[INFO] fetching crate easynn 0.1.7-beta...
[INFO] testing easynn-0.1.7-beta against try#b83b707f97d809763b7861afa7638871f3339a33 for pr-145838-1
[INFO] extracting crate easynn 0.1.7-beta into /workspace/builds/worker-1-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-1-tc2/source/Cargo.toml
[INFO] validating manifest of crates.io crate easynn 0.1.7-beta on toolchain b83b707f97d809763b7861afa7638871f3339a33
[INFO] running `Command { std: CARGO_HOME="/workspace/cargo-home" RUSTUP_HOME="/workspace/rustup-home" "/workspace/cargo-home/bin/cargo" "+b83b707f97d809763b7861afa7638871f3339a33" "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" "+b83b707f97d809763b7861afa7638871f3339a33" "generate-lockfile" "--manifest-path" "Cargo.toml", kill_on_drop: false }`
[INFO] [stderr]     Blocking waiting for file lock on package cache
[INFO] [stderr]     Updating crates.io index
[INFO] [stderr]     Blocking waiting for file lock on package cache
[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" "+b83b707f97d809763b7861afa7638871f3339a33" "fetch" "--manifest-path" "Cargo.toml", kill_on_drop: false }`
[INFO] running `Command { std: "docker" "create" "-v" "/var/lib/crater-agent-workspace/builds/worker-1-tc2/target:/opt/rustwide/target:rw,Z" "-v" "/var/lib/crater-agent-workspace/builds/worker-1-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@sha256:7ad1b28ee6f5f7f699f6cf7015098d6ccdd96d6f2d78dd06228f5b4c9faf309c" "/opt/rustwide/cargo-home/bin/cargo" "+b83b707f97d809763b7861afa7638871f3339a33" "metadata" "--no-deps" "--format-version=1", kill_on_drop: false }`
[INFO] [stdout] 16b9509a9f18b4ead8022cee31a0d41a9cf718ad101b6b0e1142231a9d458fc4
[INFO] running `Command { std: "docker" "start" "-a" "16b9509a9f18b4ead8022cee31a0d41a9cf718ad101b6b0e1142231a9d458fc4", kill_on_drop: false }`
[INFO] running `Command { std: "docker" "inspect" "16b9509a9f18b4ead8022cee31a0d41a9cf718ad101b6b0e1142231a9d458fc4", kill_on_drop: false }`
[INFO] running `Command { std: "docker" "rm" "-f" "16b9509a9f18b4ead8022cee31a0d41a9cf718ad101b6b0e1142231a9d458fc4", kill_on_drop: false }`
[INFO] [stdout] 16b9509a9f18b4ead8022cee31a0d41a9cf718ad101b6b0e1142231a9d458fc4
[INFO] running `Command { std: "docker" "create" "-v" "/var/lib/crater-agent-workspace/builds/worker-1-tc2/target:/opt/rustwide/target:rw,Z" "-v" "/var/lib/crater-agent-workspace/builds/worker-1-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@sha256:7ad1b28ee6f5f7f699f6cf7015098d6ccdd96d6f2d78dd06228f5b4c9faf309c" "/opt/rustwide/cargo-home/bin/cargo" "+b83b707f97d809763b7861afa7638871f3339a33" "build" "--frozen" "--message-format=json", kill_on_drop: false }`
[INFO] [stdout] b69e8bdea6b60fdfa43fa483dbc95855311b030670576ad963432dcd5aa1144c
[INFO] running `Command { std: "docker" "start" "-a" "b69e8bdea6b60fdfa43fa483dbc95855311b030670576ad963432dcd5aa1144c", kill_on_drop: false }`
[INFO] [stderr]    Compiling rayon-core v1.13.0
[INFO] [stderr]    Compiling crossbeam-queue v0.3.12
[INFO] [stderr]    Compiling crossbeam-channel v0.5.15
[INFO] [stderr]    Compiling itertools v0.10.5
[INFO] [stderr]    Compiling crossbeam v0.8.4
[INFO] [stderr]    Compiling rayon v1.11.0
[INFO] [stderr]    Compiling easynn v0.1.7-beta (/opt/rustwide/workdir)
[INFO] [stdout] warning: unused variable: `olen`
[INFO] [stdout]   --> src/layers/dense.rs:96:13
[INFO] [stdout]    |
[INFO] [stdout] 96 |         let olen = output.flattened.len();
[INFO] [stdout]    |             ^^^^ help: if this is intentional, prefix it with an underscore: `_olen`
[INFO] [stdout]    |
[INFO] [stdout]    = note: `#[warn(unused_variables)]` (part of `#[warn(unused)]`) 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)]` (part of `#[warn(unused)]`) 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)]` (part of `#[warn(unused)]`) 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 3.99s
[INFO] running `Command { std: "docker" "inspect" "b69e8bdea6b60fdfa43fa483dbc95855311b030670576ad963432dcd5aa1144c", kill_on_drop: false }`
[INFO] running `Command { std: "docker" "rm" "-f" "b69e8bdea6b60fdfa43fa483dbc95855311b030670576ad963432dcd5aa1144c", kill_on_drop: false }`
[INFO] [stdout] b69e8bdea6b60fdfa43fa483dbc95855311b030670576ad963432dcd5aa1144c
[INFO] running `Command { std: "docker" "create" "-v" "/var/lib/crater-agent-workspace/builds/worker-1-tc2/target:/opt/rustwide/target:rw,Z" "-v" "/var/lib/crater-agent-workspace/builds/worker-1-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@sha256:7ad1b28ee6f5f7f699f6cf7015098d6ccdd96d6f2d78dd06228f5b4c9faf309c" "/opt/rustwide/cargo-home/bin/cargo" "+b83b707f97d809763b7861afa7638871f3339a33" "test" "--frozen" "--no-run" "--message-format=json", kill_on_drop: false }`
[INFO] [stdout] 6736e784d4013044718d14345209f0fcbadf3bd366f7ab3d5fe5fa47cf691f85
[INFO] running `Command { std: "docker" "start" "-a" "6736e784d4013044718d14345209f0fcbadf3bd366f7ab3d5fe5fa47cf691f85", 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)]` (part of `#[warn(unused)]`) 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)]` (part of `#[warn(unused)]`) 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)]` (part of `#[warn(unused)]`) on by default
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stderr]    Compiling easynn v0.1.7-beta (/opt/rustwide/workdir)
[INFO] [stdout] warning: method `pos2index` is never used
[INFO] [stdout]   --> src/tensor/mod.rs:38:19
[INFO] [stdout]    |
[INFO] [stdout] 26 | impl<T: NumT> Tensor<T> {
[INFO] [stdout]    | ----------------------- method in this implementation
[INFO] [stdout] ...
[INFO] [stdout] 38 |     pub(crate) fn pos2index<const RANK: usize>(&self, mut pos: usize) -> Result<TensorIndex<RANK>> {
[INFO] [stdout]    |                   ^^^^^^^^^
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: 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)]` (part of `#[warn(unused)]`) 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)]` (part of `#[warn(unused)]`) 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)]` (part of `#[warn(unused)]`) 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)]` (part of `#[warn(unused)]`) on by default
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: method `pos2index` is never used
[INFO] [stdout]   --> src/tensor/mod.rs:38:19
[INFO] [stdout]    |
[INFO] [stdout] 26 | impl<T: NumT> Tensor<T> {
[INFO] [stdout]    | ----------------------- method in this implementation
[INFO] [stdout] ...
[INFO] [stdout] 38 |     pub(crate) fn pos2index<const RANK: usize>(&self, mut pos: usize) -> Result<TensorIndex<RANK>> {
[INFO] [stdout]    |                   ^^^^^^^^^
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stderr]     Finished `test` profile [unoptimized + debuginfo] target(s) in 1.10s
[INFO] running `Command { std: "docker" "inspect" "6736e784d4013044718d14345209f0fcbadf3bd366f7ab3d5fe5fa47cf691f85", kill_on_drop: false }`
[INFO] running `Command { std: "docker" "rm" "-f" "6736e784d4013044718d14345209f0fcbadf3bd366f7ab3d5fe5fa47cf691f85", kill_on_drop: false }`
[INFO] [stdout] 6736e784d4013044718d14345209f0fcbadf3bd366f7ab3d5fe5fa47cf691f85
[INFO] running `Command { std: "docker" "create" "-v" "/var/lib/crater-agent-workspace/builds/worker-1-tc2/target:/opt/rustwide/target:rw,Z" "-v" "/var/lib/crater-agent-workspace/builds/worker-1-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@sha256:7ad1b28ee6f5f7f699f6cf7015098d6ccdd96d6f2d78dd06228f5b4c9faf309c" "/opt/rustwide/cargo-home/bin/cargo" "+b83b707f97d809763b7861afa7638871f3339a33" "test" "--frozen", kill_on_drop: false }`
[INFO] [stdout] 1bcdf372f0909574aa46a80887a61581c21399d691a8498f372a07dca138688d
[INFO] running `Command { std: "docker" "start" "-a" "1bcdf372f0909574aa46a80887a61581c21399d691a8498f372a07dca138688d", 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)]` (part of `#[warn(unused)]`) 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)]` (part of `#[warn(unused)]`) 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)]` (part of `#[warn(unused)]`) 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: `easynn` (lib) generated 7 warnings (run `cargo fix --lib -p easynn` to apply 2 suggestions)
[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)]` (part of `#[warn(unused)]`) 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 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 0.10s
[INFO] [stderr]      Running unittests src/lib.rs (/opt/rustwide/target/debug/deps/easynn-4e88085c55175155)
[INFO] [stdout] 
[INFO] [stdout] running 7 tests
[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_add_weight_delta_to ... ok
[INFO] [stdout] test layers::dense::test_dense_backpropagate ... ok
[INFO] [stdout] test layers::dense::test_dense_forward ... 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): 0.9604180592250409
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.0015674449397671494
[INFO] [stdout] [Epoch 1]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.0015053741201449418
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.9253994252990929
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.8887536080528338
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.005793866278945103
[INFO] [stdout] [Epoch 2]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.005564429174269789
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.8591376787542631
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.8251158266712645
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.012057865563582596
[INFO] [stdout] [Epoch 3]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.011580374087200815
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.800201984016723
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.7685139854454127
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.01984821451641584
[INFO] [stdout] [Epoch 4]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.019062225221455027
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.7476979665680992
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.7180891270878332
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.028746060171733156
[INFO] [stdout] [Epoch 5]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.0276077161887642
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.7008492813046279
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.6730956497608718
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.038409890653929725
[INFO] [stdout] [Epoch 6]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.03688885898379881
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.6589811971562303
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.6328855417448251
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.04856284153412903
[INFO] [stdout] [Epoch 7]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.046639753009067204
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.6215065384090059
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.5968948794840631
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.058981985940284004
[INFO] [stdout] [Epoch 8]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.05664629929665671
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.5879136382496969
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.5646322581711325
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.06948930543167968
[INFO] [stdout] [Epoch 9]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.06673752893610606
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.5577560108990922
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.5356688728636793
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.07994408440467544
[INFO] [stdout] [Epoch 10]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.07677829866167998
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.5306434920048365
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.5096300097177013
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.09023650968214283
[INFO] [stdout] [Epoch 11]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.0866631438980656
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.5062346338452942
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.4861877423413395
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.1002822899829245
[INFO] [stdout] [Epoch 12]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.09631111129884039
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.48423017331030954
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.46505465844360006
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.11001813804455986
[INFO] [stdout] [Epoch 13]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.10566141977713822
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.4643674173849179
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.4459784676529111
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.11939798202650913
[INFO] [stdout] [Epoch 14]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.11466982193730556
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.4464154136601751
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.4287373632757226
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.1283897930849249
[INFO] [stdout] [Epoch 15]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.12330555727771216
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.43017079282014115
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.41313602942100575
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.1369729332218038
[INFO] [stdout] [Epoch 16]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.1315488050650762
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.41545418660682765
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.3990022008137884
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.1451359421287491
[INFO] [stdout] [Epoch 17]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.13938855881921397
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.4021071388721426
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.38618369616944326
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.1528746941577583
[INFO] [stdout] [Epoch 18]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.14682085626778438
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.38998943935058383
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.3745458575489818
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.16019086708936767
[INFO] [stdout] [Epoch 19]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.15384730875121477
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.37897682003765665
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.3639693379608876
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.16709067331354735
[INFO] [stdout] [Epoch 20]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.1604738826488329
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.36895896279977386
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.35434818786966377
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.17358381163022651
[INFO] [stdout] [Epoch 21]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.1667098926880908
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.3598377742956201
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.3455881984303107
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.17968260431768865
[INFO] [stdout] [Epoch 22]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.1725671731850522
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.351525890647262
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.3376054653744617
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.18540128958129437
[INFO] [stdout] [Epoch 23]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.17805939851214536
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.34394537972392236
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.3303251426837183
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.19075544412910883
[INFO] [stdout] [Epoch 24]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.1832015285397963
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.3370266135304598
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.3236803596315467
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.1957615145500619
[INFO] [stdout] [Epoch 25]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.1880093585720132
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.33070728714384484
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.31761127856986965
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.20043643950051274
[INFO] [stdout] [Epoch 26]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.19249915649436322
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.32493156401445444
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.31206427407642906
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.20479734752686074
[INFO] [stdout] [Epoch 27]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.19668737256280847
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.3196493303300952
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.30699121684599495
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.20886131774191652
[INFO] [stdout] [Epoch 28]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.20059040955729224
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.3148155436019751
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.302348848072331
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.21264519259636286
[INFO] [stdout] [Epoch 29]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.20422444296744996
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.31038966273519275
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2980982320878945
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.21616543369924665
[INFO] [stdout] [Epoch 30]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.20760528252261023
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.3063351486444378
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2942042767551532
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.21943801309016533
[INFO] [stdout] [Epoch 31]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.21074826776960234
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.3026190260133299
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.29063531258025554
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.22247833359065983
[INFO] [stdout] [Epoch 32]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.21366819157823408
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.299211498111453
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.28736272278331026
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.22530117289731957
[INFO] [stdout] [Epoch 33]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.21637924644830978
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.29608560770920184
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.28436061764100423
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.22792064695311848
[INFO] [stdout] [Epoch 34]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.21889498933146143
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.293216938094837
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.28160554734338294
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2303501888710626
[INFO] [stdout] [Epoch 35]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.22122832138941995
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.29058334902429434
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2790762484000477
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.23260254030617944
[INFO] [stdout] [Epoch 36]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2233914797076736
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.28816474314247387
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2767534193111603
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2346897526958884
[INFO] [stdout] [Epoch 37]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.22539603848671969
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2859428590221457
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2746195218020092
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.23662319622988418
[INFO] [stdout] [Epoch 38]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.22725291765674116
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2839010874878995
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2726586044205302
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.23841357478157874
[INFO] [stdout] [Epoch 39]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2289723972177625
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2820243083402446
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.27085614572713307
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.24007094534468737
[INFO] [stdout] [Epoch 40]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.23056413590654787
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.28029874497969
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2691989146756661
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.24160474077989463
[INFO] [stdout] [Epoch 41]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.23203719304249854
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2787118347614937
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.26767484610211956
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2430237948954623
[INFO] [stdout] [Epoch 42]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2334000526150691
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2772521131965459
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.26627292951115217
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.24433636906874873
[INFO] [stdout] [Epoch 43]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.23466064885107427
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2759091103591362
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2649831095861119
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.24555017976848145
[INFO] [stdout] [Epoch 44]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2358263926470799
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.27467325807384185
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2637961970513227
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2466724264650066
[INFO] [stdout] [Epoch 45]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.23690419837440638
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.27353580663626226
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.26270378869067806
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.24770981952163423
[INFO] [stdout] [Epoch 46]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2379005106659765
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2724887499799552
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2616981954779673
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2486686077480134
[INFO] [stdout] [Epoch 47]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.23882133087857724
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.27152475833823386
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.26077237790526414
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.24955460536907487
[INFO] [stdout] [Epoch 48]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.239672242993832
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2706371175674528
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2599198877090118
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.250373218222911
[INFO] [stdout] [Epoch 49]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24045843877864456
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2698196744006507
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25913481529162047
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25112946905007244
[INFO] [stdout] [Epoch 50]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2411847420730397
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26906678698910763
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2584117422215793
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2518280217768949
[INFO] [stdout] [Epoch 51]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24185563211187017
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26837328016642054
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2577456982690755
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25247320472808854
[INFO] [stdout] [Epoch 52]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24247526581818754
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26773440493670897
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25713212249846484
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2530690327301708
[INFO] [stdout] [Epoch 53]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24304749903137896
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26714580174691643
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2565668279949923
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2536192280884466
[INFO] [stdout] [Epoch 54]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24357590665345966
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2666034671540634
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2560459698520201
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25412724043701346
[INFO] [stdout] [Epoch 55]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2440638017130164
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2661037235427574
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2555660160877258
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2545962654744326
[INFO] [stdout] [Epoch 56]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24451425335894741
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2656431915871274
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2551237211975423
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25502926260790704
[INFO] [stdout] [Epoch 57]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24493010380593064
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26521876518539644
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2547161020813231
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25542897153652533
[INFO] [stdout] [Epoch 58]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24531398426097045
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2648275886251741
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2543404161128888
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25579792780984734
[INFO] [stdout] [Epoch 59]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24566832986586418
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2644670357637824
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25399414114481117
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25613847740217044
[INFO] [stdout] [Epoch 60]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.245995393694327
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2641346910310196
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25367495726346856
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2564527903455307
[INFO] [stdout] [Epoch 61]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24629725984512638
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2638283320821161
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2533807301289443
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2567428734661343
[INFO] [stdout] [Epoch 62]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24657585567415063
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26354591394659693
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25310949575159425
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2570105822696758
[INFO] [stdout] [Epoch 63]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24683296320906872
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2632855545346519
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25285944657236503
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2572576320210753
[INFO] [stdout] [Epoch 64]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2470702297903101
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26304552137668213
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2526289187274531
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25748560806370085
[INFO] [stdout] [Epoch 65]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2472891779816451
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26282421948416756
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25241638038988434
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25769597542224326
[INFO] [stdout] [Epoch 66]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24749121479278713
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26262018023109557
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2522204210912362
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25789008773221295
[INFO] [stdout] [Epoch 67]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24767764025527997
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26243205116505397
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2520397419362118
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2580691955375665
[INFO] [stdout] [Epoch 68]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24784965539153983
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26225858666588936
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2518731466312163
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2582344539963666
[INFO] [stdout] [Epoch 69]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24800836961536993
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2620986393776937
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2517195332556352
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2583869300326377
[INFO] [stdout] [Epoch 70]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24815480760060343
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2619511523468934
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2515778867112564
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25852760897078714
[INFO] [stdout] [Epoch 71]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.248289915652801
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26181515180551845
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25144727179132165
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25865740068712145
[INFO] [stdout] [Epoch 72]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24841456761716757
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26168974054436156
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2513268268161084
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25877714531115126
[INFO] [stdout] [Epoch 73]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24852957035408507
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2615740918258117
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2512157577868149
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2588876185075552
[INFO] [stdout] [Epoch 74]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2486356688119107
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26146744379069986
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25111333301389505
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25898953636788463
[INFO] [stdout] [Epoch 75]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24873355072497072
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2613690943176065
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2510188781799379
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25908355993935406
[INFO] [stdout] [Epoch 76]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24882385096300963
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26127839629678035
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2509317718007379
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25917029941637637
[INFO] [stdout] [Epoch 77]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2489071555567416
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26119475328416186
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2508514410514206
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25925031801888776
[INFO] [stdout] [Epoch 78]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2489840054225934
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26111761550403495
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25077735792738814
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25932413557996015
[INFO] [stdout] [Epoch 79]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24905489980824713
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26104647617156224
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2507090357124829
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25939223186371857
[INFO] [stdout] [Epoch 80]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2491202994791689
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2609808681089444
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2506460257291462
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25945504963318977
[INFO] [stdout] [Epoch 81]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24918062966496918
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2609203606311944
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25058791434751654
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2595129974863701
[INFO] [stdout] [Epoch 82]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24923628278316373
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260864556679565
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25053432023237304
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2595664524775556
[INFO] [stdout] [Epoch 83]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24928762095669857
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2608130901825209
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2504848918086131
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25961576253979635
[INFO] [stdout] [Epoch 84]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24933497834047488
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2607656236258479
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2504393049275857
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2596612487232198
[INFO] [stdout] [Epoch 85]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24937866327103517
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2607218458150239
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2503972607180717
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2597032072629358
[INFO] [stdout] [Epoch 86]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24941896025257893
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260681469814386
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2503584836070605
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2597419114892505
[INFO] [stdout] [Epoch 87]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2494561317915321
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2606442310489094
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25032271949669804
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2597776135920081
[INFO] [stdout] [Epoch 88]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2494904200910211
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2606098855555799
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25028973408490557
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.259810546250023
[INFO] [stdout] [Epoch 89]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24952204861577915
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2605782083724171
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2502593113181974
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.259840924135768
[INFO] [stdout] [Epoch 90]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2495512235372492
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2605489920541755
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2502312519661593
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2598689453047419
[INFO] [stdout] [Epoch 91]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24957813506793253
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26052204530464934
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2502053723079156
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25989479247824626
[INFO] [stdout] [Epoch 92]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24960295869336677
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26049719171632585
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25018150292169106
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2599186342276556
[INFO] [stdout] [Epoch 93]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24962585630950038
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26047426860887385
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2501594875692954
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2599406260676709
[INFO] [stdout] [Epoch 94]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2496469772726517
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2604531259586513
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25013918216802283
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2599609114654793
[INFO] [stdout] [Epoch 95]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24966645936870777
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2604336254120368
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2501204538430554
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25997962277223896
[INFO] [stdout] [Epoch 96]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2496844297077205
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26041563937597106
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.250103180054019
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25999688208281346
[INFO] [stdout] [Epoch 97]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24970100554959712
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26039905017962206
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2500872477898466
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2600128020292456
[INFO] [stdout] [Epoch 98]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24971629506615134
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26038374930157626
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25007255282657265
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26002748651304153
[INFO] [stdout] [Epoch 99]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24973039804438987
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26036963665740076
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25005899904310774
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26004103138095386
[INFO] [stdout] [Epoch 100]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24974340653553379
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26035661994283493
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25004649779043975
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26005352504859913
[INFO] [stdout] [Epoch 101]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2497554054539412
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26034461402824405
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25003496731006786
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26006504907591405
[INFO] [stdout] [Epoch 102]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24976647312977546
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2603335404003154
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25002433219780634
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26007567869815296
[INFO] [stdout] [Epoch 103]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2497766818189747
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2603233266472942
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2500145229094059
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26008548331584547
[INFO] [stdout] [Epoch 104]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2497860981738074
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2603139059843489
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25000547530471423
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26009452694687096
[INFO] [stdout] [Epoch 105]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24979478367704538
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26030521681592783
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24999713022736392
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26010286864357296
[INFO] [stdout] [Epoch 106]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24980279504255903
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602972023322119
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24998943311720426
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601105628776021
[INFO] [stdout] [Epoch 107]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498101845849215
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260289810136998
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24998233365292183
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601176598949785
[INFO] [stdout] [Epoch 108]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24981700056041084
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602829919045605
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24997578542249
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601242060436732
[INFO] [stdout] [Epoch 109]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24982328748161817
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602767030632249
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499697456192725
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601302440758256
[INFO] [stdout] [Epoch 110]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24982908640769844
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602709025035706
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499641747617815
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26013581342655967
[INFO] [stdout] [Epoch 111]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498344352121444
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26026555230933984
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24995903643524348
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26014095047120445
[INFO] [stdout] [Epoch 112]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24983936882982238
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602606175092845
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24995429705327138
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601456887625891
[INFO] [stdout] [Epoch 113]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498439194848692
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26025606584831423
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24994992563807675
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26015005924995216
[INFO] [stdout] [Epoch 114]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24984811690093361
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26025186757644747
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24994589361777697
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26015409048088667
[INFO] [stdout] [Epoch 115]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24985198849512433
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602479952541714
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24994217463946403
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601578087876369
[INFO] [stdout] [Epoch 116]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24985555955692818
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26024442357293937
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24993874439680994
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601612384589501
[INFO] [stdout] [Epoch 117]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24985885341325847
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602411291896223
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24993558047107334
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26016440189861084
[INFO] [stdout] [Epoch 118]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24986189158070973
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26023809057383007
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24993266218446755
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260167319771681
[INFO] [stdout] [Epoch 119]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24986469390600743
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602352878671003
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24992997046492538
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601700111394024
[INFO] [stdout] [Epoch 120]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249867278695568
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602327027530312
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24992748772137446
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601724935836368
[INFO] [stdout] [Epoch 121]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24986966283501177
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602303183375073
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24992519772870647
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26017478332165733
[INFO] [stdout] [Epoch 122]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24987186189940772
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26022811903823273
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24992308552168405
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601768953120348
[INFO] [stdout] [Epoch 123]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24987389025496748
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602260904828465
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24992113729709253
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26017884335231284
[INFO] [stdout] [Epoch 124]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24987576115285157
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602242194149574
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24991934032349275
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601806401691053
[INFO] [stdout] [Epoch 125]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24987748681570005
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602224936074746
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24991768285798754
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601822975012044
[INFO] [stdout] [Epoch 126]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24987907851744914
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26022090178267615
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24991615406945203
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26018382617624297
[INFO] [stdout] [Epoch 127]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498805466569572
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26021943353848187
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499147439677289
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26018523618140665
[INFO] [stdout] [Epoch 128]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498819008259175
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26021807928045837
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24991344333832421
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601865367286622
[INFO] [stdout] [Epoch 129]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498831498715028
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26021683015910096
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24991224368217377
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260187736314923
[INFO] [stdout] [Epoch 130]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24988430195414862
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602156780119911
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24991113716009042
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26018884277754656
[INFO] [stdout] [Epoch 131]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498853646008535
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26021461531044243
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24991011654152412
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26018986334552646
[INFO] [stdout] [Epoch 132]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24988634475434263
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26021363511029405
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990917515730274
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601908046867108
[INFO] [stdout] [Epoch 133]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498872488184171
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602127310065234
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990830685604254
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26019167295135637
[INFO] [stdout] [Epoch 134]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498880826997838
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602118970913846
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499075059639443
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601924738123042
[INFO] [stdout] [Epoch 135]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24988885184663925
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602111279157969
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990676724771085
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26019321250203575
[INFO] [stdout] [Epoch 136]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24988956128425843
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602104184537331
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990608588034605
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601938938468537
[INFO] [stdout] [Epoch 137]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498902156478227
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602097640693721
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499054574096067
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601945222984108
[INFO] [stdout] [Epoch 138]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989081921269918
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020916048680254
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990487772890793
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601951019627899
[INFO] [stdout] [Epoch 139]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989137592236993
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260208603762079
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990434305048442
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26019563662732886
[INFO] [stdout] [Epoch 140]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989188941419432
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020809025744795
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990384988063805
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26019612978536294
[INFO] [stdout] [Epoch 141]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989236304317128
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602076166175754
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499033949969055
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26019658465904566
[INFO] [stdout] [Epoch 142]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989279990385724
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602071797476198
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499029754270012
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601970042203999
[INFO] [stdout] [Epoch 143]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989320285058295
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020677679300763
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499025884293926
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601973912107338
[INFO] [stdout] [Epoch 144]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989357451610086
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020640512078014
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499022314753864
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601977481585514
[INFO] [stdout] [Epoch 145]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989391732878585
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602060623023867
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990190223260236
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601980773960699
[INFO] [stdout] [Epoch 146]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989423352849974
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020574609781594
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990159854973376
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26019838107445875
[INFO] [stdout] [Epoch 147]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989452518122546
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020545444095816
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990131844248856
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601986611778926
[INFO] [stdout] [Epoch 148]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498947941925643
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020518542610377
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990106008062352
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26019891953651497
[INFO] [stdout] [Epoch 149]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989504232018644
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602049372954905
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990082177598355
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601991578383958
[INFO] [stdout] [Epoch 150]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989527118531382
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602047084278182
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499006019714723
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26019937764055984
[INFO] [stdout] [Epoch 151]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989548228331326
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020449732765355
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.249900399230875
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26019958037915963
[INFO] [stdout] [Epoch 152]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989567699346563
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020430261565897
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990021222947656
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601997673788588
[INFO] [stdout] [Epoch 153]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989585658797786
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602041230195793
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990003974540256
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601999398614868
[INFO] [stdout] [Epoch 154]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989602224029495
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020395736592833
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498998806516375
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020009895402185
[INFO] [stdout] [Epoch 155]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498961750327665
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020380457232206
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989973390865905
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602002456959534
[INFO] [stdout] [Epoch 156]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989631596371875
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020366364040404
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.249899598557646
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602003810460756
[INFO] [stdout] [Epoch 157]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989644595397728
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020353364932386
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989947371421378
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020050588875
[INFO] [stdout] [Epoch 158]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498965658528827
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602034137497192
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989935856263448
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020062103968417
[INFO] [stdout] [Epoch 159]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989667644384098
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602033031581659
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498992523505078
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020072725126187
[INFO] [stdout] [Epoch 160]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989677844944136
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020320115205925
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989915438384377
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602008252174586
[INFO] [stdout] [Epoch 161]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989687253617784
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602031070648917
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989906402252926
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020091557837566
[INFO] [stdout] [Epoch 162]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989695931880349
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602030202818993
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498989806761443
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260200998924422
[INFO] [stdout] [Epoch 163]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989703936434762
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602029402360428
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989890380010496
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602010758001733
[INFO] [stdout] [Epoch 164]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498971131958203
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602028664043044
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498988328921043
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602011467079287
[INFO] [stdout] [Epoch 165]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989718129562954
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602027983042688
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989876748883127
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020121211099306
[INFO] [stdout] [Epoch 166]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989724410873373
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260202735490972
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498987071629421
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020127243670443
[INFO] [stdout] [Epoch 167]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249897302045548
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602026775539937
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989865152026916
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020132807922575
[INFO] [stdout] [Epoch 168]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989735548462663
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020262411477524
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498986001972449
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602013794021212
[INFO] [stdout] [Epoch 169]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498974047751364
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602025748241465
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989855285852597
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602014267407301
[INFO] [stdout] [Epoch 170]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989745023913756
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602025293600438
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989850919480291
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020147040435954
[INFO] [stdout] [Epoch 171]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989749217368837
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602024874254066
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989846892077824
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602015106783044
[INFO] [stdout] [Epoch 172]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989753085278596
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020244874623516
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989843177330312
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602015478257115
[INFO] [stdout] [Epoch 173]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989756652915676
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602024130698015
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989839750965726
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020158208929917
[INFO] [stdout] [Epoch 174]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989759943590753
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602023801629971
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989836590596348
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602016136929433
[INFO] [stdout] [Epoch 175]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989762978804855
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602023498108103
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989833675572432
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602016428431403
[INFO] [stdout] [Epoch 176]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498976577838988
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602023218149209
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498983098684731
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020166973035525
[INFO] [stdout] [Epoch 177]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989768360638104
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020229599240524
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989828506853012
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602016945302672
[INFO] [stdout] [Epoch 178]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498977074242176
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602022721745401
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989826219385347
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020171740491743
[INFO] [stdout] [Epoch 179]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989772939303279
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602022502057005
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498982410949809
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602017385037673
[INFO] [stdout] [Epoch 180]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989774965636927
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020222994234293
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989822163405345
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602017579646752
[INFO] [stdout] [Epoch 181]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989776834662633
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020221125206777
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498982036839143
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602017759147978
[INFO] [stdout] [Epoch 182]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989778558592507
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602021940127536
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989818712727793
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020179247141983
[INFO] [stdout] [Epoch 183]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498978014869059
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020217811175944
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989817185596414
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020180774272106
[INFO] [stdout] [Epoch 184]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989781615346476
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020216344518904
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989815777019106
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602018218284835
[INFO] [stdout] [Epoch 185]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989782968143207
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020214991721186
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989814477792266
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020183482074255
[INFO] [stdout] [Epoch 186]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989784215919877
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602021374394365
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989813279426842
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020184680438896
[INFO] [stdout] [Epoch 187]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989785366829378
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260202125930334
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498981217409273
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602018578577229
[INFO] [stdout] [Epoch 188]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989786428391678
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602021153147045
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989811154567784
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020186805296636
[INFO] [stdout] [Epoch 189]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498978740754297
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020210552318584
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989810214190444
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602018774567343
[INFO] [stdout] [Epoch 190]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498978831068096
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020964918008
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498980934681633
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020188613047085
[INFO] [stdout] [Epoch 191]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989789143706717
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020208816153895
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989808546778083
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020189413084915
[INFO] [stdout] [Epoch 192]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498978991206316
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020208047797055
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989807808848272
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019015101434
[INFO] [stdout] [Epoch 193]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979062077069
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020733908918
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989807128205332
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019083165694
[INFO] [stdout] [Epoch 194]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989791274459952
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020206685399616
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989806500401973
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019145946002
[INFO] [stdout] [Epoch 195]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989791877402126
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020206082457137
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989805921336145
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019203852558
[INFO] [stdout] [Epoch 196]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989792433536806
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260202055263222
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989805387224254
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019257263722
[INFO] [stdout] [Epoch 197]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989792946497727
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020205013361053
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498980489457647
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020193065284775
[INFO] [stdout] [Epoch 198]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989793419636552
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020454022202
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989804440173846
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020193519687207
[INFO] [stdout] [Epoch 199]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979385604476
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020410381362
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989804021047307
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019393881355
[INFO] [stdout] [Epoch 200]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989794258573803
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020203701284383
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989803634458346
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020194325402335
[INFO] [stdout] [Epoch 201]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989794629853773
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020203330004243
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498980327788101
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201946819795
[INFO] [stdout] [Epoch 202]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989794972310594
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020298754726
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989802948985423
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019501087494
[INFO] [stdout] [Epoch 203]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989795288181874
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020202671675835
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989802645622605
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020195314237593
[INFO] [stdout] [Epoch 204]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979557953148
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020238032608
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989802365810407
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019559404965
[INFO] [stdout] [Epoch 205]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989795848263088
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020211159433
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989802107720543
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019585213937
[INFO] [stdout] [Epoch 206]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989796096132566
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020186372472
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498980186966666
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019609019312
[INFO] [stdout] [Epoch 207]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979632475948
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020163509766
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498980165009336
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020196309766297
[INFO] [stdout] [Epoch 208]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989796535637668
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020142421935
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989801447565918
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019651229361
[INFO] [stdout] [Epoch 209]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989796730145014
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020122971188
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498980126076105
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019669909834
[INFO] [stdout] [Epoch 210]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989796909552398
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020201050304387
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989801088458194
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020196871401097
[INFO] [stdout] [Epoch 211]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989797075032055
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020200884824607
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498980092953151
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020197030327663
[INFO] [stdout] [Epoch 212]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989797227665225
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020073219132
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989800782942603
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020197176916454
[INFO] [stdout] [Epoch 213]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989797368449213
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020200591407217
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498980064773367
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019731212527
[INFO] [stdout] [Epoch 214]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989797498303873
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020046155245
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989800523021244
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019743683758
[INFO] [stdout] [Epoch 215]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989797618077675
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020034177854
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498980040799048
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019755186822
[INFO] [stdout] [Epoch 216]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979772855322
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020023130288
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989800301889767
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019765796883
[INFO] [stdout] [Epoch 217]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989797830452345
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020012940365
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989800204025847
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019775583264
[INFO] [stdout] [Epoch 218]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989797924440857
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020003541502
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989800113759275
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019784609911
[INFO] [stdout] [Epoch 219]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979801113287
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201999487229
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989800030500256
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020197929358
[INFO] [stdout] [Epoch 220]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798091094834
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199868760835
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799953704792
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019800615337
[INFO] [stdout] [Epoch 221]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798164849203
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019979500636
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799882871097
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198076986945
[INFO] [stdout] [Epoch 222]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979823287788
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019972697757
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979981753636
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198142321577
[INFO] [stdout] [Epoch 223]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979829562537
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199664229976
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799757273684
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019820258417
[INFO] [stdout] [Epoch 224]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798353501663
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199606353583
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799701689275
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019825816844
[INFO] [stdout] [Epoch 225]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798406884914
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199552970225
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799650420014
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019830943762
[INFO] [stdout] [Epoch 226]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979845612393
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199503731095
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799603130866
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198356726654
[INFO] [stdout] [Epoch 227]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798501540422
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201994583145
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799559512854
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198400344563
[INFO] [stdout] [Epoch 228]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798543431163
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199416423645
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799519280997
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019844057632
[INFO] [stdout] [Epoch 229]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798582069853
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019937778487
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799482172392
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201984776848
[INFO] [stdout] [Epoch 230]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798617708947
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199342145667
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979944794462
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198511912473
[INFO] [stdout] [Epoch 231]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979865058131
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019930927319
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799416373998
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019854348299
[INFO] [stdout] [Epoch 232]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798680901743
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019927895266
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979938725425
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019857260263
[INFO] [stdout] [Epoch 233]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798708868347
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019925098595
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799360395143
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198599461647
[INFO] [stdout] [Epoch 234]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979873466385
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199225190344
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979933562113
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019862423553
[INFO] [stdout] [Epoch 235]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798758456797
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019920139728
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799312770386
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019864708617
[INFO] [stdout] [Epoch 236]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798780402658
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199179451314
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799291693596
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198668162886
[INFO] [stdout] [Epoch 237]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798800644833
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199159209045
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799272253008
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019868760335
[INFO] [stdout] [Epoch 238]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798819315573
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201991405382
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799254321646
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019870553461
[INFO] [stdout] [Epoch 239]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798836536853
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019912331681
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979923778232
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019872207383
[INFO] [stdout] [Epoch 240]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798852421224
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019910743234
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799222526973
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198737329064
[INFO] [stdout] [Epoch 241]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798867072469
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019909278099
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799208455923
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198751400037
[INFO] [stdout] [Epoch 242]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979888058632
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199079267053
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799195477225
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198764378616
[INFO] [stdout] [Epoch 243]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798893051052
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201990668022
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979918350609
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198776349646
[INFO] [stdout] [Epoch 244]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798904548136
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199055305016
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.249897991724643
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198787391335
[INFO] [stdout] [Epoch 245]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798915152686
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019904470037
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799162279694
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019879757582
[INFO] [stdout] [Epoch 246]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798924933976
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019903491898
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799152885747
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019880696968
[INFO] [stdout] [Epoch 247]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798933955934
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199025896906
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979914422106
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198815634255
[INFO] [stdout] [Epoch 248]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798942277505
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199017575235
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979913622903
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198823626184
[INFO] [stdout] [Epoch 249]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979894995306
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199009899586
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799128857432
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019883099768
[INFO] [stdout] [Epoch 250]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979895703275
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019900281978
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799122058104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019883779691
[INFO] [stdout] [Epoch 251]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798963562831
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198996289606
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799115786612
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019884406829
[INFO] [stdout] [Epoch 252]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798969585966
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019899026636
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799110001987
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198849852805
[INFO] [stdout] [Epoch 253]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798975141525
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198984710696
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799104666435
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198855188253
[INFO] [stdout] [Epoch 254]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798980265798
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198979586334
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979909974509
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198860109506
[INFO] [stdout] [Epoch 255]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798984992262
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198974859765
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.249897990952058
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198864648675
[INFO] [stdout] [Epoch 256]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798989351802
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198970500125
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799091018894
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019886883549
[INFO] [stdout] [Epoch 257]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798993372908
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989664789
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799087157039
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019887269725
[INFO] [stdout] [Epoch 258]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798997081844
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198962769864
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979908359497
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198876259193
[INFO] [stdout] [Epoch 259]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979900050285
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019895934876
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799080309438
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198879544626
[INFO] [stdout] [Epoch 260]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979900365828
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198956193225
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799077278968
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198882574996
[INFO] [stdout] [Epoch 261]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799006568753
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019895328265
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799074483765
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019888537011
[INFO] [stdout] [Epoch 262]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799009253277
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019895059803
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799071905544
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198887948215
[INFO] [stdout] [Epoch 263]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799011729397
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198948121803
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799069527474
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019889032617
[INFO] [stdout] [Epoch 264]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799014013298
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198945837797
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799067334034
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019889251952
[INFO] [stdout] [Epoch 265]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799016119887
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989437311
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799065310841
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201988945426
[INFO] [stdout] [Epoch 266]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799018062958
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198941787936
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979906344473
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019889640861
[INFO] [stdout] [Epoch 267]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799019855183
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019893999561
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979906172349
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019889812976
[INFO] [stdout] [Epoch 268]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799021508272
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019893834242
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799060135862
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198899717284
[INFO] [stdout] [Epoch 269]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799023033035
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198936817557
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799058671489
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198901181546
[INFO] [stdout] [Epoch 270]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799024439435
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198935411054
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799057320777
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198902532143
[INFO] [stdout] [Epoch 271]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799025736642
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019893411374
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979905607494
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198903777897
[INFO] [stdout] [Epoch 272]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799026933152
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198932917116
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799054925815
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198904926917
[INFO] [stdout] [Epoch 273]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799028036783
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989318134
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979905386588
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198905986736
[INFO] [stdout] [Epoch 274]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799029054743
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198930795335
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979905288825
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019890696427
[INFO] [stdout] [Epoch 275]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799029993673
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892985629
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.249897990519865
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198907865916
[INFO] [stdout] [Epoch 276]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979903085971
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198928990157
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799051154757
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019890869755
[INFO] [stdout] [Epoch 277]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799031658524
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892819125
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799050387584
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198909464637
[INFO] [stdout] [Epoch 278]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979903239533
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892745435
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799049679973
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891017215
[INFO] [stdout] [Epoch 279]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799033074925
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198926774646
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799049027284
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891082472
[INFO] [stdout] [Epoch 280]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799033701768
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198926147703
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799048425265
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198911426623
[INFO] [stdout] [Epoch 281]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799034279941
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198925569415
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904786999
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891198181
[INFO] [stdout] [Epoch 282]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979903481325
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892503601
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904735779
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891249391
[INFO] [stdout] [Epoch 283]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799035305163
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892454401
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799046885378
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198912966225
[INFO] [stdout] [Epoch 284]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799035758878
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198924090177
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799046449626
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891340187
[INFO] [stdout] [Epoch 285]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799036177382
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892367157
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799046047698
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891380368
[INFO] [stdout] [Epoch 286]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799036563387
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198923285476
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904567698
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989141743
[INFO] [stdout] [Epoch 287]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799036919425
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892292932
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799045335057
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891451613
[INFO] [stdout] [Epoch 288]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799037247828
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198922600823
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904501965
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891483144
[INFO] [stdout] [Epoch 289]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799037550742
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198922297805
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799044728744
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198915122245
[INFO] [stdout] [Epoch 290]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799037830132
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892201831
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799044460415
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891539047
[INFO] [stdout] [Epoch 291]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799038087845
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198921760496
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799044212907
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891563786
[INFO] [stdout] [Epoch 292]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799038325547
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198921522697
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799043984626
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198915866044
[INFO] [stdout] [Epoch 293]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799038544805
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892130334
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799043774047
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198916076526
[INFO] [stdout] [Epoch 294]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799038747051
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198921101
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904357982
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891627066
[INFO] [stdout] [Epoch 295]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799038933597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892091434
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799043400654
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891644971
[INFO] [stdout] [Epoch 296]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799039105656
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198920742177
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799043235422
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891661485
[INFO] [stdout] [Epoch 297]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979903926436
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892058338
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799043083008
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198916767157
[INFO] [stdout] [Epoch 298]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799039410746
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198920436893
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799042942415
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891690764
[INFO] [stdout] [Epoch 299]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799039545765
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892030177
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799042812746
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891703721
[INFO] [stdout] [Epoch 300]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799039670318
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198920177123
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904269313
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198917156734
[INFO] [stdout] [Epoch 301]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799039785196
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892006214
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.249897990425828
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891726695
[INFO] [stdout] [Epoch 302]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799039891153
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891995607
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799042481045
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198917368614
[INFO] [stdout] [Epoch 303]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799039988891
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198919858234
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799042387184
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198917462367
[INFO] [stdout] [Epoch 304]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040079042
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198919767984
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799042300603
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891754884
[INFO] [stdout] [Epoch 305]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040162197
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891968474
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904222074
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198917628606
[INFO] [stdout] [Epoch 306]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040238908
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198919607923
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799042147082
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891770216
[INFO] [stdout] [Epoch 307]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904030965
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198919537085
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904207914
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198917770015
[INFO] [stdout] [Epoch 308]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904037491
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891947172
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799042016456
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891783258
[INFO] [stdout] [Epoch 309]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040435112
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891941143
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041958646
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989178903
[INFO] [stdout] [Epoch 310]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040490643
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891935579
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041905322
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198917943527
[INFO] [stdout] [Epoch 311]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040541866
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891930446
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041856128
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198917992604
[INFO] [stdout] [Epoch 312]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249897990405891
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198919257126
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041810762
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891803786
[INFO] [stdout] [Epoch 313]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904063268
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198919213455
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041768926
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989180796
[INFO] [stdout] [Epoch 314]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040672872
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198919173165
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904173032
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918118115
[INFO] [stdout] [Epoch 315]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904070994
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891913599
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041694724
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891815361
[INFO] [stdout] [Epoch 316]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904074414
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198919101695
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041661878
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918186355
[INFO] [stdout] [Epoch 317]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904077569
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891907005
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041631577
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891821654
[INFO] [stdout] [Epoch 318]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040804783
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891904084
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904160364
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891824438
[INFO] [stdout] [Epoch 319]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040831628
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891901389
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041577862
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891827007
[INFO] [stdout] [Epoch 320]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249897990408564
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891898902
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041554075
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891829375
[INFO] [stdout] [Epoch 321]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040879243
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891896609
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041532143
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891831558
[INFO] [stdout] [Epoch 322]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904090031
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918944926
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041511918
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891833571
[INFO] [stdout] [Epoch 323]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904091974
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918925386
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904149326
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891835427
[INFO] [stdout] [Epoch 324]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040937669
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918907356
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041476043
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918371373
[INFO] [stdout] [Epoch 325]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249897990409542
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918890725
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904146017
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838715
[INFO] [stdout] [Epoch 326]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040969463
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891887537
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041445518
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918401694
[INFO] [stdout] [Epoch 327]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040983526
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918861187
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041431987
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918415116
[INFO] [stdout] [Epoch 328]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040996524
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891884809
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041419522
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918427495
[INFO] [stdout] [Epoch 329]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041008506
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891883602
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041408023
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918438914
[INFO] [stdout] [Epoch 330]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041019564
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891882485
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.249897990413974
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891844941
[INFO] [stdout] [Epoch 331]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904102976
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891881457
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904138761
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891845911
[INFO] [stdout] [Epoch 332]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041039165
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918805054
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041378574
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918468046
[INFO] [stdout] [Epoch 333]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041047855
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891879626
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904137025
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891847628
[INFO] [stdout] [Epoch 334]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904105587
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918788157
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041362545
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918483867
[INFO] [stdout] [Epoch 335]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041063257
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891878068
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041355454
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918490867
[INFO] [stdout] [Epoch 336]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904107008
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918773746
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041348904
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891849731
[INFO] [stdout] [Epoch 337]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904107636
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918767357
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041342878
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918503235
[INFO] [stdout] [Epoch 338]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904108216
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918761467
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041337304
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918508697
[INFO] [stdout] [Epoch 339]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041087507
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891875602
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041332178
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891851374
[INFO] [stdout] [Epoch 340]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904109245
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891875098
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041327437
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891851838
[INFO] [stdout] [Epoch 341]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041097002
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918746323
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041323052
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891852265
[INFO] [stdout] [Epoch 342]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041101216
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891874202
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041319025
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891852658
[INFO] [stdout] [Epoch 343]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041105087
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918738047
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041315316
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989185302
[INFO] [stdout] [Epoch 344]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041108668
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918734366
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041311875
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891853353
[INFO] [stdout] [Epoch 345]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904111197
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891873097
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.249897990413087
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989185366
[INFO] [stdout] [Epoch 346]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041115018
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918727805
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041305769
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918539433
[INFO] [stdout] [Epoch 347]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904111783
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918724907
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041303074
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891854203
[INFO] [stdout] [Epoch 348]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041120428
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891872221
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041300598
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891854442
[INFO] [stdout] [Epoch 349]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041122826
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918719706
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041298277
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891854663
[INFO] [stdout] [Epoch 350]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041125046
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918717385
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041296157
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918548654
[INFO] [stdout] [Epoch 351]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249897990411271
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891871524
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041294192
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918550524
[INFO] [stdout] [Epoch 352]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041128996
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891871324
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904129237
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918552234
[INFO] [stdout] [Epoch 353]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041130734
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918711407
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041290708
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891855381
[INFO] [stdout] [Epoch 354]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041132343
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891870971
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041289165
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891855525
[INFO] [stdout] [Epoch 355]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041133826
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891870812
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041287744
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918556575
[INFO] [stdout] [Epoch 356]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041135208
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891870664
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041286412
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918557796
[INFO] [stdout] [Epoch 357]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041136473
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891870527
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041285202
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918558907
[INFO] [stdout] [Epoch 358]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904113765
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918703996
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904128408
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891855992
[INFO] [stdout] [Epoch 359]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041138727
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891870282
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041283048
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918560866
[INFO] [stdout] [Epoch 360]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041139732
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891870172
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904128207
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891856174
[INFO] [stdout] [Epoch 361]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904114067
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918700693
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041281183
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891856254
[INFO] [stdout] [Epoch 362]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904114153
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891869973
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904128036
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918563264
[INFO] [stdout] [Epoch 363]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041142324
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918698834
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041279606
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918563914
[INFO] [stdout] [Epoch 364]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041143054
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918697995
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041278896
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918564513
[INFO] [stdout] [Epoch 365]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041143732
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918697224
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041278254
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891856507
[INFO] [stdout] [Epoch 366]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904114437
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989186965
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041277644
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891856558
[INFO] [stdout] [Epoch 367]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041144953
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891869581
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.249897990412771
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891856603
[INFO] [stdout] [Epoch 368]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041145497
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918695164
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041276567
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891856645
[INFO] [stdout] [Epoch 369]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041145996
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918694576
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904127609
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891856682
[INFO] [stdout] [Epoch 370]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041146463
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891869401
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041275657
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891856716
[INFO] [stdout] [Epoch 371]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041146885
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891869349
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041275257
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891856748
[INFO] [stdout] [Epoch 372]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041147273
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891869301
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904127488
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891856775
[INFO] [stdout] [Epoch 373]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041147634
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918692533
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041274524
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918568
[INFO] [stdout] [Epoch 374]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041147978
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918692095
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041274213
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918568227
[INFO] [stdout] [Epoch 375]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249897990411483
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891869167
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041273902
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891856843
[INFO] [stdout] [Epoch 376]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249897990411486
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891869129
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041273603
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891856861
[INFO] [stdout] [Epoch 377]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041148866
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891869092
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041273358
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891856877
[INFO] [stdout] [Epoch 378]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041149122
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891869056
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041273125
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891856892
[INFO] [stdout] [Epoch 379]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904114936
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918690224
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041272892
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918569043
[INFO] [stdout] [Epoch 380]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041149582
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891868991
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904127268
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918569143
[INFO] [stdout] [Epoch 381]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041149788
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918689613
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041272492
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918569226
[INFO] [stdout] [Epoch 382]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041149965
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891868933
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041272326
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918569315
[INFO] [stdout] [Epoch 383]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041150138
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891868906
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904127217
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891856938
[INFO] [stdout] [Epoch 384]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041150304
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918688797
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041271993
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918569437
[INFO] [stdout] [Epoch 385]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115046
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918688536
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041271848
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918569487
[INFO] [stdout] [Epoch 386]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041150604
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891868831
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041271715
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891856952
[INFO] [stdout] [Epoch 387]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041150737
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918688076
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041271593
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918569554
[INFO] [stdout] [Epoch 388]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115087
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891868785
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904127147
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918569576
[INFO] [stdout] [Epoch 389]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041150987
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891868762
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904127136
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918569576
[INFO] [stdout] [Epoch 390]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041151098
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918687415
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904127125
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918569587
[INFO] [stdout] [Epoch 391]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115121
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918687215
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904127116
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989185696
[INFO] [stdout] [Epoch 392]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115131
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891868701
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904127107
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918569587
[INFO] [stdout] [Epoch 393]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041151403
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918686827
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041270971
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918569587
[INFO] [stdout] [Epoch 394]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041151498
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891868662
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041270883
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918569576
[INFO] [stdout] [Epoch 395]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115158
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918686444
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041270805
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918569554
[INFO] [stdout] [Epoch 396]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041151664
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891868627
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041270727
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891856953
[INFO] [stdout] [Epoch 397]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041151736
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918686105
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904127066
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989185695
[INFO] [stdout] [Epoch 398]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041151803
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918685933
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041270605
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918569465
[INFO] [stdout] [Epoch 399]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115187
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918685783
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904127055
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918569415
[INFO] [stdout] [Epoch 400]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115193
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918685605
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041270483
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891856938
[INFO] [stdout] [Epoch 401]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041151997
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918685444
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041270427
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891856935
[INFO] [stdout] [Epoch 402]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041152064
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891868529
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904127036
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918569304
[INFO] [stdout] [Epoch 403]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115212
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891868514
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041270316
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891856926
[INFO] [stdout] [Epoch 404]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041152164
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918684995
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041270272
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918569204
[INFO] [stdout] [Epoch 405]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115222
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918684845
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041270228
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918569154
[INFO] [stdout] [Epoch 406]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041152275
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989186847
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041270172
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891856909
[INFO] [stdout] [Epoch 407]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041152314
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891868456
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904127014
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891856903
[INFO] [stdout] [Epoch 408]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041152358
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891868442
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041270094
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918569
[INFO] [stdout] [Epoch 409]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041152402
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891868427
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904127005
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918568943
[INFO] [stdout] [Epoch 410]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041152452
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918684134
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041270028
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891856887
[INFO] [stdout] [Epoch 411]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115249
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918683995
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126996
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918568826
[INFO] [stdout] [Epoch 412]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041152547
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918683834
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041269928
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891856877
[INFO] [stdout] [Epoch 413]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041152586
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989186837
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041269906
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918568704
[INFO] [stdout] [Epoch 414]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041152613
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891868358
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041269872
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891856864
[INFO] [stdout] [Epoch 415]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041152652
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891868345
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126984
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918568566
[INFO] [stdout] [Epoch 416]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115269
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918683307
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041269795
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891856851
[INFO] [stdout] [Epoch 417]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041152735
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891868317
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126976
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918568443
[INFO] [stdout] [Epoch 418]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041152758
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918683046
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126974
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918568366
[INFO] [stdout] [Epoch 419]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041152796
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891868292
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041269706
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918568294
[INFO] [stdout] [Epoch 420]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115283
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918682796
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041269684
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918568227
[INFO] [stdout] [Epoch 421]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041152852
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891868266
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126966
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891856815
[INFO] [stdout] [Epoch 422]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115288
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918682547
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126964
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891856808
[INFO] [stdout] [Epoch 423]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041152902
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891868242
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041269617
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918568
[INFO] [stdout] [Epoch 424]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115294
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918682297
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041269595
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891856793
[INFO] [stdout] [Epoch 425]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041152969
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891868216
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126955
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891856788
[INFO] [stdout] [Epoch 426]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041153002
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918682036
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041269517
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891856782
[INFO] [stdout] [Epoch 427]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041153035
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918681914
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041269506
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891856774
[INFO] [stdout] [Epoch 428]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041153068
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918681775
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041269473
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918567683
[INFO] [stdout] [Epoch 429]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041153107
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918681653
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126945
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918567605
[INFO] [stdout] [Epoch 430]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041153135
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918681525
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041269417
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891856754
[INFO] [stdout] [Epoch 431]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041153163
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891868139
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041269395
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918567455
[INFO] [stdout] [Epoch 432]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115319
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918681275
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041269362
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989185674
[INFO] [stdout] [Epoch 433]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041153224
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891868114
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126934
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891856732
[INFO] [stdout] [Epoch 434]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041153252
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918681015
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041269317
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918567256
[INFO] [stdout] [Epoch 435]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115328
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891868089
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041269295
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891856717
[INFO] [stdout] [Epoch 436]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041153313
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891868077
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041269262
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918567106
[INFO] [stdout] [Epoch 437]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041153346
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891868064
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041269228
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891856704
[INFO] [stdout] [Epoch 438]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041153374
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891868052
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041269217
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891856696
[INFO] [stdout] [Epoch 439]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115339
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918680415
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041269206
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918566867
[INFO] [stdout] [Epoch 440]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041153407
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918680293
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041269195
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891856679
[INFO] [stdout] [Epoch 441]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041153424
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891868019
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041269173
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891856671
[INFO] [stdout] [Epoch 442]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115344
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918680076
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126915
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891856663
[INFO] [stdout] [Epoch 443]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041153463
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918679954
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126914
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891856655
[INFO] [stdout] [Epoch 444]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041153485
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918679827
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041269117
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918566473
[INFO] [stdout] [Epoch 445]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041153513
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918679716
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041269095
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918566406
[INFO] [stdout] [Epoch 446]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041153535
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891867959
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041269073
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918566323
[INFO] [stdout] [Epoch 447]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041153574
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918679466
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126904
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918566256
[INFO] [stdout] [Epoch 448]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041153596
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891867934
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041269017
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891856619
[INFO] [stdout] [Epoch 449]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041153624
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918679216
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041268995
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918566134
[INFO] [stdout] [Epoch 450]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041153662
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918679105
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041268973
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891856606
[INFO] [stdout] [Epoch 451]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041153685
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918678966
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126895
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918565984
[INFO] [stdout] [Epoch 452]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041153712
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918678855
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041268917
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891856592
[INFO] [stdout] [Epoch 453]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115374
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891867873
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041268895
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891856585
[INFO] [stdout] [Epoch 454]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041153768
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918678605
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041268873
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891856578
[INFO] [stdout] [Epoch 455]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041153796
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918678467
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126885
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989185657
[INFO] [stdout] [Epoch 456]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115383
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918678356
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126883
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918565635
[INFO] [stdout] [Epoch 457]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041153862
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918678217
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041268806
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891856557
[INFO] [stdout] [Epoch 458]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115389
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918678106
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041268773
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918565485
[INFO] [stdout] [Epoch 459]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041153912
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918677984
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126875
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891856542
[INFO] [stdout] [Epoch 460]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041153946
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918677856
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041268718
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891856535
[INFO] [stdout] [Epoch 461]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115398
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918677734
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041268707
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918565274
[INFO] [stdout] [Epoch 462]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041153996
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891867762
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041268684
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989185652
[INFO] [stdout] [Epoch 463]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041154018
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918677495
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041268662
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918565113
[INFO] [stdout] [Epoch 464]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115404
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891867738
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126864
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918565035
[INFO] [stdout] [Epoch 465]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041154057
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891867727
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126863
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891856495
[INFO] [stdout] [Epoch 466]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041154073
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918677157
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041268618
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918564874
[INFO] [stdout] [Epoch 467]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249897990411541
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891867704
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041268607
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918564796
[INFO] [stdout] [Epoch 468]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041154118
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891867693
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041268584
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891856472
[INFO] [stdout] [Epoch 469]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115414
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891867681
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041268562
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918564647
[INFO] [stdout] [Epoch 470]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041154168
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891867669
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126853
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891856458
[INFO] [stdout] [Epoch 471]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041154195
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891867657
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041268518
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989185645
[INFO] [stdout] [Epoch 472]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041154212
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891867646
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041268507
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918564424
[INFO] [stdout] [Epoch 473]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041154234
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891867634
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041268485
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891856434
[INFO] [stdout] [Epoch 474]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041154262
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918676224
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041268473
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918564263
[INFO] [stdout] [Epoch 475]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115428
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918676113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126845
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918564197
[INFO] [stdout] [Epoch 476]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249897990411543
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918675985
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041268418
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918564114
[INFO] [stdout] [Epoch 477]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041154334
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918675863
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041268396
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918564047
[INFO] [stdout] [Epoch 478]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041154367
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891867574
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041268373
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891856398
[INFO] [stdout] [Epoch 479]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041154395
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918675613
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126834
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918563925
[INFO] [stdout] [Epoch 480]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041154429
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891867549
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041268307
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891856386
[INFO] [stdout] [Epoch 481]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041154462
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918675363
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041268296
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918563786
[INFO] [stdout] [Epoch 482]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041154484
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891867525
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041268274
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891856371
[INFO] [stdout] [Epoch 483]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041154517
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918675125
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126824
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891856364
[INFO] [stdout] [Epoch 484]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041154545
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918675
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041268207
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918563575
[INFO] [stdout] [Epoch 485]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041154573
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891867488
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041268196
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891856349
[INFO] [stdout] [Epoch 486]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249897990411546
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918674753
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041268163
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918563436
[INFO] [stdout] [Epoch 487]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041154634
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891867464
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041268151
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891856336
[INFO] [stdout] [Epoch 488]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041154656
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918674514
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126813
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918563286
[INFO] [stdout] [Epoch 489]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041154678
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891867439
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041268107
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891856322
[INFO] [stdout] [Epoch 490]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041154712
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918674275
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041268063
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891856314
[INFO] [stdout] [Epoch 491]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041154734
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918674153
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041268052
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918563064
[INFO] [stdout] [Epoch 492]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115475
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918674053
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126803
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891856299
[INFO] [stdout] [Epoch 493]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041154778
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918673937
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126803
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918562903
[INFO] [stdout] [Epoch 494]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041154795
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918673826
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041268007
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918562837
[INFO] [stdout] [Epoch 495]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041154817
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989186737
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041267996
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891856276
[INFO] [stdout] [Epoch 496]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041154834
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918673587
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041267974
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918562676
[INFO] [stdout] [Epoch 497]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041154856
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918673487
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041267952
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989185626
[INFO] [stdout] [Epoch 498]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041154878
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891867336
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126794
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891856253
[INFO] [stdout] [Epoch 499]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041154895
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891867325
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041267918
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918562454
[INFO] [stdout] [Epoch 500]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041154923
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891867312
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041267896
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891856238
[INFO] [stdout] [Epoch 501]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115495
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891867301
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041267874
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918562315
[INFO] [stdout] [Epoch 502]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041154978
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891867288
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041267852
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891856225
[INFO] [stdout] [Epoch 503]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041155006
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891867276
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041267818
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891856218
[INFO] [stdout] [Epoch 504]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115504
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891867265
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041267796
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989185621
[INFO] [stdout] [Epoch 505]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115506
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891867253
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041267774
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891856203
[INFO] [stdout] [Epoch 506]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115509
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891867241
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041267752
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918561965
[INFO] [stdout] [Epoch 507]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115511
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891867228
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126773
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989185619
[INFO] [stdout] [Epoch 508]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115514
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891867217
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041267707
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918561827
[INFO] [stdout] [Epoch 509]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041155167
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918672044
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041267696
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891856175
[INFO] [stdout] [Epoch 510]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115519
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918671933
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041267663
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891856168
[INFO] [stdout] [Epoch 511]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041155222
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891867181
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126764
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918561627
[INFO] [stdout] [Epoch 512]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115526
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891867167
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041267607
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918561543
[INFO] [stdout] [Epoch 513]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041155283
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891867156
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041267585
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918561477
[INFO] [stdout] [Epoch 514]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115531
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918671444
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041267563
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891856141
[INFO] [stdout] [Epoch 515]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115534
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891867132
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126753
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918561344
[INFO] [stdout] [Epoch 516]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115536
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918671206
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126753
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891856125
[INFO] [stdout] [Epoch 517]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041155372
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918671106
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041267519
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891856117
[INFO] [stdout] [Epoch 518]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041155383
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918670995
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041267496
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918561094
[INFO] [stdout] [Epoch 519]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115541
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891867088
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041267485
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891856101
[INFO] [stdout] [Epoch 520]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041155428
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918670767
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041267474
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918560933
[INFO] [stdout] [Epoch 521]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115545
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891867065
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041267452
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918560855
[INFO] [stdout] [Epoch 522]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041155467
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891867054
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126744
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891856079
[INFO] [stdout] [Epoch 523]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041155483
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891867044
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126742
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918560705
[INFO] [stdout] [Epoch 524]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115551
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891867031
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041267396
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891856064
[INFO] [stdout] [Epoch 525]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115554
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989186702
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041267374
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891856056
[INFO] [stdout] [Epoch 526]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115556
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918670084
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041267352
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918560494
[INFO] [stdout] [Epoch 527]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115559
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918669973
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126734
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891856041
[INFO] [stdout] [Epoch 528]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115561
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891866986
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126733
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918560333
[INFO] [stdout] [Epoch 529]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041155633
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918669746
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041267308
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918560267
[INFO] [stdout] [Epoch 530]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041155655
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918669624
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041267274
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918560195
[INFO] [stdout] [Epoch 531]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041155678
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918669507
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041267252
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918560117
[INFO] [stdout] [Epoch 532]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249897990411557
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918669385
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126723
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891856005
[INFO] [stdout] [Epoch 533]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041155739
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918669274
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041267208
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918559995
[INFO] [stdout] [Epoch 534]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041155766
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918669146
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041267186
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891855992
[INFO] [stdout] [Epoch 535]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041155794
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918669024
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041267152
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918559856
[INFO] [stdout] [Epoch 536]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041155822
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918668896
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126713
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891855979
[INFO] [stdout] [Epoch 537]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115585
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918668785
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041267108
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918559734
[INFO] [stdout] [Epoch 538]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041155888
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891866866
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041267086
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918559667
[INFO] [stdout] [Epoch 539]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041155916
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918668547
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126704
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918559595
[INFO] [stdout] [Epoch 540]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041155944
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891866841
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126702
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891855953
[INFO] [stdout] [Epoch 541]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041155977
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918668297
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041266997
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891855946
[INFO] [stdout] [Epoch 542]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041155994
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918668186
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041266975
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918559384
[INFO] [stdout] [Epoch 543]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041156022
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891866807
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041266963
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989185593
[INFO] [stdout] [Epoch 544]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041156033
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891866796
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041266952
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918559223
[INFO] [stdout] [Epoch 545]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041156052
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891866784
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126694
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918559145
[INFO] [stdout] [Epoch 546]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115607
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891866773
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126693
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918559073
[INFO] [stdout] [Epoch 547]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041156086
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891866763
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041266908
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918558995
[INFO] [stdout] [Epoch 548]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041156108
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918667514
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041266897
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891855893
[INFO] [stdout] [Epoch 549]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041156135
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918667403
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041266875
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891855885
[INFO] [stdout] [Epoch 550]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041156158
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918667287
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126684
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891855878
[INFO] [stdout] [Epoch 551]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115618
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918667176
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126683
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989185587
[INFO] [stdout] [Epoch 552]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041156197
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918667065
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041266808
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918558624
[INFO] [stdout] [Epoch 553]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041156224
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891866695
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041266797
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918558546
[INFO] [stdout] [Epoch 554]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115624
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918666837
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041266786
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918558474
[INFO] [stdout] [Epoch 555]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041156263
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891866673
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041266764
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918558407
[INFO] [stdout] [Epoch 556]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041156285
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891866661
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041266741
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891855834
[INFO] [stdout] [Epoch 557]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041156313
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891866649
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126672
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918558274
[INFO] [stdout] [Epoch 558]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115634
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891866637
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041266697
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989185582
[INFO] [stdout] [Epoch 559]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041156374
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891866626
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041266664
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918558135
[INFO] [stdout] [Epoch 560]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041156402
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891866613
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041266653
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891855807
[INFO] [stdout] [Epoch 561]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041156424
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891866602
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126662
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918558
[INFO] [stdout] [Epoch 562]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041156446
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891866591
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041266597
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891855793
[INFO] [stdout] [Epoch 563]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041156469
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891866578
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041266575
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918557863
[INFO] [stdout] [Epoch 564]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041156496
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891866567
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041266553
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918557796
[INFO] [stdout] [Epoch 565]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041156535
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918665555
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126653
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891855773
[INFO] [stdout] [Epoch 566]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041156563
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891866543
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041266497
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891855767
[INFO] [stdout] [Epoch 567]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115659
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918665305
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041266475
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989185576
[INFO] [stdout] [Epoch 568]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041156624
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891866518
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041266453
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918557524
[INFO] [stdout] [Epoch 569]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041156652
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918665055
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126642
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891855746
[INFO] [stdout] [Epoch 570]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041156674
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918664944
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041266408
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918557386
[INFO] [stdout] [Epoch 571]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041156696
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918664855
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041266386
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891855731
[INFO] [stdout] [Epoch 572]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041156713
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891866474
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041266375
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891855724
[INFO] [stdout] [Epoch 573]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115674
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891866463
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041266353
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918557164
[INFO] [stdout] [Epoch 574]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041156757
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918664517
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041266342
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891855709
[INFO] [stdout] [Epoch 575]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115678
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989186644
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126632
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918557014
[INFO] [stdout] [Epoch 576]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115679
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891866429
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041266308
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918556936
[INFO] [stdout] [Epoch 577]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041156813
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891866417
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041266275
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891855688
[INFO] [stdout] [Epoch 578]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041156846
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891866406
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041266264
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989185568
[INFO] [stdout] [Epoch 579]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041156863
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891866395
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041266242
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891855673
[INFO] [stdout] [Epoch 580]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041156885
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918663834
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126623
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918556653
[INFO] [stdout] [Epoch 581]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041156913
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918663723
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041266209
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918556575
[INFO] [stdout] [Epoch 582]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041156935
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918663606
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041266186
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918556514
[INFO] [stdout] [Epoch 583]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041156957
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918663495
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041266164
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891855645
[INFO] [stdout] [Epoch 584]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041156985
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918663384
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041266142
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891855637
[INFO] [stdout] [Epoch 585]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041157013
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891866327
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126613
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918556303
[INFO] [stdout] [Epoch 586]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041157035
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918663157
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126611
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891855623
[INFO] [stdout] [Epoch 587]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041157057
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891866304
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041266098
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918556164
[INFO] [stdout] [Epoch 588]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115708
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891866293
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041266075
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989185561
[INFO] [stdout] [Epoch 589]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041157107
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891866281
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041266042
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891855603
[INFO] [stdout] [Epoch 590]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041157135
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989186627
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126602
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891855596
[INFO] [stdout] [Epoch 591]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041157162
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891866257
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041265987
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918555904
[INFO] [stdout] [Epoch 592]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115719
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891866245
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041265964
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918555837
[INFO] [stdout] [Epoch 593]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041157218
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891866234
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126593
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891855577
[INFO] [stdout] [Epoch 594]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041157257
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918662213
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126592
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989185557
[INFO] [stdout] [Epoch 595]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115728
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918662113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041265887
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891855562
[INFO] [stdout] [Epoch 596]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115729
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918662
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041265875
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891855554
[INFO] [stdout] [Epoch 597]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041157312
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918661885
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041265864
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918555476
[INFO] [stdout] [Epoch 598]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041157335
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918661774
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041265842
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918555393
[INFO] [stdout] [Epoch 599]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115735
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891866166
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126583
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918555326
[INFO] [stdout] [Epoch 600]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041157373
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891866156
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126582
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891855525
[INFO] [stdout] [Epoch 601]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115739
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891866146
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041265798
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918555193
[INFO] [stdout] [Epoch 602]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041157418
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891866134
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041265776
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891855512
[INFO] [stdout] [Epoch 603]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041157446
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891866122
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041265753
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918555043
[INFO] [stdout] [Epoch 604]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041157462
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918661114
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041265742
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918554976
[INFO] [stdout] [Epoch 605]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115749
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918661
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126572
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989185549
[INFO] [stdout] [Epoch 606]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041157512
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891866089
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126571
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918554827
[INFO] [stdout] [Epoch 607]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041157534
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918660775
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041265687
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891855476
[INFO] [stdout] [Epoch 608]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041157557
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918660664
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041265676
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891855468
[INFO] [stdout] [Epoch 609]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115758
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891866055
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041265642
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918554627
[INFO] [stdout] [Epoch 610]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249897990411576
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918660437
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126563
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918554543
[INFO] [stdout] [Epoch 611]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041157623
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918660326
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126561
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918554477
[INFO] [stdout] [Epoch 612]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115764
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891866022
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041265587
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891855441
[INFO] [stdout] [Epoch 613]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041157668
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989186601
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041265565
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918554344
[INFO] [stdout] [Epoch 614]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041157695
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891865998
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041265542
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891855427
[INFO] [stdout] [Epoch 615]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041157723
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891865987
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126552
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918554205
[INFO] [stdout] [Epoch 616]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115775
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891865976
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041265487
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891855414
[INFO] [stdout] [Epoch 617]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041157784
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891865964
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041265465
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891855408
[INFO] [stdout] [Epoch 618]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041157812
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891865952
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041265442
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891855401
[INFO] [stdout] [Epoch 619]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041157834
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918659415
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126542
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918553944
[INFO] [stdout] [Epoch 620]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041157856
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918659304
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041265398
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891855388
[INFO] [stdout] [Epoch 621]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041157884
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918659193
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041265376
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891855381
[INFO] [stdout] [Epoch 622]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041157912
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918659065
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041265354
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891855375
[INFO] [stdout] [Epoch 623]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115794
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918658943
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041265331
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918553683
[INFO] [stdout] [Epoch 624]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041157956
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891865884
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126531
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918553594
[INFO] [stdout] [Epoch 625]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041157978
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891865874
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041265298
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918553517
[INFO] [stdout] [Epoch 626]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041157995
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918658627
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041265276
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918553444
[INFO] [stdout] [Epoch 627]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041158017
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891865851
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041265254
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891855339
[INFO] [stdout] [Epoch 628]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041158045
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989186584
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041265243
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891855331
[INFO] [stdout] [Epoch 629]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041158067
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918658294
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041265232
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918553245
[INFO] [stdout] [Epoch 630]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115809
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918658183
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126521
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891855317
[INFO] [stdout] [Epoch 631]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041158106
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918658083
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041265198
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918553095
[INFO] [stdout] [Epoch 632]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041158128
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918657966
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041265176
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891855304
[INFO] [stdout] [Epoch 633]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115815
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918657855
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041265165
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891855295
[INFO] [stdout] [Epoch 634]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041158173
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891865774
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041265143
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891855288
[INFO] [stdout] [Epoch 635]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041158195
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891865763
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041265132
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891855281
[INFO] [stdout] [Epoch 636]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041158212
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891865753
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126511
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918552734
[INFO] [stdout] [Epoch 637]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115824
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891865742
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041265087
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891855268
[INFO] [stdout] [Epoch 638]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041158267
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989186573
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041265065
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918552606
[INFO] [stdout] [Epoch 639]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115829
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918657195
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041265043
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891855255
[INFO] [stdout] [Epoch 640]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041158312
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891865707
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126502
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918552484
[INFO] [stdout] [Epoch 641]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115834
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891865696
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041264998
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918552406
[INFO] [stdout] [Epoch 642]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041158373
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918656845
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041264965
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918552345
[INFO] [stdout] [Epoch 643]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249897990411584
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918656723
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041264954
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891855228
[INFO] [stdout] [Epoch 644]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041158417
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891865662
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041264932
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891855221
[INFO] [stdout] [Epoch 645]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115844
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891865652
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126491
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918552145
[INFO] [stdout] [Epoch 646]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041158467
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918656395
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041264887
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918552073
[INFO] [stdout] [Epoch 647]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041158495
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891865628
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041264865
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891855202
[INFO] [stdout] [Epoch 648]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041158517
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891865617
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041264846
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891855195
[INFO] [stdout] [Epoch 649]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041158545
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891865605
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041264824
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918551884
[INFO] [stdout] [Epoch 650]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041158584
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891865593
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126479
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891855183
[INFO] [stdout] [Epoch 651]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115861
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891865582
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041264757
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918551757
[INFO] [stdout] [Epoch 652]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041158633
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989186557
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041264735
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891855169
[INFO] [stdout] [Epoch 653]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041158656
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891865559
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041264724
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918551624
[INFO] [stdout] [Epoch 654]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041158672
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918655496
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041264713
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918551546
[INFO] [stdout] [Epoch 655]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041158695
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918655385
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041264701
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918551474
[INFO] [stdout] [Epoch 656]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115871
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918655274
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126468
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918551407
[INFO] [stdout] [Epoch 657]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041158728
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891865517
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041264668
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891855133
[INFO] [stdout] [Epoch 658]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115875
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891865507
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041264657
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891855125
[INFO] [stdout] [Epoch 659]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041158767
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891865495
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041264635
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891855118
[INFO] [stdout] [Epoch 660]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041158794
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891865484
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041264613
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918551124
[INFO] [stdout] [Epoch 661]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041158817
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891865473
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041264601
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918551046
[INFO] [stdout] [Epoch 662]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041158833
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918654625
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126458
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891855098
[INFO] [stdout] [Epoch 663]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115886
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918654525
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041264568
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891855091
[INFO] [stdout] [Epoch 664]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041158878
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918654414
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041264546
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891855083
[INFO] [stdout] [Epoch 665]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249897990411589
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918654297
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041264524
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918550763
[INFO] [stdout] [Epoch 666]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041158922
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918654197
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041264513
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918550697
[INFO] [stdout] [Epoch 667]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041158944
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891865409
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126449
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918550624
[INFO] [stdout] [Epoch 668]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041158967
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891865398
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041264457
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891855057
[INFO] [stdout] [Epoch 669]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041159
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891865386
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041264446
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989185505
[INFO] [stdout] [Epoch 670]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041159017
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918653753
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041264424
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918550436
[INFO] [stdout] [Epoch 671]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041159044
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891865364
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041264413
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891855035
[INFO] [stdout] [Epoch 672]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041159066
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918653525
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126439
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918550286
[INFO] [stdout] [Epoch 673]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115909
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918653414
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041264368
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891855023
[INFO] [stdout] [Epoch 674]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041159116
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918653303
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041264335
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918550175
[INFO] [stdout] [Epoch 675]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115915
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918653187
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041264313
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989185501
[INFO] [stdout] [Epoch 676]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041159177
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918653076
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126428
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918550047
[INFO] [stdout] [Epoch 677]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041159205
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891865296
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041264268
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891854998
[INFO] [stdout] [Epoch 678]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041159227
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891865285
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041264246
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918549914
[INFO] [stdout] [Epoch 679]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115925
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918652737
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041264224
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891854985
[INFO] [stdout] [Epoch 680]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041159283
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891865262
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041264202
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918549775
[INFO] [stdout] [Epoch 681]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249897990411593
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891865252
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126418
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989185497
[INFO] [stdout] [Epoch 682]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041159316
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918652415
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041264169
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891854963
[INFO] [stdout] [Epoch 683]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041159333
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918652315
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041264146
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918549564
[INFO] [stdout] [Epoch 684]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115936
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918652193
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041264135
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891854949
[INFO] [stdout] [Epoch 685]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041159383
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891865209
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041264113
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918549425
[INFO] [stdout] [Epoch 686]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041159405
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891865199
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126409
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891854936
[INFO] [stdout] [Epoch 687]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041159427
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891865187
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126408
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891854929
[INFO] [stdout] [Epoch 688]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115945
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891865176
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041264046
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891854923
[INFO] [stdout] [Epoch 689]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041159477
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891865165
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041264035
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918549164
[INFO] [stdout] [Epoch 690]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249897990411595
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918651544
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041264013
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989185491
[INFO] [stdout] [Epoch 691]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041159522
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918651444
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126399
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891854902
[INFO] [stdout] [Epoch 692]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115955
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891865133
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126397
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891854895
[INFO] [stdout] [Epoch 693]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041159572
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918651216
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041263946
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891854889
[INFO] [stdout] [Epoch 694]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249897990411596
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918651105
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041263935
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918548826
[INFO] [stdout] [Epoch 695]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115961
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918651
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041263913
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891854876
[INFO] [stdout] [Epoch 696]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041159633
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989186509
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126389
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989185487
[INFO] [stdout] [Epoch 697]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041159666
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891865076
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126387
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891854862
[INFO] [stdout] [Epoch 698]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041159683
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891865066
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041263847
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918548554
[INFO] [stdout] [Epoch 699]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115971
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891865056
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041263824
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989185485
[INFO] [stdout] [Epoch 700]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041159733
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918650445
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041263802
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918548426
[INFO] [stdout] [Epoch 701]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041159766
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918650334
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126378
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891854837
[INFO] [stdout] [Epoch 702]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041159794
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891865022
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041263758
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918548304
[INFO] [stdout] [Epoch 703]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115981
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918650106
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041263747
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918548226
[INFO] [stdout] [Epoch 704]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041159833
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918650017
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041263724
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891854816
[INFO] [stdout] [Epoch 705]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115986
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989186499
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041263713
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989185481
[INFO] [stdout] [Epoch 706]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041159882
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891864979
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126369
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891854803
[INFO] [stdout] [Epoch 707]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115991
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891864968
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126367
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918547965
[INFO] [stdout] [Epoch 708]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041159927
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918649573
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041263647
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989185479
[INFO] [stdout] [Epoch 709]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115995
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918649473
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041263636
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918547816
[INFO] [stdout] [Epoch 710]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904115996
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918649357
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041263613
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891854775
[INFO] [stdout] [Epoch 711]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041159982
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918649257
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041263602
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918547693
[INFO] [stdout] [Epoch 712]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041160005
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918649146
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126359
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918547616
[INFO] [stdout] [Epoch 713]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904116002
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891864904
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041263558
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918547555
[INFO] [stdout] [Epoch 714]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904116005
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891864893
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041263547
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918547477
[INFO] [stdout] [Epoch 715]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041160066
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891864884
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041263536
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989185474
[INFO] [stdout] [Epoch 716]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041160082
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918648724
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041263513
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918547344
[INFO] [stdout] [Epoch 717]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904116011
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918648624
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041263502
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891854727
[INFO] [stdout] [Epoch 718]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041160132
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891864852
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126348
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918547205
[INFO] [stdout] [Epoch 719]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041160154
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891864841
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041263458
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891854715
[INFO] [stdout] [Epoch 720]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041160177
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918648296
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041263447
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891854706
[INFO] [stdout] [Epoch 721]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249897990411602
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891864819
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041263414
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918547
[INFO] [stdout] [Epoch 722]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904116022
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891864808
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126339
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918546944
[INFO] [stdout] [Epoch 723]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041160254
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891864797
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126337
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891854688
[INFO] [stdout] [Epoch 724]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904116027
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918647863
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041263347
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891854682
[INFO] [stdout] [Epoch 725]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041160293
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891864775
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041263336
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918546755
[INFO] [stdout] [Epoch 726]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904116032
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918647636
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041263314
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918546683
[INFO] [stdout] [Epoch 727]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904116035
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918647536
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041263291
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891854663
[INFO] [stdout] [Epoch 728]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041160377
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918647425
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126328
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891854655
[INFO] [stdout] [Epoch 729]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041160393
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891864732
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126327
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918546483
[INFO] [stdout] [Epoch 730]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041160415
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891864721
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041263236
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918546433
[INFO] [stdout] [Epoch 731]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041160443
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891864709
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041263214
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918546356
[INFO] [stdout] [Epoch 732]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041160465
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891864698
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041263192
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989185463
[INFO] [stdout] [Epoch 733]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041160488
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891864688
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126317
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918546233
[INFO] [stdout] [Epoch 734]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041160515
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918646775
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041263147
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891854617
[INFO] [stdout] [Epoch 735]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904116055
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918646653
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041263114
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918546117
[INFO] [stdout] [Epoch 736]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041160576
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891864655
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041263103
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891854603
[INFO] [stdout] [Epoch 737]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041160587
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891864645
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041263092
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891854595
[INFO] [stdout] [Epoch 738]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041160593
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891864636
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126308
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891854588
[INFO] [stdout] [Epoch 739]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904116061
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918646253
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126308
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989185458
[INFO] [stdout] [Epoch 740]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904116062
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918646165
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041263058
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918545734
[INFO] [stdout] [Epoch 741]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041160632
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918646065
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041263047
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918545656
[INFO] [stdout] [Epoch 742]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041160654
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891864596
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041263036
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918545584
[INFO] [stdout] [Epoch 743]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041160676
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891864585
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041263025
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891854552
[INFO] [stdout] [Epoch 744]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041160693
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891864575
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041263003
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891854546
[INFO] [stdout] [Epoch 745]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041160715
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918645643
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041262992
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918545395
[INFO] [stdout] [Epoch 746]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041160737
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891864553
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126297
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918545323
[INFO] [stdout] [Epoch 747]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041160765
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918645415
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041262936
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918545256
[INFO] [stdout] [Epoch 748]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041160787
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918645315
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041262914
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989185452
[INFO] [stdout] [Epoch 749]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041160815
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918645204
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041262892
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918545145
[INFO] [stdout] [Epoch 750]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041160848
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891864509
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126287
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891854509
[INFO] [stdout] [Epoch 751]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904116087
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891864499
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041262847
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918545007
[INFO] [stdout] [Epoch 752]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041160893
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891864487
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041262836
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891854495
[INFO] [stdout] [Epoch 753]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904116092
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891864477
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041262803
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918544885
[INFO] [stdout] [Epoch 754]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041160937
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891864467
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126278
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891854483
[INFO] [stdout] [Epoch 755]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041160965
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918644555
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126277
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918544757
[INFO] [stdout] [Epoch 756]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041160987
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918644444
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041262759
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891854469
[INFO] [stdout] [Epoch 757]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041161015
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891864433
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041262736
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918544624
[INFO] [stdout] [Epoch 758]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041161037
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891864424
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041262714
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891854457
[INFO] [stdout] [Epoch 759]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904116106
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891864413
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041262692
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918544496
[INFO] [stdout] [Epoch 760]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041161087
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891864401
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126267
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891854444
[INFO] [stdout] [Epoch 761]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904116111
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989186439
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041262648
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918544374
[INFO] [stdout] [Epoch 762]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041161137
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989186438
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041262625
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891854432
[INFO] [stdout] [Epoch 763]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041161165
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918643683
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041262603
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891854425
[INFO] [stdout] [Epoch 764]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041161181
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918643583
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126258
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891854419
[INFO] [stdout] [Epoch 765]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904116121
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918643467
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126257
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918544113
[INFO] [stdout] [Epoch 766]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041161231
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891864338
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041262548
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918544046
[INFO] [stdout] [Epoch 767]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041161237
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891864328
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041262536
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891854397
[INFO] [stdout] [Epoch 768]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041161254
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891864317
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041262525
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918543896
[INFO] [stdout] [Epoch 769]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041161276
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891864307
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041262514
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891854383
[INFO] [stdout] [Epoch 770]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041161292
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891864297
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041262503
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918543763
[INFO] [stdout] [Epoch 771]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904116131
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891864288
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126248
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918543697
[INFO] [stdout] [Epoch 772]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904116133
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891864277
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126246
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918543636
[INFO] [stdout] [Epoch 773]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041161354
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918642656
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041262437
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891854357
[INFO] [stdout] [Epoch 774]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904116138
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891864254
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041262414
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918543513
[INFO] [stdout] [Epoch 775]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041161403
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891864244
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041262392
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918543447
[INFO] [stdout] [Epoch 776]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904116143
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918642334
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126237
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918543375
[INFO] [stdout] [Epoch 777]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041161453
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918642223
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126236
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891854331
[INFO] [stdout] [Epoch 778]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904116147
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918642135
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041262337
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891854325
[INFO] [stdout] [Epoch 779]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041161492
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891864202
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041262326
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918543175
[INFO] [stdout] [Epoch 780]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041161515
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891864193
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041262303
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891854312
[INFO] [stdout] [Epoch 781]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041161537
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891864182
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126228
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891854306
[INFO] [stdout] [Epoch 782]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904116157
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989186417
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126226
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918543
[INFO] [stdout] [Epoch 783]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041161598
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891864159
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041262237
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918542936
[INFO] [stdout] [Epoch 784]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904116162
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918641474
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041262215
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891854287
[INFO] [stdout] [Epoch 785]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041161642
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918641385
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041262192
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891854281
[INFO] [stdout] [Epoch 786]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904116167
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918641274
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126216
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918542753
[INFO] [stdout] [Epoch 787]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041161703
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891864116
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041262137
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989185427
[INFO] [stdout] [Epoch 788]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041161725
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918641047
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041262115
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891854263
[INFO] [stdout] [Epoch 789]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041161748
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891864093
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041262115
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918542564
[INFO] [stdout] [Epoch 790]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041161764
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891864084
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041262103
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891854249
[INFO] [stderr] error: test failed, to rerun pass `--lib`
[INFO] [stdout] [Epoch 791]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041161787
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891864073
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126207
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891854245
[INFO] [stdout] [Epoch 792]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904116182
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918640614
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041262048
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891854237
[INFO] [stdout] [Epoch 793]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041161842
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918640525
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041262026
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918542314
[INFO] [stdout] [Epoch 794]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904116186
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918640414
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041262004
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891854224
[INFO] [stdout] [Epoch 795]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041161886
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918640297
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126198
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918542176
[INFO] [stdout] [Epoch 796]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041161903
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891864021
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126197
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989185421
[INFO] [stdout] [Epoch 797]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904116192
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891864012
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126196
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891854204
[INFO] [stdout] [Epoch 798]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904116193
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918640003
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041261948
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891854197
[INFO] [stdout] [Epoch 799]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041161959
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918639903
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041261926
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918541904
[INFO] [stdout] [Epoch 800]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904116198
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989186398
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041261915
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918541826
[INFO] [stdout] [Epoch 801]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041161992
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891863971
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041261904
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891854176
[INFO] [stdout] [Epoch 802]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041162003
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891863962
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041261893
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918541687
[INFO] [stdout] [Epoch 803]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904116202
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918639514
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041261881
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891854162
[INFO] [stdout] [Epoch 804]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041162036
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918639414
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126187
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918541554
[INFO] [stdout] [Epoch 805]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041162053
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918639326
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041261848
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891854149
[INFO] [stdout] [Epoch 806]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041162075
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891863921
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041261826
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918541415
[INFO] [stdout] [Epoch 807]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041162092
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891863912
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041261815
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891854136
[INFO] [stdout] [Epoch 808]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904116212
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891863901
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041261793
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918541304
[INFO] [stdout] [Epoch 809]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041162147
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891863889
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126177
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891854124
[INFO] [stdout] [Epoch 810]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904116217
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891863879
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041261748
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891854118
[INFO] [stdout] [Epoch 811]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041162192
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891863869
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041261715
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891854112
[INFO] [stdout] [Epoch 812]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904116223
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918638565
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041261693
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918541065
[INFO] [stdout] [Epoch 813]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041162258
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918638465
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126167
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918541
[INFO] [stdout] [Epoch 814]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904116228
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891863835
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041261648
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918540943
[INFO] [stdout] [Epoch 815]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041162303
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891863825
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041261637
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891854089
[INFO] [stdout] [Epoch 816]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041162325
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918638143
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041261615
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918540827
[INFO] [stdout] [Epoch 817]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041162358
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891863803
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041261593
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891854076
[INFO] [stdout] [Epoch 818]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041162386
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891863792
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126156
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918540705
[INFO] [stdout] [Epoch 819]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041162414
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918637805
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041261548
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891854064
[INFO] [stdout] [Epoch 820]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904116243
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918637716
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041261526
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918540577
[INFO] [stdout] [Epoch 821]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041162453
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918637605
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041261504
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891854051
[INFO] [stdout] [Epoch 822]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904116248
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891863749
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041261482
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918540455
[INFO] [stdout] [Epoch 823]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041162503
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989186374
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126147
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989185404
[INFO] [stdout] [Epoch 824]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041162525
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918637283
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041261448
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891854033
[INFO] [stdout] [Epoch 825]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041162541
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918637194
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041261437
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891854024
[INFO] [stdout] [Epoch 826]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041162553
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918637105
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041261415
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891854018
[INFO] [stdout] [Epoch 827]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041162575
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891863699
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041261404
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918540116
[INFO] [stdout] [Epoch 828]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041162591
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989186369
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041261393
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891854004
[INFO] [stdout] [Epoch 829]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041162603
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891863681
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041261382
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918539966
[INFO] [stdout] [Epoch 830]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041162625
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918636694
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126136
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891853991
[INFO] [stdout] [Epoch 831]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041162641
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918636606
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041261349
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918539844
[INFO] [stdout] [Epoch 832]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041162658
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918636506
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041261337
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918539766
[INFO] [stdout] [Epoch 833]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904116268
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989186364
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041261315
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918539705
[INFO] [stdout] [Epoch 834]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041162702
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989186363
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041261304
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891853965
[INFO] [stdout] [Epoch 835]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904116272
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989186362
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041261282
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918539583
[INFO] [stdout] [Epoch 836]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904116274
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918636095
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126126
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918539517
[INFO] [stdout] [Epoch 837]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904116277
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918635995
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041261238
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918539456
[INFO] [stdout] [Epoch 838]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041162797
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891863588
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041261215
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989185394
[INFO] [stdout] [Epoch 839]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041162825
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891863578
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041261204
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918539345
[INFO] [stdout] [Epoch 840]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904116284
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891863568
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041261182
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918539267
[INFO] [stdout] [Epoch 841]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041162863
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918635584
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126116
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891853921
[INFO] [stdout] [Epoch 842]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904116288
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918635473
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126115
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891853915
[INFO] [stdout] [Epoch 843]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041162902
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918635373
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041261126
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918539084
[INFO] [stdout] [Epoch 844]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904116293
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891863527
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041261093
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891853903
[INFO] [stdout] [Epoch 845]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041162958
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918635157
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041261082
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891853897
[INFO] [stdout] [Epoch 846]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904116298
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891863504
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126106
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918538917
[INFO] [stdout] [Epoch 847]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041163008
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891863494
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041261038
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918538845
[INFO] [stdout] [Epoch 848]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041163036
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891863484
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041261004
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891853879
[INFO] [stdout] [Epoch 849]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041163063
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918634724
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041260982
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918538745
[INFO] [stdout] [Epoch 850]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041163097
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891863461
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126096
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891853869
[INFO] [stdout] [Epoch 851]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904116312
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918634513
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041260927
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891853862
[INFO] [stdout] [Epoch 852]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904116314
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891863441
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041260927
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891853855
[INFO] [stdout] [Epoch 853]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041163163
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918634296
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041260904
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918538506
[INFO] [stdout] [Epoch 854]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904116319
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891863419
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126087
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891853843
[INFO] [stdout] [Epoch 855]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041163213
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891863409
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126086
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891853836
[INFO] [stdout] [Epoch 856]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904116322
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918634
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126086
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891853829
[INFO] [stdout] [Epoch 857]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041163235
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891863391
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041260838
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918538234
[INFO] [stdout] [Epoch 858]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041163258
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918633797
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041260827
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918538157
[INFO] [stdout] [Epoch 859]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904116327
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891863372
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041260816
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891853809
[INFO] [stdout] [Epoch 860]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041163285
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918633614
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041260805
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891853802
[INFO] [stdout] [Epoch 861]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041163302
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918633525
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041260782
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891853795
[INFO] [stdout] [Epoch 862]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041163324
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918633414
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126077
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918537896
[INFO] [stdout] [Epoch 863]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904116334
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891863332
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126075
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891853783
[INFO] [stdout] [Epoch 864]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041163363
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891863322
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126075
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918537757
[INFO] [stdout] [Epoch 865]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041163374
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891863313
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041260738
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891853768
[INFO] [stdout] [Epoch 866]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904116339
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918633025
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041260705
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918537635
[INFO] [stdout] [Epoch 867]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041163419
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918632925
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041260694
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891853757
[INFO] [stdout] [Epoch 868]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041163435
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918632825
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126067
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918537496
[INFO] [stdout] [Epoch 869]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041163452
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891863273
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126066
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891853743
[INFO] [stdout] [Epoch 870]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904116348
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891863262
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126065
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918537374
[INFO] [stdout] [Epoch 871]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041163502
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918632526
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041260627
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891853732
[INFO] [stdout] [Epoch 872]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041163518
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918632415
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041260605
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891853725
[INFO] [stdout] [Epoch 873]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041163546
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918632326
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041260582
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891853719
[INFO] [stdout] [Epoch 874]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041163574
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891863221
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126055
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918537146
[INFO] [stdout] [Epoch 875]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041163602
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989186321
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041260527
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891853708
[INFO] [stdout] [Epoch 876]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904116363
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918631987
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041260505
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918537035
[INFO] [stdout] [Epoch 877]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041163663
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891863188
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041260483
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891853698
[INFO] [stdout] [Epoch 878]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904116368
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891863178
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126046
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891853692
[INFO] [stdout] [Epoch 879]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041163702
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918631676
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041260438
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891853685
[INFO] [stdout] [Epoch 880]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041163735
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918631576
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041260416
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918536797
[INFO] [stdout] [Epoch 881]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041163757
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918631465
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041260394
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891853674
[INFO] [stdout] [Epoch 882]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041163785
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891863135
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041260372
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918536686
[INFO] [stdout] [Epoch 883]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041163807
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891863125
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126036
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918536613
[INFO] [stdout] [Epoch 884]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904116383
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891863116
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041260338
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891853656
[INFO] [stdout] [Epoch 885]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041163857
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918631054
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041260305
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891853649
[INFO] [stdout] [Epoch 886]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041163874
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918630943
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041260294
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918536425
[INFO] [stdout] [Epoch 887]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904116389
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891863085
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041260283
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891853635
[INFO] [stdout] [Epoch 888]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041163907
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891863076
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041260272
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918536297
[INFO] [stdout] [Epoch 889]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041163924
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891863067
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126026
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891853622
[INFO] [stdout] [Epoch 890]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904116394
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918630566
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041260238
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918536164
[INFO] [stdout] [Epoch 891]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041163963
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918630466
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041260227
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891853611
[INFO] [stdout] [Epoch 892]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041163974
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891863038
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041260216
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918536025
[INFO] [stdout] [Epoch 893]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041163996
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918630283
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041260205
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891853597
[INFO] [stdout] [Epoch 894]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041164018
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891863017
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041260194
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918535903
[INFO] [stdout] [Epoch 895]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904116403
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918630083
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041260172
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918535825
[INFO] [stdout] [Epoch 896]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041164046
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891862999
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126016
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918535764
[INFO] [stdout] [Epoch 897]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041164057
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989186299
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041260138
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891853571
[INFO] [stdout] [Epoch 898]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041164085
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891862979
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041260127
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891853563
[INFO] [stdout] [Epoch 899]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041164107
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918629694
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041260116
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918535575
[INFO] [stdout] [Epoch 900]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041164124
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918629583
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041260094
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918535514
[INFO] [stdout] [Epoch 901]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041164146
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918629495
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041260072
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891853547
[INFO] [stdout] [Epoch 902]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041164173
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891862938
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041260038
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918535403
[INFO] [stdout] [Epoch 903]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249897990411642
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891862929
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041260027
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918535337
[INFO] [stdout] [Epoch 904]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041164218
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891862919
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041260016
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891853528
[INFO] [stdout] [Epoch 905]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904116423
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918629084
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041260005
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891853521
[INFO] [stdout] [Epoch 906]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904116425
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918628995
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041259983
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918535153
[INFO] [stdout] [Epoch 907]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904116428
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891862888
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904125996
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989185351
[INFO] [stdout] [Epoch 908]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249897990411643
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891862879
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041259939
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891853503
[INFO] [stdout] [Epoch 909]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041164323
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891862868
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041259927
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891853497
[INFO] [stdout] [Epoch 910]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904116435
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918628584
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041259894
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918534926
[INFO] [stdout] [Epoch 911]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041164373
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918628473
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041259883
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891853485
[INFO] [stdout] [Epoch 912]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249897990411644
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918628373
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904125985
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918534804
[INFO] [stdout] [Epoch 913]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041164423
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891862827
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041259816
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891853476
[INFO] [stdout] [Epoch 914]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041164457
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918628157
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041259805
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918534687
[INFO] [stdout] [Epoch 915]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041164484
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891862805
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041259783
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891853463
[INFO] [stdout] [Epoch 916]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249897990411645
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891862795
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904125976
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918534576
[INFO] [stdout] [Epoch 917]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041164523
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891862786
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904125975
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989185345
[INFO] [stdout] [Epoch 918]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041164534
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891862777
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041259728
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918534443
[INFO] [stdout] [Epoch 919]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904116455
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891862767
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041259728
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891853437
[INFO] [stdout] [Epoch 920]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041164573
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891862757
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041259717
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918534293
[INFO] [stdout] [Epoch 921]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041164584
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918627474
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041259694
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891853424
[INFO] [stdout] [Epoch 922]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041164606
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918627385
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041259672
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891853418
[INFO] [stdout] [Epoch 923]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904116463
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918627274
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904125966
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891853412
[INFO] [stdout] [Epoch 924]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904116464
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891862718
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904125966
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918534043
[INFO] [stdout] [Epoch 925]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041164662
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891862709
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041259628
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918534
[INFO] [stdout] [Epoch 926]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904116469
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891862698
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041259617
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891853393
[INFO] [stdout] [Epoch 927]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041164706
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918626886
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041259594
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918533866
[INFO] [stdout] [Epoch 928]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041164723
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918626786
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041259572
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918533805
[INFO] [stdout] [Epoch 929]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041164745
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918626697
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041259572
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891853374
[INFO] [stdout] [Epoch 930]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041164762
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989186266
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904125955
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891853368
[INFO] [stdout] [Epoch 931]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041164784
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989186265
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041259528
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918533627
[INFO] [stdout] [Epoch 932]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041164812
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891862639
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041259506
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918533566
[INFO] [stdout] [Epoch 933]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041164823
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918626297
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041259494
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891853349
[INFO] [stdout] [Epoch 934]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041164845
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891862621
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041259483
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891853343
[INFO] [stdout] [Epoch 935]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041164862
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891862611
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904125946
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918533377
[INFO] [stdout] [Epoch 936]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041164884
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918626003
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904125944
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891853331
[INFO] [stdout] [Epoch 937]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041164912
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918625914
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041259417
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891853325
[INFO] [stdout] [Epoch 938]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041164928
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989186258
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041259406
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918533205
[INFO] [stdout] [Epoch 939]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041164956
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989186257
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041259383
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891853314
[INFO] [stdout] [Epoch 940]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041164984
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989186256
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904125935
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918533094
[INFO] [stdout] [Epoch 941]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041165012
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891862548
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041259328
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891853305
[INFO] [stdout] [Epoch 942]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904116504
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891862539
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041259306
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891853298
[INFO] [stdout] [Epoch 943]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041165062
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891862528
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041259295
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891853292
[INFO] [stdout] [Epoch 944]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041165084
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918625187
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041259272
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918532855
[INFO] [stdout] [Epoch 945]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041165112
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918625087
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904125925
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989185328
[INFO] [stdout] [Epoch 946]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041165134
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918624987
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041259228
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918532744
[INFO] [stdout] [Epoch 947]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041165145
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891862489
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041259217
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891853267
[INFO] [stdout] [Epoch 948]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041165167
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918624793
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041259217
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918532594
[INFO] [stdout] [Epoch 949]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041165173
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918624704
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041259195
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891853254
[INFO] [stdout] [Epoch 950]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041165195
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891862461
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041259173
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891853247
[INFO] [stdout] [Epoch 951]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041165212
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891862451
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041259161
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891853241
[INFO] [stdout] [Epoch 952]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041165223
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891862442
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041259161
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918532345
[INFO] [stdout] [Epoch 953]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041165245
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918624327
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904125914
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891853228
[INFO] [stdout] [Epoch 954]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041165267
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918624227
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041259117
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891853222
[INFO] [stdout] [Epoch 955]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041165284
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891862414
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041259106
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891853216
[INFO] [stdout] [Epoch 956]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249897990411653
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918624043
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041259095
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918532095
[INFO] [stdout] [Epoch 957]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041165317
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918623944
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041259084
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891853203
[INFO] [stdout] [Epoch 958]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041165328
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918623855
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041259061
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891853197
[INFO] [stdout] [Epoch 959]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041165356
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891862376
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904125904
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918531906
[INFO] [stdout] [Epoch 960]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041165373
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891862366
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041259028
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918531834
[INFO] [stdout] [Epoch 961]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904116539
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918623566
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041259028
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891853178
[INFO] [stdout] [Epoch 962]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041165406
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918623466
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041258995
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918531723
[INFO] [stdout] [Epoch 963]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041165434
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918623366
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041258984
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891853167
[INFO] [stdout] [Epoch 964]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041165456
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891862327
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041258962
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918531606
[INFO] [stdout] [Epoch 965]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041165472
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891862317
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904125895
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891853154
[INFO] [stdout] [Epoch 966]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041165495
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918623083
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041258928
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918531484
[INFO] [stdout] [Epoch 967]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041165517
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918622967
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041258906
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891853142
[INFO] [stdout] [Epoch 968]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041165545
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891862288
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041258884
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918531373
[INFO] [stdout] [Epoch 969]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041165567
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918622767
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041258862
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918531323
[INFO] [stdout] [Epoch 970]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041165595
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891862267
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904125885
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918531245
[INFO] [stdout] [Epoch 971]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904116561
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918622583
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041258828
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891853119
[INFO] [stdout] [Epoch 972]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041165633
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891862247
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041258806
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918531134
[INFO] [stdout] [Epoch 973]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904116565
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891862238
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041258806
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891853108
[INFO] [stdout] [Epoch 974]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041165678
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891862229
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041258773
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891853102
[INFO] [stdout] [Epoch 975]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041165695
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891862218
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904125875
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891853096
[INFO] [stdout] [Epoch 976]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041165722
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918622084
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041258728
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918530907
[INFO] [stdout] [Epoch 977]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904116575
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918621973
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041258706
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891853086
[INFO] [stdout] [Epoch 978]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041165778
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891862187
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041258684
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918530807
[INFO] [stdout] [Epoch 979]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041165806
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891862177
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904125865
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918530746
[INFO] [stdout] [Epoch 980]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041165828
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891862167
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904125864
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891853068
[INFO] [stdout] [Epoch 981]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041165833
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918621584
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904125864
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989185306
[INFO] [stdout] [Epoch 982]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041165855
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918621495
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041258628
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918530546
[INFO] [stdout] [Epoch 983]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904116586
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918621407
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041258617
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918530474
[INFO] [stdout] [Epoch 984]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041165878
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891862131
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041258595
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891853041
[INFO] [stdout] [Epoch 985]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904116589
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918621223
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041258595
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891853035
[INFO] [stdout] [Epoch 986]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041165917
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918621124
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041258573
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918530296
[INFO] [stdout] [Epoch 987]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041165933
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891862102
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904125855
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891853023
[INFO] [stdout] [Epoch 988]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041165955
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891862093
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041258529
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891853017
[INFO] [stdout] [Epoch 989]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041165972
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891862084
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041258517
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918530113
[INFO] [stdout] [Epoch 990]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041166
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918620735
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041258506
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918530046
[INFO] [stdout] [Epoch 991]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904116601
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918620635
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041258484
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891853
[INFO] [stdout] [Epoch 992]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041166044
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918620546
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041258462
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918529935
[INFO] [stdout] [Epoch 993]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041166055
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891862045
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041258462
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918529874
[INFO] [stdout] [Epoch 994]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041166078
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891862035
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904125844
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891852982
[INFO] [stdout] [Epoch 995]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904116609
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891862025
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041258418
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891852975
[INFO] [stdout] [Epoch 996]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041166122
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891862016
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041258395
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918529697
[INFO] [stdout] [Epoch 997]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041166139
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891862007
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041258384
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918529636
[INFO] [stdout] [Epoch 998]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041166166
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891861996
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041258373
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891852957
[INFO] [stdout] [Epoch 999]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041166177
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918619863
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904125835
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918529513
[INFO] [stdout] 
[INFO] [stdout] thread 'models::sequential::test_sequential_xor1' (31) 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:     0x5824a00c4fa2 - std::backtrace_rs::backtrace::libunwind::trace::h589a96ef7638b383
[INFO] [stdout]                                at /rustc/b83b707f97d809763b7861afa7638871f3339a33/library/std/src/../../backtrace/src/backtrace/libunwind.rs:117:9
[INFO] [stdout]    1:     0x5824a00c4fa2 - std::backtrace_rs::backtrace::trace_unsynchronized::h23a30a7548c40de4
[INFO] [stdout]                                at /rustc/b83b707f97d809763b7861afa7638871f3339a33/library/std/src/../../backtrace/src/backtrace/mod.rs:66:14
[INFO] [stdout]    2:     0x5824a00c4fa2 - std::sys::backtrace::_print_fmt::h87dabd6535c8c07a
[INFO] [stdout]                                at /rustc/b83b707f97d809763b7861afa7638871f3339a33/library/std/src/sys/backtrace.rs:66:9
[INFO] [stdout]    3:     0x5824a00c4fa2 - <std::sys::backtrace::BacktraceLock::print::DisplayBacktrace as core::fmt::Display>::fmt::hffd20ad4e5eca8ab
[INFO] [stdout]                                at /rustc/b83b707f97d809763b7861afa7638871f3339a33/library/std/src/sys/backtrace.rs:39:26
[INFO] [stdout]    4:     0x5824a00d5d5f - core::fmt::rt::Argument::fmt::h75c83e3fd5ce2419
[INFO] [stdout]                                at /rustc/b83b707f97d809763b7861afa7638871f3339a33/library/core/src/fmt/rt.rs:173:76
[INFO] [stdout]    5:     0x5824a00d5d5f - core::fmt::write::h6d9d3a7cfd7b84f5
[INFO] [stdout]                                at /rustc/b83b707f97d809763b7861afa7638871f3339a33/library/core/src/fmt/mod.rs:1468:25
[INFO] [stdout]    6:     0x5824a00926c3 - std::io::default_write_fmt::he11a713685e2464d
[INFO] [stdout]                                at /rustc/b83b707f97d809763b7861afa7638871f3339a33/library/std/src/io/mod.rs:639:11
[INFO] [stdout]    7:     0x5824a00926c3 - std::io::Write::write_fmt::h9d08f7e050bd2612
[INFO] [stdout]                                at /rustc/b83b707f97d809763b7861afa7638871f3339a33/library/std/src/io/mod.rs:1954:13
[INFO] [stdout]    8:     0x5824a009e562 - std::sys::backtrace::BacktraceLock::print::hb28797143397220e
[INFO] [stdout]                                at /rustc/b83b707f97d809763b7861afa7638871f3339a33/library/std/src/sys/backtrace.rs:42:9
[INFO] [stdout]    9:     0x5824a00a2faf - std::panicking::default_hook::{{closure}}::h7555113b62983743
[INFO] [stdout]                                at /rustc/b83b707f97d809763b7861afa7638871f3339a33/library/std/src/panicking.rs:301:27
[INFO] [stdout]   10:     0x5824a00a2e41 - std::panicking::default_hook::h2714b564abe8d914
[INFO] [stdout]                                at /rustc/b83b707f97d809763b7861afa7638871f3339a33/library/std/src/panicking.rs:325:9
[INFO] [stdout]   11:     0x5824a000159e - <alloc::boxed::Box<F,A> as core::ops::function::Fn<Args>>::call::h0e9de8cceb22406e
[INFO] [stdout]                                at /rustc/b83b707f97d809763b7861afa7638871f3339a33/library/alloc/src/boxed.rs:1999:9
[INFO] [stdout]   12:     0x5824a000159e - test::test_main_with_exit_callback::{{closure}}::h8125bd66cf4739d0
[INFO] [stdout]                                at /rustc/b83b707f97d809763b7861afa7638871f3339a33/library/test/src/lib.rs:145:21
[INFO] [stdout]   13:     0x5824a00a36fe - <alloc::boxed::Box<F,A> as core::ops::function::Fn<Args>>::call::h1fd0a0802eaec16b
[INFO] [stdout]                                at /rustc/b83b707f97d809763b7861afa7638871f3339a33/library/alloc/src/boxed.rs:1999:9
[INFO] [stdout]   14:     0x5824a00a36fe - std::panicking::panic_with_hook::h190dc82263685ec5
[INFO] [stdout]                                at /rustc/b83b707f97d809763b7861afa7638871f3339a33/library/std/src/panicking.rs:842:13
[INFO] [stdout]   15:     0x5824a00a341a - std::panicking::panic_handler::{{closure}}::h8b036a1b366643ac
[INFO] [stdout]                                at /rustc/b83b707f97d809763b7861afa7638871f3339a33/library/std/src/panicking.rs:707:13
[INFO] [stdout]   16:     0x5824a009e699 - std::sys::backtrace::__rust_end_short_backtrace::h45affcfc0c830da8
[INFO] [stdout]                                at /rustc/b83b707f97d809763b7861afa7638871f3339a33/library/std/src/sys/backtrace.rs:174:18
[INFO] [stdout]   17:     0x5824a008691d - __rustc[9b67c8562bba447b]::rust_begin_unwind
[INFO] [stdout]                                at /rustc/b83b707f97d809763b7861afa7638871f3339a33/library/std/src/panicking.rs:698:5
[INFO] [stdout]   18:     0x5824a00dd8b0 - core::panicking::panic_fmt::hc084f85b1e76c16d
[INFO] [stdout]                                at /rustc/b83b707f97d809763b7861afa7638871f3339a33/library/core/src/panicking.rs:75:14
[INFO] [stdout]   19:     0x5824a00dd6b3 - core::panicking::assert_failed_inner::h1215dc8d5a90adff
[INFO] [stdout]                                at /rustc/b83b707f97d809763b7861afa7638871f3339a33/library/core/src/panicking.rs:439:17
[INFO] [stdout]   20:     0x58249ffcaebc - core::panicking::assert_failed::h4f423febbd3a0159
[INFO] [stdout]                                at /rustc/b83b707f97d809763b7861afa7638871f3339a33/library/core/src/panicking.rs:394:5
[INFO] [stdout]   21:     0x58249ffb4387 - easynn::models::sequential::test_sequential_xor1::ha4d10e4e57019563
[INFO] [stdout]                                at /opt/rustwide/workdir/src/models/sequential.rs:242:5
[INFO] [stdout]   22:     0x58249ffb4727 - easynn::models::sequential::test_sequential_xor1::{{closure}}::h7cfba9041bfa02d0
[INFO] [stdout]                                at /opt/rustwide/workdir/src/models/sequential.rs:205:26
[INFO] [stdout]   23:     0x58249ffb1386 - core::ops::function::FnOnce::call_once::h3b29cf9ed37001ff
[INFO] [stdout]                                at /rustc/b83b707f97d809763b7861afa7638871f3339a33/library/core/src/ops/function.rs:253:5
[INFO] [stdout]   24:     0x5824a000135b - core::ops::function::FnOnce::call_once::hdbf42be2a49fb464
[INFO] [stdout]                                at /rustc/b83b707f97d809763b7861afa7638871f3339a33/library/core/src/ops/function.rs:253:5
[INFO] [stdout]   25:     0x5824a000135b - test::__rust_begin_short_backtrace::h8ae08814d38cb356
[INFO] [stdout]                                at /rustc/b83b707f97d809763b7861afa7638871f3339a33/library/test/src/lib.rs:663:18
[INFO] [stdout]   26:     0x5824a0016e45 - test::run_test_in_process::{{closure}}::h54574ee4f414d690
[INFO] [stdout]                                at /rustc/b83b707f97d809763b7861afa7638871f3339a33/library/test/src/lib.rs:686:74
[INFO] [stdout]   27:     0x5824a0016e45 - <core::panic::unwind_safe::AssertUnwindSafe<F> as core::ops::function::FnOnce<()>>::call_once::h01a0b7e7aad1a501
[INFO] [stdout]                                at /rustc/b83b707f97d809763b7861afa7638871f3339a33/library/core/src/panic/unwind_safe.rs:272:9
[INFO] [stdout]   28:     0x5824a0016e45 - std::panicking::catch_unwind::do_call::h23820e817c5612cd
[INFO] [stdout]                                at /rustc/b83b707f97d809763b7861afa7638871f3339a33/library/std/src/panicking.rs:590:40
[INFO] [stdout]   29:     0x5824a0016e45 - std::panicking::catch_unwind::h89c73a1febce5587
[INFO] [stdout]                                at /rustc/b83b707f97d809763b7861afa7638871f3339a33/library/std/src/panicking.rs:553:19
[INFO] [stdout]   30:     0x5824a0016e45 - std::panic::catch_unwind::h6f55caf410861914
[INFO] [stdout]                                at /rustc/b83b707f97d809763b7861afa7638871f3339a33/library/std/src/panic.rs:359:14
[INFO] [stdout]   31:     0x5824a0016e45 - test::run_test_in_process::h4db851878e5d3983
[INFO] [stdout]                                at /rustc/b83b707f97d809763b7861afa7638871f3339a33/library/test/src/lib.rs:686:27
[INFO] [stdout]   32:     0x5824a0016e45 - test::run_test::{{closure}}::h57e8c5acaceacd24
[INFO] [stdout]                                at /rustc/b83b707f97d809763b7861afa7638871f3339a33/library/test/src/lib.rs:607:43
[INFO] [stdout]   33:     0x58249ffed834 - test::run_test::{{closure}}::he775ccc3a9d3b97e
[INFO] [stdout]                                at /rustc/b83b707f97d809763b7861afa7638871f3339a33/library/test/src/lib.rs:637:41
[INFO] [stdout]   34:     0x58249ffed834 - std::sys::backtrace::__rust_begin_short_backtrace::hcd94d85d7765225a
[INFO] [stdout]                                at /rustc/b83b707f97d809763b7861afa7638871f3339a33/library/std/src/sys/backtrace.rs:158:18
[INFO] [stdout]   35:     0x58249fff107a - std::thread::Builder::spawn_unchecked_::{{closure}}::{{closure}}::ha814de8393f07830
[INFO] [stdout]                                at /rustc/b83b707f97d809763b7861afa7638871f3339a33/library/std/src/thread/mod.rs:559:17
[INFO] [stdout]   36:     0x58249fff107a - <core::panic::unwind_safe::AssertUnwindSafe<F> as core::ops::function::FnOnce<()>>::call_once::h8deb00af0abdeaf1
[INFO] [stdout]                                at /rustc/b83b707f97d809763b7861afa7638871f3339a33/library/core/src/panic/unwind_safe.rs:272:9
[INFO] [stdout]   37:     0x58249fff107a - std::panicking::catch_unwind::do_call::h1917979b0c279ea2
[INFO] [stdout]                                at /rustc/b83b707f97d809763b7861afa7638871f3339a33/library/std/src/panicking.rs:590:40
[INFO] [stdout]   38:     0x58249fff107a - std::panicking::catch_unwind::h791ad91fee6ce34e
[INFO] [stdout]                                at /rustc/b83b707f97d809763b7861afa7638871f3339a33/library/std/src/panicking.rs:553:19
[INFO] [stdout]   39:     0x58249fff107a - std::panic::catch_unwind::h982b8f40ed9c3cce
[INFO] [stdout]                                at /rustc/b83b707f97d809763b7861afa7638871f3339a33/library/std/src/panic.rs:359:14
[INFO] [stdout]   40:     0x58249fff107a - std::thread::Builder::spawn_unchecked_::{{closure}}::h545e54700c945db4
[INFO] [stdout]                                at /rustc/b83b707f97d809763b7861afa7638871f3339a33/library/std/src/thread/mod.rs:557:30
[INFO] [stdout]   41:     0x58249fff107a - core::ops::function::FnOnce::call_once{{vtable.shim}}::hb427481a5227d78a
[INFO] [stdout]                                at /rustc/b83b707f97d809763b7861afa7638871f3339a33/library/core/src/ops/function.rs:253:5
[INFO] [stdout]   42:     0x5824a009883f - <alloc::boxed::Box<F,A> as core::ops::function::FnOnce<Args>>::call_once::h1d452951a81e9d0a
[INFO] [stdout]                                at /rustc/b83b707f97d809763b7861afa7638871f3339a33/library/alloc/src/boxed.rs:1985:9
[INFO] [stdout]   43:     0x5824a009883f - std::sys::pal::unix::thread::Thread::new::thread_start::h29c84e109630689a
[INFO] [stdout]                                at /rustc/b83b707f97d809763b7861afa7638871f3339a33/library/std/src/sys/pal/unix/thread.rs:118:17
[INFO] [stdout]   44:     0x7a4973570aa4 - <unknown>
[INFO] [stdout]   45:     0x7a49735fda34 - 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 1.16s
[INFO] [stdout] 
[INFO] running `Command { std: "docker" "inspect" "1bcdf372f0909574aa46a80887a61581c21399d691a8498f372a07dca138688d", kill_on_drop: false }`
[INFO] running `Command { std: "docker" "rm" "-f" "1bcdf372f0909574aa46a80887a61581c21399d691a8498f372a07dca138688d", kill_on_drop: false }`
[INFO] [stdout] 1bcdf372f0909574aa46a80887a61581c21399d691a8498f372a07dca138688d