargo-home:ro,Z" "-v" "/var/lib/crater-agent-workspace/rustup-home:/opt/rustwide/rustup-home:ro,Z" "-e" "SOURCE_DIR=/opt/rustwide/workdir" "-e" "CARGO_TARGET_DIR=/opt/rustwide/target" "-e" "CARGO_INCREMENTAL=0" "-e" "RUST_BACKTRACE=full" "-e" "RUSTFLAGS=--cap-lints=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:e90291280db7d1fac5b66fc6dad9f9662629e7365a55743daf9bdf73ebc4ea79" "/opt/rustwide/cargo-home/bin/cargo" "+b6ae10aa7319b7ebb6c7b3331dd71a9d6c9c5b4b" "test" "--frozen", kill_on_drop: false }`
[INFO] [stdout] 4d878cd55fa22989bd57c9434777cbcf780dc508fb33eedf423d9bad7eb6a5ee
[INFO] running `Command { std: "docker" "start" "-a" "4d878cd55fa22989bd57c9434777cbcf780dc508fb33eedf423d9bad7eb6a5ee", 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 0.06s
[INFO] [stderr]      Running unittests src/lib.rs (/opt/rustwide/target/debug/deps/easynn-867cd2ac072b2970)
[INFO] [stdout] 
[INFO] [stdout] running 7 tests
[INFO] [stdout] test layers::dense::test_add_weight_delta_to ... ok
[INFO] [stdout] test layers::dense::test_dense_activate ... ok
[INFO] [stdout] test layers::dense::test_dense_descend ... ok
[INFO] [stdout] test models::sequential::test_sequential_predict ... ok
[INFO] [stdout] test layers::dense::test_dense_forward ... 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
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 1
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.9604007360760156
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.0015681308515308192
[INFO] [stdout] [Epoch 1]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.0015060328698101987
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.9253834208954357
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.8887382374279764
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.005795082564878488
[INFO] [stdout] [Epoch 2]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.0055655972953093
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.8591234551251915
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.8251021663022339
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.012059483951006953
[INFO] [stdout] [Epoch 3]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.011581928386547077
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.8001893225800982
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.7685018254059263
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.01985012968907652
[INFO] [stdout] [Epoch 4]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.019064064553389094
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.7476866776999913
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.7180782852630717
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.028748186047813092
[INFO] [stdout] [Epoch 5]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.02760975788031969
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.7008392003019939
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.6730859679700348
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.0384121572425026
[INFO] [stdout] [Epoch 6]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.0368910358156995
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.6589721807800382
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.6328768824211487
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.04856519228194336
[INFO] [stdout] [Epoch 7]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.04664201066757841
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.6214984619228952
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.5968871228307485
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.05898437549405702
[INFO] [stdout] [Epoch 8]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.05664859422449236
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.5879063928818016
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.5646252997236823
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.06949169775237385
[INFO] [stdout] [Epoch 9]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.06673982652137984
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.5577495016653345
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.5356626213993874
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.07994645117982858
[INFO] [stdout] [Epoch 10]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.07678057171310737
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.5306376358410906
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.5096243854617832
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.09023882899048029
[INFO] [stdout] [Epoch 11]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.08666537136245725
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.5062293580035626
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.48618267542662136
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.1002845451724122
[INFO] [stdout] [Epoch 12]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.09631327718358468
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.48422541398066
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.4650500875870258
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.11002031679094676
[INFO] [stdout] [Epoch 13]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.10566351224602527
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.4643631185105414
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.4459743390175239
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.11940007554440982
[INFO] [stdout] [Epoch 14]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.11467183255285118
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.4464115259177168
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.4287336294913753
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.128391795467743
[INFO] [stdout] [Epoch 15]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.12330748036722036
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.43016727274153965
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.4131326487409747
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.1369748408904066
[INFO] [stdout] [Epoch 16]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.1315506371911465
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.41545099582236966
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.3989991363878038
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.14513775337186835
[INFO] [stdout] [Epoch 17]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.13939029833834238
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.40210424345740925
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.38618091541649574
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.1528764087496947
[INFO] [stdout] [Epoch 18]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.14682250296320676
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.3899868092624859
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.37454333161569153
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.1601924859727862
[INFO] [stdout] [Epoch 19]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.15384886352826385
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.378974428626916
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.36396704125329005
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.16709219833702557
[INFO] [stdout] [Epoch 20]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.16047534728287938
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.36895678638903917
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.3543460976480333
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.17358524533180833
[INFO] [stdout] [Epoch 21]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.16671126961666874
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.3598357918139766
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.3455862944581432
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.17968394974704818
[INFO] [stdout] [Epoch 22]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.1725684653370651
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.3515240833132278
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.3376037296140241
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.1854025501542572
[INFO] [stdout] [Epoch 23]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.17806060916814861
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.3439437307701662
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.33032355903166755
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.19075662350925032
[INFO] [stdout] [Epoch 24]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.183202661218284
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.3370251079644467
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.3236789136890545
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.19576261655292537
[INFO] [stdout] [Epoch 25]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.1880104169374295
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.33070591153944984
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.3176099574424876
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.20043746801668716
[INFO] [stdout] [Epoch 26]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.19250014428322637
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.3249303063302899
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.31206306619961044
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.20479830646074942
[INFO] [stdout] [Epoch 27]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.19668829352490372
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.31964817975090665
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.30699011183277086
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.20886221096352767
[INFO] [stdout] [Epoch 28]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.200591267409372
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.31481449039997583
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.30234783658013686
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.21264602390387705
[INFO] [stdout] [Epoch 29]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.20422524135728348
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.3103886981486531
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.29809730570196635
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2161662067904756
[INFO] [stdout] [Epoch 30]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.20760602500157277
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.3063342647712179
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2942034278862778
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.21943873154131807
[INFO] [stdout] [Epoch 31]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.21074895777228186
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.3026182157173763
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2906345343749681
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.22247900084108863
[INFO] [stdout] [Epoch 32]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2136688324077815
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.299210754940548
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.28736200904490233
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.22530179223913566
[INFO] [stdout] [Epoch 33]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2163798412664659
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.29608492582249785
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.284359962759927
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.22792122152474908
[INFO] [stdout] [Epoch 34]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.218895541152369
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2932163121989086
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.28160494623583177
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.23035072165397596
[INFO] [stdout] [Epoch 35]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.22122883307647853
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.29058277431661445
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2790756964536766
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.23260303412413308
[INFO] [stdout] [Epoch 36]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.22339195397281741
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.28816421526137265
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.27675291233702226
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.23469021021616623
[INFO] [stdout] [Epoch 37]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.22539647789160605
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.28594237400243294
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.27461905599193653
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2366236199660807
[INFO] [stdout] [Epoch 38]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.22725332461542389
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.28390064172144863
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.27265817630927924
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2384139670975638
[INFO] [stdout] [Epoch 39]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.22897277400050026
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.28202389854092574
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.27085575215870505
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.24007130845945254
[INFO] [stdout] [Epoch 40]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.23056448464445822
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.28029836815212017
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2691985527732963
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.24160507677303467
[INFO] [stdout] [Epoch 41]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2320375157328225
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2787114881731484
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2676745132414917
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.24302410571309144
[INFO] [stdout] [Epoch 42]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.233400351126853
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2772517943528271
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2662726232964551
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.24433665652968967
[INFO] [stdout] [Epoch 43]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.23466092493111398
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.27590881698105224
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2649828278286026
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.24555044557059705
[INFO] [stdout] [Epoch 44]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2358266479260014
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.27467298807799534
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2637959377501068
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2466726721915695
[INFO] [stdout] [Epoch 45]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.23690443437278336
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.27353555811687824
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.26270355001544987
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.24771004664765314
[INFO] [stdout] [Epoch 46]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.23790072880040605
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2724885211927224
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.26169797575349063
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2486688176464513
[INFO] [stdout] [Epoch 47]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.23882153246765184
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.27152454768575274
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.26077217559739696
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.24955479931691135
[INFO] [stdout] [Epoch 48]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.23967242926396165
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.27063692358611496
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2599197014121049
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2503733974070111
[INFO] [stdout] [Epoch 49]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24045861086969347
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2698194957487927
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25913464371714057
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2511296345728364
[INFO] [stdout] [Epoch 50]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24118490104375204
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26906662243630197
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25841158418782434
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25182817466166973
[INFO] [stdout] [Epoch 51]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24185577894506766
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2683731285837839
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25774555269186616
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2524733459243309
[INFO] [stdout] [Epoch 52]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24247540142572743
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2677342652881207
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2571319883827112
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25306916311835426
[INFO] [stdout] [Epoch 53]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24304762425886745
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2671456730810515
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2565667044270419
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25361934848471207
[INFO] [stdout] [Epoch 54]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24357602228471745
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2666033485971597
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2560458559927121
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2541273515975624
[INFO] [stdout] [Epoch 55]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24406390847429896
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2661036142920406
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2555659111660758
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2545963680996709
[INFO] [stdout] [Epoch 56]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2445143519229239
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.265643090904829
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2551236245049978
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2550293573463419
[INFO] [stdout] [Epoch 57]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24493019479542674
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26521867239330693
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25471601296653196
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2554290589884246
[INFO] [stdout] [Epoch 58]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24531406825248303
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2648275030996779
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2543403339769307
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2557980085306708
[INFO] [stdout] [Epoch 59]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24566840739285623
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2644669569313299
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2539940654368492
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25613855190578
[INFO] [stdout] [Epoch 60]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24599546525031107
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2641346183639939
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2536748874767798
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25645285910719284
[INFO] [stdout] [Epoch 61]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24629732588654804
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.263828265095057
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2533806657972929
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2567429369253229
[INFO] [stdout] [Epoch 62]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24657591662308015
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2635458521927499
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25310943644591705
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25701064083268554
[INFO] [stdout] [Epoch 63]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2468330194557112
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26328549760281117
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25285939189774
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25725768606345795
[INFO] [stdout] [Epoch 64]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24707028169534503
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2630454688883017
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25262886832032483
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2574856579325323
[INFO] [stdout] [Epoch 65]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24728922587840402
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2628241710907155
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2524163339155232
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25769602143823317
[INFO] [stdout] [Epoch 66]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24749125898927915
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2626201356116312
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2522203782414105
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.257890130191665
[INFO] [stdout] [Epoch 67]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24767768103607507
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2624320100240054
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2520397024270548
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25806923471420007
[INFO] [stdout] [Epoch 68]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24784969301951779
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26225854873101717
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2518731102012689
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25823449014300714
[INFO] [stdout] [Epoch 69]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2480084043333441
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2620986043982212
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2517194996640517
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25838696338278616
[INFO] [stdout] [Epoch 70]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24815483963282783
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26195112009179194
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.251577855736157
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2585276397400747
[INFO] [stdout] [Epoch 71]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2482899452063677
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26181512206193236
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2514472432282799
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2586574290746583
[INFO] [stdout] [Epoch 72]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24841459488330184
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2616897131161622
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2513268004767622
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25877717150077684
[INFO] [stdout] [Epoch 73]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24852959550934606
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26157406653226817
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2512157334975902
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2588876426689952
[INFO] [stdout] [Epoch 74]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.248635692019303
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26146742046525834
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25111331061483405
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25898955865782386
[INFO] [stdout] [Epoch 75]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24873357213497402
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2613690728067679
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25101885752361996
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2590835805024279
[INFO] [stdout] [Epoch 76]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24882387071453177
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26127837645906804
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25093175275128893
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2591703183860895
[INFO] [stdout] [Epoch 77]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24890717377800034
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26119473498917245
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25085142348360134
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25925033551846377
[INFO] [stdout] [Epoch 78]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24898402223193264
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2611175986315656
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2507773417257556
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25932415172312684
[INFO] [stdout] [Epoch 79]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24905491531489105
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2610464606108067
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25070902077061874
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2593922467554353
[INFO] [stdout] [Epoch 80]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24912031378392005
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26098085375775426
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2506460119489472
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25945506337031865
[INFO] [stdout] [Epoch 81]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24918064286085403
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26092034739539866
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25058790163854094
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25951301015829675
[INFO] [stdout] [Epoch 82]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24923629495602823
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26086454447234325
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2505343085112385
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25956646416676404
[INFO] [stdout] [Epoch 83]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2492876321857602
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26081307892382727
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2504848809984437
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25961577332239794
[INFO] [stdout] [Epoch 84]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249334988698831
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2607656132418798
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25043929495750133
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2596612586694445
[INFO] [stdout] [Epoch 85]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24937867282613452
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2607218362377328
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25039725152271863
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.259703216437584
[INFO] [stdout] [Epoch 86]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24941896906665564
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2606814609810277
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25035847512617904
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2597419199521114
[INFO] [stdout] [Epoch 87]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24945613992200777
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260644222901628
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25032271167472353
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25977762139824584
[INFO] [stdout] [Epoch 88]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24949042759087528
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2606098780410265
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25028972687060186
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2598105534505331
[INFO] [stdout] [Epoch 89]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24952205553389198
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26057820144139743
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25025930466431806
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2598409307775068
[INFO] [stdout] [Epoch 90]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24955122991871753
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2605489856613252
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25023124582913686
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25986895143103067
[INFO] [stdout] [Epoch 91]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24957814095436184
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26052203940813584
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25020536664757365
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2598947981290515
[INFO] [stdout] [Epoch 92]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249602964123141
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26049718627757146
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2501814977009797
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2599186394398489
[INFO] [stdout] [Epoch 93]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24962586131803083
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2604742635923028
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25015948275404754
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25994063087526703
[INFO] [stdout] [Epoch 94]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24964698189260645
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2604531213314554
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2501391777267297
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2599609158998583
[INFO] [stdout] [Epoch 95]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24966646363022393
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2604336211439597
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.250120449746659
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2599796268623489
[INFO] [stdout] [Epoch 96]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24968443363859988
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26041563543910967
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25010317627572093
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2599968858553589
[INFO] [stdout] [Epoch 97]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24970100917548668
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2603990465482431
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2500872443049327
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2600128055088614
[INFO] [stdout] [Epoch 98]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2497162984107105
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26038374595194713
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25007254961225006
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2600274897224529
[INFO] [stdout] [Epoch 99]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24973040112944372
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2603696335676338
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25005899607835547
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26004103434112397
[INFO] [stdout] [Epoch 100]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24974340938121545
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260356617092744
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2500464950558713
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2600535277788659
[INFO] [stdout] [Epoch 101]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24975540807882282
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2603446113992117
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25003496478780296
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26006505159411597
[INFO] [stdout] [Epoch 102]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249766475550989
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2603335379751714
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2500243298713546
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26007568102074513
[INFO] [stdout] [Epoch 103]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24977668405232362
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26032332441020234
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25001452076355823
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2600854854580062
[INFO] [stdout] [Epoch 104]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24978610023386916
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26031390392070386
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25000547332544404
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2600945289226019
[INFO] [stdout] [Epoch 105]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24979478557726684
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26030521491225905
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24999712840173366
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26010287046578945
[INFO] [stdout] [Epoch 106]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24980279679534417
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602972005760952
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24998943143328176
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26011056455821757
[INFO] [stdout] [Epoch 107]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498101862017122
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26028980851697453
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24998233209970222
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601176614449825
[INFO] [stdout] [Epoch 108]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498170020517612
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26028299041006153
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24997578398982312
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26012420747320214
[INFO] [stdout] [Epoch 109]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24982328885726332
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260276701684503
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499697442977967
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601302453942297
[INFO] [stdout] [Epoch 110]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498290876766182
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26027090123163515
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499641735428624
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601358146424637
[INFO] [stdout] [Epoch 111]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24983443638262215
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602655511358987
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24995903531091715
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260140951592564
[INFO] [stdout] [Epoch 112]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24983936990949843
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602606164266896
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24995429601619262
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26014568979674224
[INFO] [stdout] [Epoch 113]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24984392048079124
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602560648495115
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24994992468147098
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26015006020366743
[INFO] [stdout] [Epoch 114]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498481178196022
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26025186665493066
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499458927353955
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26015409136040807
[INFO] [stdout] [Epoch 115]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498519893425359
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26024799440393964
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24994217382554365
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26015780959872287
[INFO] [stdout] [Epoch 116]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24985556033861342
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602444227884578
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499387436460349
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26016123920691286
[INFO] [stdout] [Epoch 117]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498588541343191
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602411284657859
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24993557977854072
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601644025883501
[INFO] [stdout] [Epoch 118]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24986189224585142
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602380899059301
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24993266154565533
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26016732040771623
[INFO] [stdout] [Epoch 119]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24986469451957066
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602352872507939
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24992996987566246
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26017001172590226
[INFO] [stdout] [Epoch 120]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498672792615565
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26023270218431255
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24992748717781374
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601724941244467
[INFO] [stdout] [Epoch 121]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24986966335711863
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602303178126817
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24992519722729953
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26017478382032383
[INFO] [stdout] [Epoch 122]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24987186238103903
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602281185538922
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24992308505915814
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26017689577182945
[INFO] [stdout] [Epoch 123]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24987389069926505
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26022609003584835
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24992113687042875
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26017884377625317
[INFO] [stdout] [Epoch 124]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24987576156271357
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602242190024021
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24991933992990692
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26018064055997453
[INFO] [stdout] [Epoch 125]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498774871937995
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602224932266882
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499176824949115
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26018229786156993
[INFO] [stdout] [Epoch 126]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24987907886625177
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26022090143119203
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24991615373451695
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601838265084727
[INFO] [stdout] [Epoch 127]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24988054697873716
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602194332140256
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24991474365875013
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601852364876848
[INFO] [stdout] [Epoch 128]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498819011227725
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602180789809313
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499134430532864
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601865370110034
[INFO] [stdout] [Epoch 129]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498831501453677
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260216829882568
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24991224341921833
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26018773657518546
[INFO] [stdout] [Epoch 130]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498843022068081
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26021567775666693
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24991113691750297
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601888430174444
[INFO] [stdout] [Epoch 131]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498853648339536
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602146150746807
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499101163177233
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601898635666407
[INFO] [stdout] [Epoch 132]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498863449694017
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26021363489257604
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499091749508301
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601908048904994
[INFO] [stdout] [Epoch 133]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24988724901683565
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602127308054486
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990830666555278
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601916731391646
[INFO] [stdout] [Epoch 134]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24988808288285366
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602118969056607
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990750578819654
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601924739853726
[INFO] [stdout] [Epoch 135]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24988885201555186
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602111277442324
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990676708556073
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26019321266150863
[INFO] [stdout] [Epoch 136]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498895614401129
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602104182952287
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990608573073775
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601938939937866
[INFO] [stdout] [Epoch 137]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989021579163267
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602097639229143
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990545727156682
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601945224337771
[INFO] [stdout] [Epoch 138]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989081934539958
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020916035145586
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990487760153818
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26019510208748775
[INFO] [stdout] [Epoch 139]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989137604482323
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260208603636981
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499043429329565
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26019563674218665
[INFO] [stdout] [Epoch 140]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989188952719601
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602080901418031
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499038497721877
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601961298911445
[INFO] [stdout] [Epoch 141]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989236314745522
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602076165106499
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990339489682814
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601965847564557
[INFO] [stdout] [Epoch 142]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989280000010003
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602071796487368
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499029753346468
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601970043100883
[INFO] [stdout] [Epoch 143]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989320293940884
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020677670154285
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990258834416176
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601973912933004
[INFO] [stdout] [Epoch 144]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498935745980857
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602064050361577
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990223139672585
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601977482345487
[INFO] [stdout] [Epoch 145]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498939174044606
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602060622240756
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990190216000216
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26019807746600826
[INFO] [stdout] [Epoch 146]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498942335983543
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602057460253264
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990159848272345
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601983811388084
[INFO] [stdout] [Epoch 147]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498945252457116
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020545437383824
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990131838063426
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601986612370874
[INFO] [stdout] [Epoch 148]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989479425209876
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020518536393666
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990106002352483
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26019891959095537
[INFO] [stdout] [Epoch 149]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989504237515353
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260204937237892
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990082172327152
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601991578884508
[INFO] [stdout] [Epoch 150]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989527123606817
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602047083744336
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990060192280614
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26019937768657
[INFO] [stdout] [Epoch 151]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498954823301818
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260204497278156
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.249900399185941
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260199580421439
[INFO] [stdout] [Epoch 152]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989567703675003
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020430256974664
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499002121879846
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601997674176972
[INFO] [stdout] [Epoch 153]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989585662795638
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602041229769738
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990003970708577
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26019993989715134
[INFO] [stdout] [Epoch 154]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989602227722416
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602039573263735
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989988061624913
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602000989867587
[INFO] [stdout] [Epoch 155]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989617506688308
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602038045355808
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989973387597186
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020024572599026
[INFO] [stdout] [Epoch 156]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989631599524104
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020366360625835
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989959852745056
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602003810736219
[INFO] [stdout] [Epoch 157]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989644598310648
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602035336175723
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989947368631638
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602005059139993
[INFO] [stdout] [Epoch 158]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498965658798049
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602034137201758
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989935853685685
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020062106281483
[INFO] [stdout] [Epoch 159]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989667646872737
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020330313065937
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989925232668522
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020072727243865
[INFO] [stdout] [Epoch 160]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989677847245004
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602032011264314
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989915436182475
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602008252368331
[INFO] [stdout] [Epoch 161]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498968725574545
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020310704099703
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989906400217357
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020091559608777
[INFO] [stdout] [Epoch 162]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498969593384827
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602030202596031
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989898065732283
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602009989406011
[INFO] [stdout] [Epoch 163]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498970393825533
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602029402152214
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989890378269852
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602010758149384
[INFO] [stdout] [Epoch 164]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989711321266686
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602028663848431
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989883287600334
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020114672138944
[INFO] [stdout] [Epoch 165]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989718131122243
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020279828606224
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498987674739342
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602012121232508
[INFO] [stdout] [Epoch 166]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989724412317005
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020273547392303
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498987071491557
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602012724478526
[INFO] [stdout] [Epoch 167]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989730205891766
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602026775380124
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989865150750717
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602013280893508
[INFO] [stdout] [Epoch 168]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498973554970125
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020262409977896
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989860018542764
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602013794113024
[INFO] [stdout] [Epoch 169]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989740478661485
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602025748100586
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989855284758028
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020142674904095
[INFO] [stdout] [Epoch 170]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498974502497789
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020252934679405
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498985091846611
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020147041186753
[INFO] [stdout] [Epoch 171]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989749218355756
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020248741293006
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.249898468911378
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020151068507186
[INFO] [stdout] [Epoch 172]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249897530861943
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260202448734472
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989843176458684
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020154783179594
[INFO] [stdout] [Epoch 173]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989756653765682
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020241305869635
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989839750157192
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602015820947539
[INFO] [stdout] [Epoch 174]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989759944380166
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020238015249897
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989836589846
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602016136978173
[INFO] [stdout] [Epoch 175]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989762979538374
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602023498008721
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989833674875764
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602016428474785
[INFO] [stdout] [Epoch 176]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498976577907183
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020232180549957
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498983098620017
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602016697341992
[INFO] [stdout] [Epoch 177]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989768361272494
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020229598346045
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989828506251538
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602016945336557
[INFO] [stdout] [Epoch 178]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989770743012296
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602022721660348
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989826218825986
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020171740788584
[INFO] [stdout] [Epoch 179]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989772939853358
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602022501976009
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498982410897758
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020173850634826
[INFO] [stdout] [Epoch 180]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498977496614969
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020222993461756
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989822162920675
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020175796689893
[INFO] [stdout] [Epoch 181]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989776835140975
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020221124468784
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989820367939816
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020177591669186
[INFO] [stdout] [Epoch 182]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989778559039086
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602021940056922
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989818712306683
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020179247300984
[INFO] [stdout] [Epoch 183]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989780149107868
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602021781049921
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989817185203442
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020180774403096
[INFO] [stdout] [Epoch 184]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989781615736734
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020216343869296
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989815776652075
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020182182953505
[INFO] [stdout] [Epoch 185]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989782968508545
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602021499109661
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989814477449176
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602018348215558
[INFO] [stdout] [Epoch 186]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989784216262217
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020213743342174
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498981327910583
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602018468049823
[INFO] [stdout] [Epoch 187]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989785367150505
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602021259245324
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989812173792098
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602018578581137
[INFO] [stdout] [Epoch 188]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989786428693242
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020211530909954
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498981115428592
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020186805317036
[INFO] [stdout] [Epoch 189]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498978740782648
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020210551776257
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498981021392592
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602018774567661
[INFO] [stdout] [Epoch 190]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498978831094782
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020964865452
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.249898093465678
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602018861303436
[INFO] [stdout] [Epoch 191]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989789143958202
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020881564379
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498980854654431
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602018941305755
[INFO] [stdout] [Epoch 192]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989789912300472
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020208047301246
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989807808628117
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019015097347
[INFO] [stdout] [Epoch 193]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989790620994926
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020733860655
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989807127997732
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019083160363
[INFO] [stdout] [Epoch 194]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989791274672127
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020206684929137
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989806500205938
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019145939524
[INFO] [stdout] [Epoch 195]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989791877603185
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020206081997904
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989805921150787
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020192038450224
[INFO] [stdout] [Epoch 196]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989792433727598
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020552587333
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989805387048752
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019257255212
[INFO] [stdout] [Epoch 197]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989792946679057
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020501292176
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989804894410048
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020193065190705
[INFO] [stdout] [Epoch 198]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979341980916
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020453979154
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989804440015792
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019351958486
[INFO] [stdout] [Epoch 199]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249897938562093
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020204103391303
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989804020897013
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020193938703556
[INFO] [stdout] [Epoch 200]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989794258730896
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020370086963
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.249898036343152
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201943252853
[INFO] [stdout] [Epoch 201]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989794630004
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020203329596464
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989803277744446
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019468185599
[INFO] [stdout] [Epoch 202]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989794972454493
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020202987145913
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989802948854933
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020195010745445
[INFO] [stdout] [Epoch 203]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989795288319921
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020267128043
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498980264549773
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020195314102607
[INFO] [stdout] [Epoch 204]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979557966414
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020202379936186
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989802365690703
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020195593909595
[INFO] [stdout] [Epoch 205]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989795848390775
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020202111209506
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989802107605613
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019585199465
[INFO] [stdout] [Epoch 206]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989796096255665
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020186334459
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989801869556139
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201960900441
[INFO] [stdout] [Epoch 207]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989796324878355
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020201634721873
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498980164998689
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019630961332
[INFO] [stdout] [Epoch 208]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989796535752637
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020142384757
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498980144746321
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020196512136984
[INFO] [stdout] [Epoch 209]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989796730256364
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020201229343815
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989801260661804
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019669893838
[INFO] [stdout] [Epoch 210]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989796909660422
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020104993974
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989801088362135
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019687123803
[INFO] [stdout] [Epoch 211]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989797075137005
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020200884463157
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989800929438413
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019703016174
[INFO] [stdout] [Epoch 212]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989797227767332
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020073183281
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989800782852228
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020197176747917
[INFO] [stdout] [Epoch 213]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249897973685487
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020059105143
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989800647645805
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019731195433
[INFO] [stdout] [Epoch 214]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989797498400937
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020046119919
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989800522935707
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020197436664416
[INFO] [stdout] [Epoch 215]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989797618172502
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020034142762
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498980040790709
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019755169303
[INFO] [stdout] [Epoch 216]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989797728645988
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020200230954127
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498980030180835
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019765779176
[INFO] [stdout] [Epoch 217]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979783054321
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260202001290569
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498980020394625
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019775565386
[INFO] [stdout] [Epoch 218]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989797924529972
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020003507014
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989800113681354
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019784591875
[INFO] [stdout] [Epoch 219]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979801122037
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019994837973
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498980003042389
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020197929176214
[INFO] [stdout] [Epoch 220]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798091180837
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199868419264
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799953629857
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198005970246
[INFO] [stdout] [Epoch 221]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798164933824
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019979466628
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799882797495
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201980768026
[INFO] [stdout] [Epoch 222]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798232961213
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199726638876
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799817463976
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019814213612
[INFO] [stdout] [Epoch 223]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798295707527
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019966389256
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799757202422
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198202397665
[INFO] [stdout] [Epoch 224]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979835358272
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199606017363
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799701619078
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198257981014
[INFO] [stdout] [Epoch 225]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798406964966
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019955263512
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799650350772
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198309249315
[INFO] [stdout] [Epoch 226]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798456203044
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199503397046
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799603062524
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198356537566
[INFO] [stdout] [Epoch 227]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979850161868
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199457981413
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799559445344
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019840015474
[INFO] [stdout] [Epoch 228]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798543508612
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019941609147
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799519214254
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019844038583
[INFO] [stdout] [Epoch 229]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798582146547
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199377453546
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979948210638
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201984774937
[INFO] [stdout] [Epoch 230]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979861778495
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199341815137
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979944787926
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198511720827
[INFO] [stdout] [Epoch 231]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798650656686
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199308943404
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979941630924
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019854329084
[INFO] [stdout] [Epoch 232]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798680976522
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199278623557
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979938719007
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198572410014
[INFO] [stdout] [Epoch 233]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798708942573
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019925065751
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799360331472
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198599268607
[INFO] [stdout] [Epoch 234]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979873473757
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199224862506
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979933555796
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198624042123
[INFO] [stdout] [Epoch 235]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798758530052
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199201070027
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979931270766
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019864689242
[INFO] [stdout] [Epoch 236]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979878047548
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199179124603
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979929163127
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198667968814
[INFO] [stdout] [Epoch 237]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979880071725
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199158882834
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799272191068
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198687409013
[INFO] [stdout] [Epoch 238]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798819387612
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199140212474
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799254260053
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019870534003
[INFO] [stdout] [Epoch 239]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798836608565
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199122991516
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979923772106
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198721879023
[INFO] [stdout] [Epoch 240]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798852492615
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199107107467
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979922246601
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019873713407
[INFO] [stdout] [Epoch 241]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798867143556
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019909245653
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979920839524
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198751204837
[INFO] [stdout] [Epoch 242]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798880657127
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199078942957
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799195416817
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198764183267
[INFO] [stdout] [Epoch 243]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979889312161
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199066478467
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799183445927
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019877615416
[INFO] [stdout] [Epoch 244]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798904618454
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019905498162
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979917240436
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198787195725
[INFO] [stdout] [Epoch 245]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798915222772
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201990443773
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799162219964
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019879738011
[INFO] [stdout] [Epoch 246]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798925003862
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019903459622
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979915282621
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198806773875
[INFO] [stdout] [Epoch 247]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979893402563
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019902557445
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799144161698
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198815438383
[INFO] [stdout] [Epoch 248]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798942347025
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199017253054
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799136169832
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198823430246
[INFO] [stdout] [Epoch 249]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979895002241
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199009577666
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.249897991287984
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019883080168
[INFO] [stdout] [Epoch 250]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979895710193
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199002498157
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979912199923
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019883760085
[INFO] [stdout] [Epoch 251]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798963631857
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198995968224
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799115727881
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198843872205
[INFO] [stdout] [Epoch 252]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798969654864
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198989945215
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979910994338
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201988496567
[INFO] [stdout] [Epoch 253]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798975210295
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198984389786
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979910460795
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019885499214
[INFO] [stdout] [Epoch 254]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798980334446
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198979265635
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799099686724
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019885991336
[INFO] [stdout] [Epoch 255]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798985060793
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198974539294
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799095147527
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019886445256
[INFO] [stdout] [Epoch 256]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979898942024
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198970179836
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799090960718
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019886863936
[INFO] [stdout] [Epoch 257]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798993441245
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019896615884
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979908709894
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198872501143
[INFO] [stdout] [Epoch 258]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798997150098
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019896244998
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799083536962
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019887606312
[INFO] [stdout] [Epoch 259]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979900057102
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019895902906
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799080251507
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019887934857
[INFO] [stdout] [Epoch 260]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799003726373
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989558737
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799077221106
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198882378975
[INFO] [stdout] [Epoch 261]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799006636773
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198952963314
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799074425967
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019888517412
[INFO] [stdout] [Epoch 262]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799009321226
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198950278856
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799071847812
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198887752266
[INFO] [stdout] [Epoch 263]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799011797278
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198947802803
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799069469812
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198890130275
[INFO] [stdout] [Epoch 264]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799014081118
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198945518963
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799067276414
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198892323665
[INFO] [stdout] [Epoch 265]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799016187647
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019894341243
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799065253301
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019889434678
[INFO] [stdout] [Epoch 266]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799018130648
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198941469436
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799063387252
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019889621283
[INFO] [stdout] [Epoch 267]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799019922804
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019893967728
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799061666057
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198897934027
[INFO] [stdout] [Epoch 268]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799021575837
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198938024247
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799060078485
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198899521596
[INFO] [stdout] [Epoch 269]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979902310054
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019893649954
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799058614157
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019890098593
[INFO] [stdout] [Epoch 270]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799024506884
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989350932
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799057263512
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198902336567
[INFO] [stdout] [Epoch 271]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979902580404
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019893379603
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979905601772
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019890358236
[INFO] [stdout] [Epoch 272]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799027000495
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019893259959
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979905486864
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019890473144
[INFO] [stdout] [Epoch 273]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799028104076
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019893149601
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979905380876
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019890579132
[INFO] [stdout] [Epoch 274]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979902912198
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198930478095
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799052831163
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019890676892
[INFO] [stdout] [Epoch 275]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799030060872
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198929539207
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799051929457
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019890767062
[INFO] [stdout] [Epoch 276]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799030926865
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198928673216
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979905109776
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019890850233
[INFO] [stdout] [Epoch 277]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799031725635
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892787445
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979905033062
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019890926946
[INFO] [stdout] [Epoch 278]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979903246239
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198927137694
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904962304
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019890997704
[INFO] [stdout] [Epoch 279]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799033141946
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892645814
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799048970396
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198910629694
[INFO] [stdout] [Epoch 280]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799033768756
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198925831334
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.249897990483684
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198911231684
[INFO] [stdout] [Epoch 281]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799034346907
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892525318
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799047813147
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891178693
[INFO] [stdout] [Epoch 282]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799034880167
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198924719906
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799047301006
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891229908
[INFO] [stdout] [Epoch 283]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979903537204
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198924228044
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799046828612
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198912771464
[INFO] [stdout] [Epoch 284]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979903582571
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198923774374
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799046392905
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891320717
[INFO] [stdout] [Epoch 285]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979903624417
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198923355914
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904599102
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198913609055
[INFO] [stdout] [Epoch 286]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799036630136
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892296994
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799045620337
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891397974
[INFO] [stdout] [Epoch 287]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979903698614
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892261394
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799045278424
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891432165
[INFO] [stdout] [Epoch 288]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799037314517
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892228557
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799044963054
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198914637027
[INFO] [stdout] [Epoch 289]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799037617402
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892198268
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799044672167
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198914927917
[INFO] [stdout] [Epoch 290]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979903789677
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892170331
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904440386
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891519623
[INFO] [stdout] [Epoch 291]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799038154456
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892144562
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799044156386
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989154437
[INFO] [stdout] [Epoch 292]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979903839213
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892120795
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799043928115
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198915671966
[INFO] [stdout] [Epoch 293]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799038611354
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198920988735
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904371757
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198915882514
[INFO] [stdout] [Epoch 294]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799038813568
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892078651
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799043523364
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198916076714
[INFO] [stdout] [Epoch 295]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979903900008
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892060001
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799043344246
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891625583
[INFO] [stdout] [Epoch 296]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249897990391721
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198920427984
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799043179034
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891642105
[INFO] [stdout] [Epoch 297]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799039330782
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989202693
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799043026642
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891657344
[INFO] [stdout] [Epoch 298]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799039477129
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892012296
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799042886082
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198916714
[INFO] [stdout] [Epoch 299]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799039612126
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198919987947
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799042756425
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198916843656
[INFO] [stdout] [Epoch 300]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799039736646
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198919863435
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799042636845
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891696324
[INFO] [stdout] [Epoch 301]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799039851496
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198919748583
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799042526545
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891707354
[INFO] [stdout] [Epoch 302]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799039957425
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198919642656
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799042424812
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198917175275
[INFO] [stdout] [Epoch 303]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904005513
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198919544946
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799042330973
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891726911
[INFO] [stdout] [Epoch 304]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040145258
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891945483
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799042244415
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891735567
[INFO] [stdout] [Epoch 305]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040228386
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198919371695
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799042164573
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891743551
[INFO] [stdout] [Epoch 306]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040305063
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891929501
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799042090938
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198917509146
[INFO] [stdout] [Epoch 307]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040375782
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891922429
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799042023017
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891757707
[INFO] [stdout] [Epoch 308]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040441016
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198919159065
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041960367
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198917639714
[INFO] [stdout] [Epoch 309]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040501184
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198919098897
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041902583
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989176975
[INFO] [stdout] [Epoch 310]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040556684
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198919043397
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904184928
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198917750803
[INFO] [stdout] [Epoch 311]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040607877
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891899221
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041800118
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198917799964
[INFO] [stdout] [Epoch 312]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040655084
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918945003
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041754773
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198917845305
[INFO] [stdout] [Epoch 313]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904069863
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918901455
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041712962
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891788712
[INFO] [stdout] [Epoch 314]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904073879
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989188613
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041674388
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198917925697
[INFO] [stdout] [Epoch 315]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904077584
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891882424
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041638802
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198917961274
[INFO] [stdout] [Epoch 316]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040810007
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891879008
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904160599
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198917994086
[INFO] [stdout] [Epoch 317]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904084152
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891875857
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041575722
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891802436
[INFO] [stdout] [Epoch 318]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040870594
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891872948
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041547806
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918052273
[INFO] [stdout] [Epoch 319]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040897404
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918702686
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041522054
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891807803
[INFO] [stdout] [Epoch 320]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904092214
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918677945
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041498298
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891810178
[INFO] [stdout] [Epoch 321]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904094495
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918655135
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041476388
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989181237
[INFO] [stdout] [Epoch 322]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040966
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891863408
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041456173
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918143905
[INFO] [stdout] [Epoch 323]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040985403
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891861468
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041437538
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918162546
[INFO] [stdout] [Epoch 324]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041003308
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891859678
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041420344
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891817974
[INFO] [stdout] [Epoch 325]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041019825
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918580256
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904140448
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989181956
[INFO] [stdout] [Epoch 326]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041035052
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918565024
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904138985
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891821023
[INFO] [stdout] [Epoch 327]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041049104
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891855098
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041376354
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891822373
[INFO] [stdout] [Epoch 328]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904106207
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891853802
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904136391
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891823617
[INFO] [stdout] [Epoch 329]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041074015
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891852607
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041352434
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918247645
[INFO] [stdout] [Epoch 330]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041085037
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891851505
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041341845
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891825823
[INFO] [stdout] [Epoch 331]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904109521
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891850488
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041332078
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918268006
[INFO] [stdout] [Epoch 332]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041104593
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891849549
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041323063
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891827702
[INFO] [stdout] [Epoch 333]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041113253
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918486825
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904131475
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918285337
[INFO] [stdout] [Epoch 334]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041121238
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891847885
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904130708
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918293
[INFO] [stdout] [Epoch 335]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041128605
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918471477
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904130001
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918300075
[INFO] [stdout] [Epoch 336]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904113539
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918464693
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041293492
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918306586
[INFO] [stdout] [Epoch 337]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041141647
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891845844
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041287478
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989183126
[INFO] [stdout] [Epoch 338]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041147423
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918452664
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041281927
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891831815
[INFO] [stdout] [Epoch 339]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041152752
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891844733
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904127681
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891832327
[INFO] [stdout] [Epoch 340]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041157668
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891844241
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041272093
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891832799
[INFO] [stdout] [Epoch 341]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041162203
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843787
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126774
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918332343
[INFO] [stdout] [Epoch 342]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041166383
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184337
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041263724
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891833636
[INFO] [stdout] [Epoch 343]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041170244
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918429827
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041260016
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918340065
[INFO] [stdout] [Epoch 344]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041173802
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918426274
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041256597
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918343485
[INFO] [stdout] [Epoch 345]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041177083
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918423
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041253444
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891834664
[INFO] [stdout] [Epoch 346]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041180108
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918419973
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904125055
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918349535
[INFO] [stdout] [Epoch 347]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041182895
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918417187
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041247873
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891835221
[INFO] [stdout] [Epoch 348]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041185462
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918414617
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041245397
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918354687
[INFO] [stdout] [Epoch 349]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041187843
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841224
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904124311
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891835697
[INFO] [stdout] [Epoch 350]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904119003
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918410054
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041241012
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891835907
[INFO] [stdout] [Epoch 351]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904119205
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840804
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904123907
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891836101
[INFO] [stdout] [Epoch 352]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041193916
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840617
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041237284
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918362797
[INFO] [stdout] [Epoch 353]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041195632
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840446
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904123564
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891836444
[INFO] [stdout] [Epoch 354]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041197205
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840287
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904123412
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891836596
[INFO] [stdout] [Epoch 355]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041198665
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840142
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041232721
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918367365
[INFO] [stdout] [Epoch 356]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041200014
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840006
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041231422
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918368653
[INFO] [stdout] [Epoch 357]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041201258
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839882
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041230235
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918369847
[INFO] [stdout] [Epoch 358]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041202396
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918397686
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041229135
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891837094
[INFO] [stdout] [Epoch 359]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041203456
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839662
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041228114
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891837196
[INFO] [stdout] [Epoch 360]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041204433
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839565
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041227181
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989183729
[INFO] [stdout] [Epoch 361]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041205338
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839474
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041226315
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891837376
[INFO] [stdout] [Epoch 362]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041206165
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839392
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041225516
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918374565
[INFO] [stdout] [Epoch 363]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041206937
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918393156
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041224783
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891837529
[INFO] [stdout] [Epoch 364]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904120763
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839245
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041224106
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891837597
[INFO] [stdout] [Epoch 365]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041208285
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918391796
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041223476
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891837661
[INFO] [stdout] [Epoch 366]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041208896
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918391185
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.249897990412229
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918377185
[INFO] [stdout] [Epoch 367]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041209446
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839064
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041222366
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891837772
[INFO] [stdout] [Epoch 368]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041209962
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839012
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041221866
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891837821
[INFO] [stdout] [Epoch 369]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121044
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891838964
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904122141
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891837867
[INFO] [stdout] [Epoch 370]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121087
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918389215
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041221
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918379084
[INFO] [stdout] [Epoch 371]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041211275
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918388804
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041220612
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891837947
[INFO] [stdout] [Epoch 372]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121164
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918388443
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041220256
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891837982
[INFO] [stdout] [Epoch 373]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121198
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918388104
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041219934
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838015
[INFO] [stdout] [Epoch 374]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041212296
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891838778
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041219635
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918380444
[INFO] [stdout] [Epoch 375]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121258
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989183875
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041219357
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918380727
[INFO] [stdout] [Epoch 376]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121285
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918387233
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041219102
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918380977
[INFO] [stdout] [Epoch 377]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121309
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918386994
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121887
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918381215
[INFO] [stdout] [Epoch 378]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041213318
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918386767
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041218647
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838143
[INFO] [stdout] [Epoch 379]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041213523
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891838656
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041218447
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838164
[INFO] [stdout] [Epoch 380]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041213723
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918386356
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041218258
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918381826
[INFO] [stdout] [Epoch 381]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041213906
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891838618
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121808
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918382
[INFO] [stdout] [Epoch 382]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041214067
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918386017
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041217925
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918382154
[INFO] [stdout] [Epoch 383]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041214222
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891838586
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121778
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918382303
[INFO] [stdout] [Epoch 384]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121436
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918385723
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041217647
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918382437
[INFO] [stdout] [Epoch 385]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041214494
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891838559
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041217514
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918382564
[INFO] [stdout] [Epoch 386]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121461
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918385473
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041217403
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918382675
[INFO] [stdout] [Epoch 387]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041214722
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891838536
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041217303
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838278
[INFO] [stdout] [Epoch 388]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041214822
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918385257
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041217203
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838288
[INFO] [stdout] [Epoch 389]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041214916
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918385157
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041217114
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838297
[INFO] [stdout] [Epoch 390]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215005
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891838508
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041217026
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918383053
[INFO] [stdout] [Epoch 391]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215083
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918385
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216948
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838313
[INFO] [stdout] [Epoch 392]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215155
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891838493
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121688
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918383197
[INFO] [stdout] [Epoch 393]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215222
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891838486
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216815
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918383264
[INFO] [stdout] [Epoch 394]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215288
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384796
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121676
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918383325
[INFO] [stdout] [Epoch 395]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215344
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891838474
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216704
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838338
[INFO] [stdout] [Epoch 396]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249897990412154
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891838468
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216648
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918383436
[INFO] [stdout] [Epoch 397]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121545
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384635
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216604
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838348
[INFO] [stdout] [Epoch 398]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215494
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891838459
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121656
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918383525
[INFO] [stdout] [Epoch 399]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215538
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384546
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216515
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838357
[INFO] [stdout] [Epoch 400]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215582
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989183845
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121647
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838361
[INFO] [stdout] [Epoch 401]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215616
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891838447
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216437
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838364
[INFO] [stdout] [Epoch 402]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121565
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384435
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216404
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918383675
[INFO] [stdout] [Epoch 403]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215682
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384396
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121637
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838371
[INFO] [stdout] [Epoch 404]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121571
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384363
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216348
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838373
[INFO] [stdout] [Epoch 405]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215732
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891838434
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216326
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838375
[INFO] [stdout] [Epoch 406]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215754
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891838432
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216304
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918383775
[INFO] [stdout] [Epoch 407]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215777
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384296
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216282
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918383797
[INFO] [stdout] [Epoch 408]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249897990412158
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384285
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121627
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838381
[INFO] [stdout] [Epoch 409]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121581
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384274
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121626
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838382
[INFO] [stdout] [Epoch 410]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121582
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384263
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216249
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838383
[INFO] [stdout] [Epoch 411]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215832
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891838425
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216237
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838384
[INFO] [stdout] [Epoch 412]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215843
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891838424
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216226
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838385
[INFO] [stdout] [Epoch 413]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215854
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891838423
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216215
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838387
[INFO] [stdout] [Epoch 414]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215865
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891838422
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216204
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838388
[INFO] [stdout] [Epoch 415]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215877
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891838421
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216193
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838389
[INFO] [stdout] [Epoch 416]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215888
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384196
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216182
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989183839
[INFO] [stdout] [Epoch 417]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249897990412159
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384185
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121617
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918383913
[INFO] [stdout] [Epoch 418]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121591
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384174
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121616
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918383924
[INFO] [stdout] [Epoch 419]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121592
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384163
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216149
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918383936
[INFO] [stdout] [Epoch 420]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215932
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891838415
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216137
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918383947
[INFO] [stdout] [Epoch 421]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215943
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384135
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216126
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838396
[INFO] [stdout] [Epoch 422]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215954
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384124
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216115
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838397
[INFO] [stdout] [Epoch 423]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215965
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 424]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 425]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 426]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 427]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 428]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 429]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 430]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 431]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 432]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 433]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 434]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 435]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 436]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 437]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 438]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 439]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 440]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 441]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 442]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 443]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 444]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 445]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 446]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 447]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 448]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 449]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 450]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 451]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 452]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 453]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 454]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 455]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 456]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 457]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 458]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 459]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 460]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 461]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 462]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 463]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 464]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 465]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 466]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 467]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 468]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 469]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 470]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 471]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 472]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 473]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 474]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 475]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 476]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 477]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 478]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 479]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 480]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 481]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 482]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 483]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 484]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 485]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 486]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 487]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 488]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 489]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 490]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 491]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 492]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 493]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 494]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 495]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 496]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 497]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 498]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 499]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 500]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 501]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 502]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 503]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 504]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 505]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 506]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 507]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 508]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 509]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 510]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 511]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 512]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 513]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 514]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 515]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 516]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 517]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 518]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 519]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 520]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 521]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 522]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 523]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 524]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 525]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 526]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 527]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 528]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 529]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 530]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 531]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 532]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 533]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 534]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 535]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 536]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 537]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 538]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 539]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 540]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 541]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 542]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 543]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 544]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 545]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 546]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 547]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 548]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 549]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 550]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 551]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 552]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 553]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 554]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 555]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 556]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 557]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 558]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 559]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 560]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 561]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 562]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 563]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 564]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 565]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 566]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 567]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 568]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 569]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 570]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 571]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 572]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 573]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 574]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 575]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 576]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 577]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 578]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 579]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 580]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 581]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 582]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 583]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 584]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 585]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 586]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 587]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 588]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 589]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 590]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 591]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 592]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 593]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 594]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 595]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 596]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 597]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 598]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 599]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 600]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 601]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 602]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 603]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 604]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 605]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 606]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 607]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 608]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 609]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 610]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 611]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 612]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 613]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 614]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 615]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 616]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 617]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 618]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 619]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 620]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 621]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 622]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 623]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 624]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 625]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 626]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 627]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 628]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 629]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 630]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 631]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 632]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 633]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 634]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 635]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 636]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 637]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 638]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 639]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 640]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 641]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 642]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 643]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 644]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 645]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 646]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 647]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 648]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 649]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 650]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 651]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 652]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 653]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 654]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 655]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 656]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 657]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 658]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 659]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 660]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 661]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 662]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 663]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 664]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 665]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 666]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 667]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 668]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 669]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 670]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 671]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 672]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 673]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 674]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 675]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 676]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 677]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 678]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 679]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 680]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 681]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 682]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 683]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 684]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 685]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 686]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 687]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 688]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 689]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 690]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 691]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 692]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 693]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 694]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 695]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 696]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 697]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 698]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 699]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 700]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 701]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 702]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 703]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 704]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 705]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 706]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 707]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 708]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 709]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 710]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 711]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 712]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 713]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 714]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 715]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 716]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 717]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 718]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 719]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 720]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 721]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 722]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 723]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 724]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 725]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 726]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 727]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 728]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 729]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 730]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 731]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 732]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 733]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 734]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 735]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 736]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 737]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 738]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 739]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 740]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 741]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 742]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 743]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 744]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 745]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 746]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 747]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 748]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 749]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 750]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 751]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 752]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 753]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 754]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 755]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 756]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 757]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 758]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 759]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 760]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 761]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 762]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 763]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 764]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 765]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 766]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 767]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 768]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 769]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 770]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 771]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 772]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 773]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 774]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 775]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 776]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 777]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 778]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 779]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 780]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 781]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 782]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 783]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 784]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 785]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 786]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 787]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 788]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 789]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 790]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 791]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 792]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 793]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 794]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 795]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 796]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 797]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 798]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 799]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 800]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 801]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 802]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 803]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 804]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 805]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 806]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 807]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 808]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 809]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 810]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 811]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 812]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 813]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 814]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 815]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 816]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 817]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 818]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 819]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 820]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 821]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 822]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 823]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 824]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 825]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 826]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 827]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 828]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 829]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 830]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 831]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 832]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 833]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 834]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 835]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 836]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 837]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 838]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 839]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 840]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 841]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 842]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 843]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 844]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 845]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 846]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 847]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 848]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 849]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 850]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 851]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 852]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 853]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 854]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 855]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 856]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 857]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 858]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 859]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 860]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 861]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 862]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 863]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 864]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 865]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 866]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 867]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 868]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 869]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 870]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 871]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 872]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 873]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 874]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 875]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 876]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 877]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 878]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 879]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 880]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 881]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 882]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 883]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 884]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 885]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 886]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 887]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 888]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 889]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 890]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 891]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 892]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 893]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 894]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 895]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 896]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 897]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 898]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 899]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 900]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 901]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 902]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 903]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 904]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 905]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 906]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 907]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 908]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 909]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 910]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 911]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 912]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 913]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 914]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 915]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 916]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 917]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 918]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 919]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 920]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 921]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 922]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 923]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 924]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 925]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 926]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 927]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 928]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 929]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 930]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 931]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 932]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 933]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 934]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 935]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 936]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 937]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 938]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 939]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 940]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 941]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 942]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 943]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 944]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 945]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 946]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 947]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 948]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 949]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 950]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 951]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 952]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 953]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 954]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 955]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 956]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 957]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 958]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 959]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 960]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 961]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 962]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 963]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 964]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 965]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 966]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 967]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 968]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 969]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 970]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 971]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 972]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 973]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 974]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 975]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 976]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 977]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 978]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 979]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 980]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 981]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 982]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 983]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 984]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 985]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 986]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 987]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 988]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 989]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 990]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 991]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 992]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 993]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 994]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 995]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 996]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 997]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 998]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 999]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] 
[INFO] [stdout] thread 'models::sequential::test_sequential_xor1' (23) 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:     0x5a8d6c4154e2 - std::backtrace_rs::backtrace::libunwind::trace::hc4a5f428cfb78751
[INFO] [stdout]                                at /rustc/b6ae10aa7319b7ebb6c7b3331dd71a9d6c9c5b4b/library/std/src/../../backtrace/src/backtrace/libunwind.rs:117:9
[INFO] [stdout]    1:     0x5a8d6c4154e2 - std::backtrace_rs::backtrace::trace_unsynchronized::h20e1095684b4c296
[INFO] [stdout]                                at /rustc/b6ae10aa7319b7ebb6c7b3331dd71a9d6c9c5b4b/library/std/src/../../backtrace/src/backtrace/mod.rs:66:14
[INFO] [stdout]    2:     0x5a8d6c4154e2 - std::sys::backtrace::_print_fmt::h461f2e3a8f6b29e2
[INFO] [stdout]                                at /rustc/b6ae10aa7319b7ebb6c7b3331dd71a9d6c9c5b4b/library/std/src/sys/backtrace.rs:66:9
[INFO] [stdout]    3:     0x5a8d6c4154e2 - <std::sys::backtrace::BacktraceLock::print::DisplayBacktrace as core::fmt::Display>::fmt::h4ee3a75aa71a2c45
[INFO] [stdout]                                at /rustc/b6ae10aa7319b7ebb6c7b3331dd71a9d6c9c5b4b/library/std/src/sys/backtrace.rs:39:26
[INFO] [stdout]    4:     0x5a8d6c4384ff - core::fmt::rt::Argument::fmt::h6f1564705cd089af
[INFO] [stdout]                                at /rustc/b6ae10aa7319b7ebb6c7b3331dd71a9d6c9c5b4b/library/core/src/fmt/rt.rs:173:76
[INFO] [stdout]    5:     0x5a8d6c4384ff - core::fmt::write::h21ca93b65a7c281a
[INFO] [stdout]                                at /rustc/b6ae10aa7319b7ebb6c7b3331dd71a9d6c9c5b4b/library/core/src/fmt/mod.rs:1468:25
[INFO] [stdout]    6:     0x5a8d6c4023d3 - std::io::default_write_fmt::h6f3360f4711e9130
[INFO] [stdout]                                at /rustc/b6ae10aa7319b7ebb6c7b3331dd71a9d6c9c5b4b/library/std/src/io/mod.rs:639:11
[INFO] [stdout]    7:     0x5a8d6c4023d3 - std::io::Write::write_fmt::hf4539125c0e0bc30
[INFO] [stdout]                                at /rustc/b6ae10aa7319b7ebb6c7b3331dd71a9d6c9c5b4b/library/std/src/io/mod.rs:1954:13
[INFO] [stdout]    8:     0x5a8d6c415af2 - std::sys::backtrace::BacktraceLock::print::h58d5d73f9e953cf1
[INFO] [stdout]                                at /rustc/b6ae10aa7319b7ebb6c7b3331dd71a9d6c9c5b4b/library/std/src/sys/backtrace.rs:42:9
[INFO] [stdout]    9:     0x5a8d6c4008dc - std::panicking::default_hook::{{closure}}::h1457fbe47c9457d1
[INFO] [stdout]                                at /rustc/b6ae10aa7319b7ebb6c7b3331dd71a9d6c9c5b4b/library/std/src/panicking.rs:301:27
[INFO] [stdout]   10:     0x5a8d6c40074e - std::panicking::default_hook::hccb5e73b206c0830
[INFO] [stdout]                                at /rustc/b6ae10aa7319b7ebb6c7b3331dd71a9d6c9c5b4b/library/std/src/panicking.rs:325:9
[INFO] [stdout]   11:     0x5a8d6c350ade - <alloc::boxed::Box<F,A> as core::ops::function::Fn<Args>>::call::h04940a86aa899793
[INFO] [stdout]                                at /rustc/b6ae10aa7319b7ebb6c7b3331dd71a9d6c9c5b4b/library/alloc/src/boxed.rs:1985:9
[INFO] [stdout]   12:     0x5a8d6c350ade - test::test_main_with_exit_callback::{{closure}}::hca6cc295403b83e8
[INFO] [stdout]                                at /rustc/b6ae10aa7319b7ebb6c7b3331dd71a9d6c9c5b4b/library/test/src/lib.rs:145:21
[INFO] [stdout]   13:     0x5a8d6c401663 - <alloc::boxed::Box<F,A> as core::ops::function::Fn<Args>>::call::h9d85285925beb538
[INFO] [stdout]                                at /rustc/b6ae10aa7319b7ebb6c7b3331dd71a9d6c9c5b4b/library/alloc/src/boxed.rs:1985:9
[INFO] [stdout]   14:     0x5a8d6c401663 - std::panicking::panic_with_hook::h3190ecc6229cdd29
[INFO] [stdout]                                at /rustc/b6ae10aa7319b7ebb6c7b3331dd71a9d6c9c5b4b/library/std/src/panicking.rs:842:13
[INFO] [stdout]   15:     0x5a8d6c4158fa - std::panicking::panic_handler::{{closure}}::ha1f1b769bc2bb40c
[INFO] [stdout]                                at /rustc/b6ae10aa7319b7ebb6c7b3331dd71a9d6c9c5b4b/library/std/src/panicking.rs:707:13
[INFO] [stdout]   16:     0x5a8d6c415859 - std::sys::backtrace::__rust_end_short_backtrace::h5f9cf66f19c2a172
[INFO] [stdout]                                at /rustc/b6ae10aa7319b7ebb6c7b3331dd71a9d6c9c5b4b/library/std/src/sys/backtrace.rs:174:18
[INFO] [stdout]   17:     0x5a8d6c40130d - __rustc[a93bd50104b99ad4]::rust_begin_unwind
[INFO] [stdout]                                at /rustc/b6ae10aa7319b7ebb6c7b3331dd71a9d6c9c5b4b/library/std/src/panicking.rs:698:5
[INFO] [stdout]   18:     0x5a8d6c43f4f0 - core::panicking::panic_fmt::hc70c3c83f13c1375
[INFO] [stdout]                                at /rustc/b6ae10aa7319b7ebb6c7b3331dd71a9d6c9c5b4b/library/core/src/panicking.rs:75:14
[INFO] [stdout]   19:     0x5a8d6c43f5e0 - core::panicking::assert_failed_inner::hb61150608d983f16
[INFO] [stdout]                                at /rustc/b6ae10aa7319b7ebb6c7b3331dd71a9d6c9c5b4b/library/core/src/panicking.rs:448:17
[INFO] [stdout]   20:     0x5a8d6c31ffa6 - core::panicking::assert_failed::hc57cde1730582402
[INFO] [stdout]                                at /rustc/b6ae10aa7319b7ebb6c7b3331dd71a9d6c9c5b4b/library/core/src/panicking.rs:403:5
[INFO] [stdout]   21:     0x5a8d6c311387 - easynn::models::sequential::test_sequential_xor1::h199790f958c20a03
[INFO] [stdout]                                at /opt/rustwide/workdir/src/models/sequential.rs:242:5
[INFO] [stdout]   22:     0x5a8d6c311ad7 - easynn::models::sequential::test_sequential_xor1::{{closure}}::h9cf530042313cdda
[INFO] [stdout]                                at /opt/rustwide/workdir/src/models/sequential.rs:205:26
[INFO] [stdout]   23:     0x5a8d6c3259d6 - core::ops::function::FnOnce::call_once::h6b1f259a8b154fb9
[INFO] [stdout]                                at /rustc/b6ae10aa7319b7ebb6c7b3331dd71a9d6c9c5b4b/library/core/src/ops/function.rs:253:5
[INFO] [stdout]   24:     0x5a8d6c351b5b - core::ops::function::FnOnce::call_once::h70d190c46966003b
[INFO] [stdout]                                at /rustc/b6ae10aa7319b7ebb6c7b3331dd71a9d6c9c5b4b/library/core/src/ops/function.rs:253:5
[INFO] [stdout]   25:     0x5a8d6c351b5b - test::__rust_begin_short_backtrace::hb077ccaabb17d2ec
[INFO] [stdout]                                at /rustc/b6ae10aa7319b7ebb6c7b3331dd71a9d6c9c5b4b/library/test/src/lib.rs:663:18
[INFO] [stdout]   26:     0x5a8d6c34f7c5 - test::run_test_in_process::{{closure}}::hd73eeb633a1507bd
[INFO] [stdout]                                at /rustc/b6ae10aa7319b7ebb6c7b3331dd71a9d6c9c5b4b/library/test/src/lib.rs:686:74
[INFO] [stdout]   27:     0x5a8d6c34f7c5 - <core::panic::unwind_safe::AssertUnwindSafe<F> as core::ops::function::FnOnce<()>>::call_once::h033c82756fc9d192
[INFO] [stdout]                                at /rustc/b6ae10aa7319b7ebb6c7b3331dd71a9d6c9c5b4b/library/core/src/panic/unwind_safe.rs:272:9
[INFO] [stdout]   28:     0x5a8d6c34f7c5 - std::panicking::catch_unwind::do_call::h734b44e243d34585
[INFO] [stdout]                                at /rustc/b6ae10aa7319b7ebb6c7b3331dd71a9d6c9c5b4b/library/std/src/panicking.rs:590:40
[INFO] [stdout]   29:     0x5a8d6c34f7c5 - std::panicking::catch_unwind::h5e85417122517fb1
[INFO] [stdout]                                at /rustc/b6ae10aa7319b7ebb6c7b3331dd71a9d6c9c5b4b/library/std/src/panicking.rs:553:19
[INFO] [stdout]   30:     0x5a8d6c34f7c5 - std::panic::catch_unwind::h0900e58a82a7d188
[INFO] [stdout]                                at /rustc/b6ae10aa7319b7ebb6c7b3331dd71a9d6c9c5b4b/library/std/src/panic.rs:359:14
[INFO] [stdout]   31:     0x5a8d6c34f7c5 - test::run_test_in_process::h27721efd62ae77d0
[INFO] [stdout]                                at /rustc/b6ae10aa7319b7ebb6c7b3331dd71a9d6c9c5b4b/library/test/src/lib.rs:686:27
[INFO] [stdout]   32:     0x5a8d6c34f7c5 - test::run_test::{{closure}}::h8b1264838cc597aa
[INFO] [stdout]                                at /rustc/b6ae10aa7319b7ebb6c7b3331dd71a9d6c9c5b4b/library/test/src/lib.rs:607:43
[INFO] [stdout]   33:     0x5a8d6c3766d4 - test::run_test::{{closure}}::h6ecb96835e2eae4f
[INFO] [stdout]                                at /rustc/b6ae10aa7319b7ebb6c7b3331dd71a9d6c9c5b4b/library/test/src/lib.rs:637:41
[INFO] [stdout]   34:     0x5a8d6c3766d4 - std::sys::backtrace::__rust_begin_short_backtrace::h098f38ee82a820a7
[INFO] [stdout]                                at /rustc/b6ae10aa7319b7ebb6c7b3331dd71a9d6c9c5b4b/library/std/src/sys/backtrace.rs:158:18
[INFO] [stdout]   35:     0x5a8d6c35d2ea - std::thread::Builder::spawn_unchecked_::{{closure}}::{{closure}}::h042592f748f5e369
[INFO] [stdout]                                at /rustc/b6ae10aa7319b7ebb6c7b3331dd71a9d6c9c5b4b/library/std/src/thread/mod.rs:559:17
[INFO] [stdout]   36:     0x5a8d6c35d2ea - <core::panic::unwind_safe::AssertUnwindSafe<F> as core::ops::function::FnOnce<()>>::call_once::h134fe7f3a2d9ec8d
[INFO] [stdout]                                at /rustc/b6ae10aa7319b7ebb6c7b3331dd71a9d6c9c5b4b/library/core/src/panic/unwind_safe.rs:272:9
[INFO] [stdout]   37:     0x5a8d6c35d2ea - std::panicking::catch_unwind::do_call::heb87d604f60aee16
[INFO] [stdout]                                at /rustc/b6ae10aa7319b7ebb6c7b3331dd71a9d6c9c5b4b/library/std/src/panicking.rs:590:40
[INFO] [stdout]   38:     0x5a8d6c35d2ea - std::panicking::catch_unwind::h2203a0f553b89fd7
[INFO] [stdout]                                at /rustc/b6ae10aa7319b7ebb6c7b3331dd71a9d6c9c5b4b/library/std/src/panicking.rs:553:19
[INFO] [stdout]   39:     0x5a8d6c35d2ea - std::panic::catch_unwind::h4d383e0b0e25d838
[INFO] [stdout]                                at /rustc/b6ae10aa7319b7ebb6c7b3331dd71a9d6c9c5b4b/library/std/src/panic.rs:359:14
[INFO] [stdout]   40:     0x5a8d6c35d2ea - std::thread::Builder::spawn_unchecked_::{{closure}}::h76cbdd4635cfd58b
[INFO] [stdout]                                at /rustc/b6ae10aa7319b7ebb6c7b3331dd71a9d6c9c5b4b/library/std/src/thread/mod.rs:557:30
[INFO] [stdout]   41:     0x5a8d6c35d2ea - core::ops::function::FnOnce::call_once{{vtable.shim}}::he72e066680f5e267
[INFO] [stdout]                                at /rustc/b6ae10aa7319b7ebb6c7b3331dd71a9d6c9c5b4b/library/core/src/ops/function.rs:253:5
[INFO] [stdout]   42:     0x5a8d6c3ebfef - <alloc::boxed::Box<F,A> as core::ops::function::FnOnce<Args>>::call_once::h1b9c6fea2cbefc68
[INFO] [stdout]                                at /rustc/b6ae10aa7319b7ebb6c7b3331dd71a9d6c9c5b4b/library/alloc/src/boxed.rs:1971:9
[INFO] [stdout]   43:     0x5a8d6c3ebfef - std::sys::pal::unix::thread::Thread::new::thread_start::h92caa992bc760789
[INFO] [stdout]                                at /rustc/b6ae10aa7319b7ebb6c7b3331dd71a9d6c9c5b4b/library/std/src/sys/pal/unix/thread.rs:107:17
[INFO] [stdout]   44:     0x77a0205adaa4 - <unknown>
[INFO] [stdout]   45:     0x77a02063aa34 - clone
[INFO] [stdout]   46:                0x0 - <unknown>
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] failures:
[INFO] [stdout]     models::sequential::test_sequential_xor1
[INFO] [stdout] 
[INFO] [stdout] test result: FAILED. 6 passed; 1 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.62s
[INFO] [stdout] 
[INFO] [stderr] error: test failed, to rerun pass `--lib`
[INFO] running `Command { std: "docker" "inspect" "4d878cd55fa22989bd57c9434777cbcf780dc508fb33eedf423d9bad7eb6a5ee", kill_on_drop: false }`
[INFO] running `Command { std: "docker" "rm" "-f" "4d878cd55fa22989bd57c9434777cbcf780dc508fb33eedf423d9bad7eb6a5ee", kill_on_drop: false }`
[INFO] [stdout] 4d878cd55fa22989bd57c9434777cbcf780dc508fb33eedf423d9bad7eb6a5ee