[INFO] fetching crate easynn 0.1.7-beta...
[INFO] testing easynn-0.1.7-beta against 1.76.0 for beta-1.77-1
[INFO] extracting crate easynn 0.1.7-beta into /workspace/builds/worker-7-tc1/source
[INFO] validating manifest of crates.io crate easynn 0.1.7-beta on toolchain 1.76.0
[INFO] running `Command { std: CARGO_HOME="/workspace/cargo-home" RUSTUP_HOME="/workspace/rustup-home" "/workspace/cargo-home/bin/cargo" "+1.76.0" "metadata" "--manifest-path" "Cargo.toml" "--no-deps", kill_on_drop: false }`
[INFO] started tweaking crates.io crate easynn 0.1.7-beta
[INFO] finished tweaking crates.io crate easynn 0.1.7-beta
[INFO] tweaked toml for crates.io crate easynn 0.1.7-beta written to /workspace/builds/worker-7-tc1/source/Cargo.toml
[INFO] running `Command { std: CARGO_HOME="/workspace/cargo-home" RUSTUP_HOME="/workspace/rustup-home" "/workspace/cargo-home/bin/cargo" "+1.76.0" "generate-lockfile" "--manifest-path" "Cargo.toml", kill_on_drop: false }`
[INFO] [stderr]     Updating crates.io index
[INFO] running `Command { std: CARGO_HOME="/workspace/cargo-home" RUSTUP_HOME="/workspace/rustup-home" "/workspace/cargo-home/bin/cargo" "+1.76.0" "fetch" "--manifest-path" "Cargo.toml", kill_on_drop: false }`
[INFO] running `Command { std: "docker" "create" "-v" "/var/lib/crater-agent-workspace/builds/worker-7-tc1/target:/opt/rustwide/target:rw,Z" "-v" "/var/lib/crater-agent-workspace/builds/worker-7-tc1/source:/opt/rustwide/workdir:ro,Z" "-v" "/var/lib/crater-agent-workspace/cargo-home:/opt/rustwide/cargo-home:ro,Z" "-v" "/var/lib/crater-agent-workspace/rustup-home:/opt/rustwide/rustup-home:ro,Z" "-e" "SOURCE_DIR=/opt/rustwide/workdir" "-e" "CARGO_TARGET_DIR=/opt/rustwide/target" "-e" "CARGO_HOME=/opt/rustwide/cargo-home" "-e" "RUSTUP_HOME=/opt/rustwide/rustup-home" "-w" "/opt/rustwide/workdir" "-m" "1610612736" "--user" "0:0" "--network" "none" "ghcr.io/rust-lang/crates-build-env/linux@sha256:2788e3201cd34a07e3172128adcd8b3090168a8e3bcc40d7c032b9dda1df7d1c" "/opt/rustwide/cargo-home/bin/cargo" "+1.76.0" "metadata" "--no-deps" "--format-version=1", kill_on_drop: false }`
[INFO] [stdout] 46dbcec34fbb3c2eba9da2cd93252a74470f4443cb17794be7a5056020edf196
[INFO] running `Command { std: "docker" "start" "-a" "46dbcec34fbb3c2eba9da2cd93252a74470f4443cb17794be7a5056020edf196", kill_on_drop: false }`
[INFO] running `Command { std: "docker" "inspect" "46dbcec34fbb3c2eba9da2cd93252a74470f4443cb17794be7a5056020edf196", kill_on_drop: false }`
[INFO] running `Command { std: "docker" "rm" "-f" "46dbcec34fbb3c2eba9da2cd93252a74470f4443cb17794be7a5056020edf196", kill_on_drop: false }`
[INFO] [stdout] 46dbcec34fbb3c2eba9da2cd93252a74470f4443cb17794be7a5056020edf196
[INFO] running `Command { std: "docker" "create" "-v" "/var/lib/crater-agent-workspace/builds/worker-7-tc1/target:/opt/rustwide/target:rw,Z" "-v" "/var/lib/crater-agent-workspace/builds/worker-7-tc1/source:/opt/rustwide/workdir:ro,Z" "-v" "/var/lib/crater-agent-workspace/cargo-home:/opt/rustwide/cargo-home:ro,Z" "-v" "/var/lib/crater-agent-workspace/rustup-home:/opt/rustwide/rustup-home:ro,Z" "-e" "SOURCE_DIR=/opt/rustwide/workdir" "-e" "CARGO_TARGET_DIR=/opt/rustwide/target" "-e" "CARGO_INCREMENTAL=0" "-e" "RUST_BACKTRACE=full" "-e" "RUSTFLAGS=--cap-lints=warn" "-e" "RUSTDOCFLAGS=--cap-lints=warn" "-e" "CARGO_HOME=/opt/rustwide/cargo-home" "-e" "RUSTUP_HOME=/opt/rustwide/rustup-home" "-w" "/opt/rustwide/workdir" "-m" "1610612736" "--user" "0:0" "--network" "none" "ghcr.io/rust-lang/crates-build-env/linux@sha256:2788e3201cd34a07e3172128adcd8b3090168a8e3bcc40d7c032b9dda1df7d1c" "/opt/rustwide/cargo-home/bin/cargo" "+1.76.0" "build" "--frozen" "--message-format=json", kill_on_drop: false }`
[INFO] [stdout] cf28e6b4b38c4a67806ac31a83a562a3d09604337c4c74997b645128eac74e70
[INFO] running `Command { std: "docker" "start" "-a" "cf28e6b4b38c4a67806ac31a83a562a3d09604337c4c74997b645128eac74e70", kill_on_drop: false }`
[INFO] [stderr]    Compiling crossbeam-channel v0.5.11
[INFO] [stderr]    Compiling crossbeam-queue v0.3.11
[INFO] [stderr]    Compiling getrandom v0.2.12
[INFO] [stderr]    Compiling num_cpus v1.16.0
[INFO] [stderr]    Compiling rand_core v0.6.4
[INFO] [stderr]    Compiling rand_chacha v0.3.1
[INFO] [stderr]    Compiling crossbeam v0.8.4
[INFO] [stderr]    Compiling rand v0.8.5
[INFO] [stderr]    Compiling easynn v0.1.7-beta (/opt/rustwide/workdir)
[INFO] [stdout] warning: unused variable: `olen`
[INFO] [stdout]   --> src/layers/dense.rs:96:13
[INFO] [stdout]    |
[INFO] [stdout] 96 |         let olen = output.flattened.len();
[INFO] [stdout]    |             ^^^^ help: if this is intentional, prefix it with an underscore: `_olen`
[INFO] [stdout]    |
[INFO] [stdout]    = note: `#[warn(unused_variables)]` on by default
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: unused variable: `dlen`
[INFO] [stdout]    --> src/layers/dense.rs:148:13
[INFO] [stdout]     |
[INFO] [stdout] 148 |         let dlen = delta.flattened.len();
[INFO] [stdout]     |             ^^^^ help: if this is intentional, prefix it with an underscore: `_dlen`
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: unused variable: `dlen`
[INFO] [stdout]    --> src/layers/dense.rs:205:13
[INFO] [stdout]     |
[INFO] [stdout] 205 |         let dlen = delta.flattened.len();
[INFO] [stdout]     |             ^^^^ help: if this is intentional, prefix it with an underscore: `_dlen`
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: variable does not need to be mutable
[INFO] [stdout]    --> src/models/sequential.rs:137:17
[INFO] [stdout]     |
[INFO] [stdout] 137 |             let mut cum_dw = &dws.par_chunks_mut(1).reduce_with(|dw1, dw2| {
[INFO] [stdout]     |                 ----^^^^^^
[INFO] [stdout]     |                 |
[INFO] [stdout]     |                 help: remove this `mut`
[INFO] [stdout]     |
[INFO] [stdout]     = note: `#[warn(unused_mut)]` on by default
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: variable does not need to be mutable
[INFO] [stdout]    --> src/models/sequential.rs:146:17
[INFO] [stdout]     |
[INFO] [stdout] 146 |             let mut cum_db = &dbs.par_chunks_mut(1).reduce_with(|db1, db2| {
[INFO] [stdout]     |                 ----^^^^^^
[INFO] [stdout]     |                 |
[INFO] [stdout]     |                 help: remove this `mut`
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: function `determine_thread` is never used
[INFO] [stdout]   --> src/layers/dense.rs:18:4
[INFO] [stdout]    |
[INFO] [stdout] 18 | fn determine_thread(len: usize) -> usize {
[INFO] [stdout]    |    ^^^^^^^^^^^^^^^^
[INFO] [stdout]    |
[INFO] [stdout]    = note: `#[warn(dead_code)]` on by default
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: method `pos2index` is never used
[INFO] [stdout]   --> src/tensor/mod.rs:38:19
[INFO] [stdout]    |
[INFO] [stdout] 26 | impl<T: NumT> Tensor<T> {
[INFO] [stdout]    | ----------------------- method in this implementation
[INFO] [stdout] ...
[INFO] [stdout] 38 |     pub(crate) fn pos2index<const RANK: usize>(&self, mut pos: usize) -> Result<TensorIndex<RANK>> {
[INFO] [stdout]    |                   ^^^^^^^^^
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: 7 warnings emitted
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stderr]     Finished dev [unoptimized + debuginfo] target(s) in 1.13s
[INFO] running `Command { std: "docker" "inspect" "cf28e6b4b38c4a67806ac31a83a562a3d09604337c4c74997b645128eac74e70", kill_on_drop: false }`
[INFO] running `Command { std: "docker" "rm" "-f" "cf28e6b4b38c4a67806ac31a83a562a3d09604337c4c74997b645128eac74e70", kill_on_drop: false }`
[INFO] [stdout] cf28e6b4b38c4a67806ac31a83a562a3d09604337c4c74997b645128eac74e70
[INFO] running `Command { std: "docker" "create" "-v" "/var/lib/crater-agent-workspace/builds/worker-7-tc1/target:/opt/rustwide/target:rw,Z" "-v" "/var/lib/crater-agent-workspace/builds/worker-7-tc1/source:/opt/rustwide/workdir:ro,Z" "-v" "/var/lib/crater-agent-workspace/cargo-home:/opt/rustwide/cargo-home:ro,Z" "-v" "/var/lib/crater-agent-workspace/rustup-home:/opt/rustwide/rustup-home:ro,Z" "-e" "SOURCE_DIR=/opt/rustwide/workdir" "-e" "CARGO_TARGET_DIR=/opt/rustwide/target" "-e" "CARGO_INCREMENTAL=0" "-e" "RUST_BACKTRACE=full" "-e" "RUSTFLAGS=--cap-lints=warn" "-e" "RUSTDOCFLAGS=--cap-lints=warn" "-e" "CARGO_HOME=/opt/rustwide/cargo-home" "-e" "RUSTUP_HOME=/opt/rustwide/rustup-home" "-w" "/opt/rustwide/workdir" "-m" "1610612736" "--user" "0:0" "--network" "none" "ghcr.io/rust-lang/crates-build-env/linux@sha256:2788e3201cd34a07e3172128adcd8b3090168a8e3bcc40d7c032b9dda1df7d1c" "/opt/rustwide/cargo-home/bin/cargo" "+1.76.0" "test" "--frozen" "--no-run" "--message-format=json", kill_on_drop: false }`
[INFO] [stdout] a3a2a9802c08920a62791f29acb2ffca6196eb48a470ee3c2b83c4bd8ab53e7e
[INFO] running `Command { std: "docker" "start" "-a" "a3a2a9802c08920a62791f29acb2ffca6196eb48a470ee3c2b83c4bd8ab53e7e", kill_on_drop: false }`
[INFO] [stdout] warning: unused variable: `olen`
[INFO] [stdout]   --> src/layers/dense.rs:96:13
[INFO] [stdout]    |
[INFO] [stdout] 96 |         let olen = output.flattened.len();
[INFO] [stdout]    |             ^^^^ help: if this is intentional, prefix it with an underscore: `_olen`
[INFO] [stdout]    |
[INFO] [stdout]    = note: `#[warn(unused_variables)]` on by default
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: unused variable: `dlen`
[INFO] [stdout]    --> src/layers/dense.rs:148:13
[INFO] [stdout]     |
[INFO] [stdout] 148 |         let dlen = delta.flattened.len();
[INFO] [stdout]     |             ^^^^ help: if this is intentional, prefix it with an underscore: `_dlen`
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: unused variable: `dlen`
[INFO] [stdout]    --> src/layers/dense.rs:205:13
[INFO] [stdout]     |
[INFO] [stdout] 205 |         let dlen = delta.flattened.len();
[INFO] [stdout]     |             ^^^^ help: if this is intentional, prefix it with an underscore: `_dlen`
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: variable does not need to be mutable
[INFO] [stdout]    --> src/models/sequential.rs:137:17
[INFO] [stdout]     |
[INFO] [stdout] 137 |             let mut cum_dw = &dws.par_chunks_mut(1).reduce_with(|dw1, dw2| {
[INFO] [stdout]     |                 ----^^^^^^
[INFO] [stdout]     |                 |
[INFO] [stdout]     |                 help: remove this `mut`
[INFO] [stdout]     |
[INFO] [stdout]     = note: `#[warn(unused_mut)]` on by default
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: variable does not need to be mutable
[INFO] [stdout]    --> src/models/sequential.rs:146:17
[INFO] [stdout]     |
[INFO] [stdout] 146 |             let mut cum_db = &dbs.par_chunks_mut(1).reduce_with(|db1, db2| {
[INFO] [stdout]     |                 ----^^^^^^
[INFO] [stdout]     |                 |
[INFO] [stdout]     |                 help: remove this `mut`
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: function `determine_thread` is never used
[INFO] [stdout]   --> src/layers/dense.rs:18:4
[INFO] [stdout]    |
[INFO] [stdout] 18 | fn determine_thread(len: usize) -> usize {
[INFO] [stdout]    |    ^^^^^^^^^^^^^^^^
[INFO] [stdout]    |
[INFO] [stdout]    = note: `#[warn(dead_code)]` on by default
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: method `pos2index` is never used
[INFO] [stdout]   --> src/tensor/mod.rs:38:19
[INFO] [stdout]    |
[INFO] [stdout] 26 | impl<T: NumT> Tensor<T> {
[INFO] [stdout]    | ----------------------- method in this implementation
[INFO] [stdout] ...
[INFO] [stdout] 38 |     pub(crate) fn pos2index<const RANK: usize>(&self, mut pos: usize) -> Result<TensorIndex<RANK>> {
[INFO] [stdout]    |                   ^^^^^^^^^
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: 7 warnings emitted
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stderr]    Compiling easynn v0.1.7-beta (/opt/rustwide/workdir)
[INFO] [stdout] warning: unused import: `crate::layers::activation::Activation::*`
[INFO] [stdout]    --> src/models/sequential.rs:180:9
[INFO] [stdout]     |
[INFO] [stdout] 180 |     use crate::layers::activation::Activation::*;
[INFO] [stdout]     |         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[INFO] [stdout]     |
[INFO] [stdout]     = note: `#[warn(unused_imports)]` on by default
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: unused import: `rand::Rng`
[INFO] [stdout]    --> src/models/sequential.rs:207:9
[INFO] [stdout]     |
[INFO] [stdout] 207 |     use rand::Rng;
[INFO] [stdout]     |         ^^^^^^^^^
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: unused variable: `olen`
[INFO] [stdout]   --> src/layers/dense.rs:96:13
[INFO] [stdout]    |
[INFO] [stdout] 96 |         let olen = output.flattened.len();
[INFO] [stdout]    |             ^^^^ help: if this is intentional, prefix it with an underscore: `_olen`
[INFO] [stdout]    |
[INFO] [stdout]    = note: `#[warn(unused_variables)]` on by default
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: unused variable: `dlen`
[INFO] [stdout]    --> src/layers/dense.rs:148:13
[INFO] [stdout]     |
[INFO] [stdout] 148 |         let dlen = delta.flattened.len();
[INFO] [stdout]     |             ^^^^ help: if this is intentional, prefix it with an underscore: `_dlen`
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: unused variable: `dlen`
[INFO] [stdout]    --> src/layers/dense.rs:205:13
[INFO] [stdout]     |
[INFO] [stdout] 205 |         let dlen = delta.flattened.len();
[INFO] [stdout]     |             ^^^^ help: if this is intentional, prefix it with an underscore: `_dlen`
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: variable does not need to be mutable
[INFO] [stdout]    --> src/models/sequential.rs:137:17
[INFO] [stdout]     |
[INFO] [stdout] 137 |             let mut cum_dw = &dws.par_chunks_mut(1).reduce_with(|dw1, dw2| {
[INFO] [stdout]     |                 ----^^^^^^
[INFO] [stdout]     |                 |
[INFO] [stdout]     |                 help: remove this `mut`
[INFO] [stdout]     |
[INFO] [stdout]     = note: `#[warn(unused_mut)]` on by default
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: variable does not need to be mutable
[INFO] [stdout]    --> src/models/sequential.rs:146:17
[INFO] [stdout]     |
[INFO] [stdout] 146 |             let mut cum_db = &dbs.par_chunks_mut(1).reduce_with(|db1, db2| {
[INFO] [stdout]     |                 ----^^^^^^
[INFO] [stdout]     |                 |
[INFO] [stdout]     |                 help: remove this `mut`
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: function `determine_thread` is never used
[INFO] [stdout]   --> src/layers/dense.rs:18:4
[INFO] [stdout]    |
[INFO] [stdout] 18 | fn determine_thread(len: usize) -> usize {
[INFO] [stdout]    |    ^^^^^^^^^^^^^^^^
[INFO] [stdout]    |
[INFO] [stdout]    = note: `#[warn(dead_code)]` on by default
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: method `pos2index` is never used
[INFO] [stdout]   --> src/tensor/mod.rs:38:19
[INFO] [stdout]    |
[INFO] [stdout] 26 | impl<T: NumT> Tensor<T> {
[INFO] [stdout]    | ----------------------- method in this implementation
[INFO] [stdout] ...
[INFO] [stdout] 38 |     pub(crate) fn pos2index<const RANK: usize>(&self, mut pos: usize) -> Result<TensorIndex<RANK>> {
[INFO] [stdout]    |                   ^^^^^^^^^
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] warning: 9 warnings emitted
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stderr]     Finished test [unoptimized + debuginfo] target(s) in 1.31s
[INFO] running `Command { std: "docker" "inspect" "a3a2a9802c08920a62791f29acb2ffca6196eb48a470ee3c2b83c4bd8ab53e7e", kill_on_drop: false }`
[INFO] running `Command { std: "docker" "rm" "-f" "a3a2a9802c08920a62791f29acb2ffca6196eb48a470ee3c2b83c4bd8ab53e7e", kill_on_drop: false }`
[INFO] [stdout] a3a2a9802c08920a62791f29acb2ffca6196eb48a470ee3c2b83c4bd8ab53e7e
[INFO] running `Command { std: "docker" "create" "-v" "/var/lib/crater-agent-workspace/builds/worker-7-tc1/target:/opt/rustwide/target:rw,Z" "-v" "/var/lib/crater-agent-workspace/builds/worker-7-tc1/source:/opt/rustwide/workdir:ro,Z" "-v" "/var/lib/crater-agent-workspace/cargo-home:/opt/rustwide/cargo-home:ro,Z" "-v" "/var/lib/crater-agent-workspace/rustup-home:/opt/rustwide/rustup-home:ro,Z" "-e" "SOURCE_DIR=/opt/rustwide/workdir" "-e" "CARGO_TARGET_DIR=/opt/rustwide/target" "-e" "CARGO_INCREMENTAL=0" "-e" "RUST_BACKTRACE=full" "-e" "RUSTFLAGS=--cap-lints=warn" "-e" "RUSTDOCFLAGS=--cap-lints=warn" "-e" "CARGO_HOME=/opt/rustwide/cargo-home" "-e" "RUSTUP_HOME=/opt/rustwide/rustup-home" "-w" "/opt/rustwide/workdir" "-m" "1610612736" "--user" "0:0" "--network" "none" "ghcr.io/rust-lang/crates-build-env/linux@sha256:2788e3201cd34a07e3172128adcd8b3090168a8e3bcc40d7c032b9dda1df7d1c" "/opt/rustwide/cargo-home/bin/cargo" "+1.76.0" "test" "--frozen", kill_on_drop: false }`
[INFO] [stdout] 0dda0c006e42beb458f5ea6aff011718cf6f7bc0ef6995759245fb17b7568376
[INFO] running `Command { std: "docker" "start" "-a" "0dda0c006e42beb458f5ea6aff011718cf6f7bc0ef6995759245fb17b7568376", kill_on_drop: false }`
[INFO] [stderr] warning: unused variable: `olen`
[INFO] [stderr]   --> src/layers/dense.rs:96:13
[INFO] [stderr]    |
[INFO] [stderr] 96 |         let olen = output.flattened.len();
[INFO] [stderr]    |             ^^^^ help: if this is intentional, prefix it with an underscore: `_olen`
[INFO] [stderr]    |
[INFO] [stderr]    = note: `#[warn(unused_variables)]` on by default
[INFO] [stderr] 
[INFO] [stderr] warning: unused variable: `dlen`
[INFO] [stderr]    --> src/layers/dense.rs:148:13
[INFO] [stderr]     |
[INFO] [stderr] 148 |         let dlen = delta.flattened.len();
[INFO] [stderr]     |             ^^^^ help: if this is intentional, prefix it with an underscore: `_dlen`
[INFO] [stderr] 
[INFO] [stderr] warning: unused variable: `dlen`
[INFO] [stderr]    --> src/layers/dense.rs:205:13
[INFO] [stderr]     |
[INFO] [stderr] 205 |         let dlen = delta.flattened.len();
[INFO] [stderr]     |             ^^^^ help: if this is intentional, prefix it with an underscore: `_dlen`
[INFO] [stderr] 
[INFO] [stderr] warning: variable does not need to be mutable
[INFO] [stderr]    --> src/models/sequential.rs:137:17
[INFO] [stderr]     |
[INFO] [stderr] 137 |             let mut cum_dw = &dws.par_chunks_mut(1).reduce_with(|dw1, dw2| {
[INFO] [stderr]     |                 ----^^^^^^
[INFO] [stderr]     |                 |
[INFO] [stderr]     |                 help: remove this `mut`
[INFO] [stderr]     |
[INFO] [stderr]     = note: `#[warn(unused_mut)]` on by default
[INFO] [stderr] 
[INFO] [stderr] warning: variable does not need to be mutable
[INFO] [stderr]    --> src/models/sequential.rs:146:17
[INFO] [stderr]     |
[INFO] [stderr] 146 |             let mut cum_db = &dbs.par_chunks_mut(1).reduce_with(|db1, db2| {
[INFO] [stderr]     |                 ----^^^^^^
[INFO] [stderr]     |                 |
[INFO] [stderr]     |                 help: remove this `mut`
[INFO] [stderr] 
[INFO] [stderr] warning: function `determine_thread` is never used
[INFO] [stderr]   --> src/layers/dense.rs:18:4
[INFO] [stderr]    |
[INFO] [stderr] 18 | fn determine_thread(len: usize) -> usize {
[INFO] [stderr]    |    ^^^^^^^^^^^^^^^^
[INFO] [stderr]    |
[INFO] [stderr]    = note: `#[warn(dead_code)]` on by default
[INFO] [stderr] 
[INFO] [stderr] warning: method `pos2index` is never used
[INFO] [stderr]   --> src/tensor/mod.rs:38:19
[INFO] [stderr]    |
[INFO] [stderr] 26 | impl<T: NumT> Tensor<T> {
[INFO] [stderr]    | ----------------------- method in this implementation
[INFO] [stderr] ...
[INFO] [stderr] 38 |     pub(crate) fn pos2index<const RANK: usize>(&self, mut pos: usize) -> Result<TensorIndex<RANK>> {
[INFO] [stderr]    |                   ^^^^^^^^^
[INFO] [stderr] 
[INFO] [stderr] warning: unused import: `crate::layers::activation::Activation::*`
[INFO] [stderr]    --> src/models/sequential.rs:180:9
[INFO] [stderr]     |
[INFO] [stderr] 180 |     use crate::layers::activation::Activation::*;
[INFO] [stderr]     |         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[INFO] [stderr]     |
[INFO] [stderr]     = note: `#[warn(unused_imports)]` on by default
[INFO] [stderr] 
[INFO] [stderr] warning: unused import: `rand::Rng`
[INFO] [stderr]    --> src/models/sequential.rs:207:9
[INFO] [stderr]     |
[INFO] [stderr] 207 |     use rand::Rng;
[INFO] [stderr]     |         ^^^^^^^^^
[INFO] [stderr] 
[INFO] [stderr] warning: `easynn` (lib) generated 7 warnings (run `cargo fix --lib -p easynn` to apply 5 suggestions)
[INFO] [stderr] warning: `easynn` (lib test) generated 9 warnings (7 duplicates) (run `cargo fix --lib -p easynn --tests` to apply 2 suggestions)
[INFO] [stderr]     Finished test [unoptimized + debuginfo] target(s) in 0.03s
[INFO] [stderr]      Running unittests src/lib.rs (/opt/rustwide/target/debug/deps/easynn-ba98de354681552f)
[INFO] [stdout] 
[INFO] [stdout] running 7 tests
[INFO] [stdout] test layers::dense::test_add_weight_delta_to ... ok
[INFO] [stdout] test layers::dense::test_dense_activate ... ok
[INFO] [stdout] test layers::dense::test_dense_descend ... ok
[INFO] [stdout] test models::sequential::test_sequential_predict ... ok
[INFO] [stdout] test layers::dense::test_dense_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): 0.9604131763297838
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.0015676382626339956
[INFO] [stdout] [Epoch 1]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.001505559787429748
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.9253949141213343
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.8887492755198034
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.005794209100588719
[INFO] [stdout] [Epoch 2]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.005564758420190971
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.8591336695273118
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.8251119762118898
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.01205832172729361
[INFO] [stdout] [Epoch 3]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.011580812186862863
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.8001984151284424
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.7685105578878793
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.01984875433588873
[INFO] [stdout] [Epoch 4]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.01906274366418754
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.7476947845705147
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.7180860711015226
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.028746659382468027
[INFO] [stdout] [Epoch 5]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.02760829167092229
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.7008464397733718
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.6730929207583464
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.038410529527221404
[INFO] [stdout] [Epoch 6]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.036889472557943434
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.6589786557156071
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.6328831009492693
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.048563504129049716
[INFO] [stdout] [Epoch 7]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.04664038936553935
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.6215042618987964
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.596892693127604
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.05898265947292299
[INFO] [stdout] [Epoch 8]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.05664694615779523
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.587911596009704
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.5646302968077197
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.06948997974359894
[INFO] [stdout] [Epoch 9]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.06673817654575243
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.5577541761555785
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.5356671107798177
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.07994475151538007
[INFO] [stdout] [Epoch 10]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.07677893935537101
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.5306418413440237
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.5096284244268006
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.09023716341266386
[INFO] [stdout] [Epoch 11]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.08666377174152237
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.5062331467612827
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.4861863141495359
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.10028292563956635
[INFO] [stdout] [Epoch 12]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.09631172178423952
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.4842288318166335
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.46505337007669495
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.11001875215352382
[INFO] [stdout] [Epoch 13]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.10566200956824427
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.46436620568053755
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.4459773039355883
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.11939857211151454
[INFO] [stdout] [Epoch 14]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.11467038865589856
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.4464143178420558
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.4287363108555104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.12839035748114783
[INFO] [stdout] [Epoch 15]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.12330609932489438
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.43016980063571936
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.41313507653054476
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.13697347092047052
[INFO] [stdout] [Epoch 16]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.1315493214720199
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.4154532872406458
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.39900133706591623
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.14513645264758518
[INFO] [stdout] [Epoch 17]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.13938904912274078
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.40210632276176844
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.3861829123804025
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.15287517743322807
[INFO] [stdout] [Epoch 18]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.14682132040687226
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.3899886980276409
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.37454514558574625
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.16019132338726166
[INFO] [stdout] [Epoch 19]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.1538477469811261
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.37897614599040436
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.3639686906091843
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.16709110315500314
[INFO] [stdout] [Epoch 20]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.160474295470065
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.36895834935418814
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.3543475987197622
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.1735842157307176
[INFO] [stdout] [Epoch 21]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.1667102807877812
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.35983721551236925
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.3455876617780795
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.17968298353689718
[INFO] [stdout] [Epoch 22]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.17256753738883604
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.35152538123235155
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.33760497613555035
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.1854016448821041
[INFO] [stdout] [Epoch 23]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.1780597397447728
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.3439449149510985
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.330324696319035
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.19075577654423118
[INFO] [stdout] [Epoch 24]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.18320184779307966
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.3370261891737411
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.323679952082461
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.1957618251550375
[INFO] [stdout] [Epoch 25]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.188009656878898
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.3307068994187833
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.31761090620179955
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.20043672939202922
[INFO] [stdout] [Epoch 26]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.1924994349081049
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.3249312095269254
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.3120639336296592
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.204797617805494
[INFO] [stdout] [Epoch 27]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.19668763214039645
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.3196490060315911
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.3069909053927401
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.20886156949854184
[INFO] [stdout] [Epoch 28]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2005906513463996
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.31481524675051836
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.30234856297919777
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2126454269016039
[INFO] [stdout] [Epoch 29]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.20422466799630037
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.3103893908611133
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.29809797098301316
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2161656515954311
[INFO] [stdout] [Epoch 30]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.20760549179225204
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.3063348995203621
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2942040374993559
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2194382155853401
[INFO] [stdout] [Epoch 31]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.21074846224816063
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.3026187976278246
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.29063509324176284
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.22247852165429158
[INFO] [stdout] [Epoch 32]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.21366837219678167
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.29921128864584057
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.28736252161546544
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2253013474573682
[INFO] [stdout] [Epoch 33]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.21637941409805642
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.29608541551713397
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2843604330626555
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2279208088942212
[INFO] [stdout] [Epoch 34]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.21889514486201003
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.29321676168426913
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.28160537792157214
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.23035033903361138
[INFO] [stdout] [Epoch 35]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2212284656078804
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.29058318704155295
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2790760928347074
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.23260267948610952
[INFO] [stdout] [Epoch 36]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.22339161337845956
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2881645943581514
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2767532764215685
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.23468988164502697
[INFO] [stdout] [Epoch 37]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.22539616233188392
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2859427223185909
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.27461939051477474
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.23662331565673603
[INFO] [stdout] [Epoch 38]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.22725303235672925
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2839009618481078
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2726584837589227
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.23841368535244348
[INFO] [stdout] [Epoch 39]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.22897250341248673
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2820241928379729
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2708560348015892
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.24007104768502616
[INFO] [stdout] [Epoch 40]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.23056423419669908
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2802986387706486
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.26919881267533086
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2416048354758753
[INFO] [stdout] [Epoch 41]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.23203728399103066
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2787117370754848
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.26767475228729565
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2430238824956275
[INFO] [stdout] [Epoch 42]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.23340013674880064
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2772520233304362
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.26627284320655087
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.24433645008576022
[INFO] [stdout] [Epoch 43]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2346607266623641
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2759090276705652
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2649830301748108
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.24555025468090527
[INFO] [stdout] [Epoch 44]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2358264645955414
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.27467318197558127
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2637961239693482
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.24667249571910851
[INFO] [stdout] [Epoch 45]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2369042648886318
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2735357365911498
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.26270372142214027
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.24770988353316314
[INFO] [stdout] [Epoch 46]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2379005721452499
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2724886854963402
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.26169813355068505
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.24866866690395428
[INFO] [stdout] [Epoch 47]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.23882138769455769
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2715246989658752
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2607723208868266
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.24955466002935744
[INFO] [stdout] [Epoch 48]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2396722954921949
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2706370628938209
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25991983520322554
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25037326872206445
[INFO] [stdout] [Epoch 49]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24045848728067068
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2698196240475878
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2591347669353033
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2511295156988176
[INFO] [stdout] [Epoch 50]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2411847868771444
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26906674060980973
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25841169768166117
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25182806486367004
[INFO] [stdout] [Epoch 51]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2418556734950687
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26837323744270536
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25774565723997417
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2524732445205043
[INFO] [stdout] [Epoch 52]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24247530403749232
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.267734365576541
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25713208469971005
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2530690694763938
[INFO] [stdout] [Epoch 53]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2430475343251286
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2671457654821606
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2565667931690671
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2536192620185106
[INFO] [stdout] [Epoch 54]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24357593924257756
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.266603433738447
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2560459377624044
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2541272717640568
[INFO] [stdout] [Epoch 55]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24406383180220015
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2661036927500165
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25556598651711576
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.254596294395867
[INFO] [stdout] [Epoch 56]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24451428113779067
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26564316320932047
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25512369394623136
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2550292893065159
[INFO] [stdout] [Epoch 57]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24493012944997788
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2652187390313694
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25471607696572723
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25542899618149506
[INFO] [stdout] [Epoch 58]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24531400793270788
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.264827564519161
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25434039296420224
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2557979505577348
[INFO] [stdout] [Epoch 59]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2456683517156485
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2644670135441285
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25399411980778086
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2561384983978074
[INFO] [stdout] [Epoch 60]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24599541386125426
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2641346705490168
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25367493759527565
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25645280972287066
[INFO] [stdout] [Epoch 61]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.246297278457845
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2638283132009386
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25338071199818146
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25674289134904216
[INFO] [stdout] [Epoch 62]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24657587285162005
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26354589654032834
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2531094790373314
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25701059877266447
[INFO] [stdout] [Epoch 63]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24683297906126694
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2632855384873957
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2528594311632949
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2572576472499928
[INFO] [stdout] [Epoch 64]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24707024441889305
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2630455065817468
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2526289045211097
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2574856221163687
[INFO] [stdout] [Epoch 65]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24728919148056047
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26282420584332145
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25241636729192585
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25769598838905194
[INFO] [stdout] [Epoch 66]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24749122724884548
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2626201676538837
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2522204090147898
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25789009969667376
[INFO] [stdout] [Epoch 67]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24767765174868547
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26243203956817124
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2520397308012717
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25806920657682547
[INFO] [stdout] [Epoch 68]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2478496659963832
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26225857597260743
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25187313636409225
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25823446418168045
[INFO] [stdout] [Epoch 69]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2480083794000859
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2620986295173334
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25171952378844703
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2583869394298166
[INFO] [stdout] [Epoch 70]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24815481662839586
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2619511432543417
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25157787798146974
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2585276176406047
[INFO] [stdout] [Epoch 71]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24828992398203678
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26181514342078405
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.251447263741321
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25865740868569476
[INFO] [stdout] [Epoch 72]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24841457530174127
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2616897328121681
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2513268193928062
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2587771526902934
[INFO] [stdout] [Epoch 73]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24852957744375778
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26157408469522214
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2512157509412914
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2588876253151013
[INFO] [stdout] [Epoch 74]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24863567535262324
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26146743721477333
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2511133267010683
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2589895426479941
[INFO] [stdout] [Epoch 75]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24873355675913358
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2613690882530815
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2510188723582595
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25908356573279057
[INFO] [stdout] [Epoch 76]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24882385652977207
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2612783907037836
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25093176643191384
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2591703047607661
[INFO] [stdout] [Epoch 77]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24890716069223975
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26119474812594135
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25085143610015415
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2592503229489594
[INFO] [stdout] [Epoch 78]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2489840101601806
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2611176107467136
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25077735336114376
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2593241401277658
[INFO] [stdout] [Epoch 79]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2490549041787062
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26104647178391116
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2507090315012684
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25939223605883927
[INFO] [stdout] [Epoch 80]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24912030351090922
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26098086406217497
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2506460218453128
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2594550535029245
[INFO] [stdout] [Epoch 81]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24918063338420873
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2609203568987663
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2505879107655752
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25951300105591035
[INFO] [stdout] [Epoch 82]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24923628621409627
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2608645532370096
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25053431692882405
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2595664557701479
[INFO] [stdout] [Epoch 83]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2492876241216501
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2608130870072789
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25048488876179065
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2596157655768911
[INFO] [stdout] [Epoch 84]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24933498126004622
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26076562069711967
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25043930211751386
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2596612515246097
[INFO] [stdout] [Epoch 85]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2493786659642352
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26072184311363156
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25039725812633173
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25970320984688344
[INFO] [stdout] [Epoch 86]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24941896273694691
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2606814673226466
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2503584812166698
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25974191387260614
[INFO] [stdout] [Epoch 87]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24945613408325093
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2606442287505175
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2503227172919971
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25977761579031844
[INFO] [stdout] [Epoch 88]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24949042220502182
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2606098834355005
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2502897320514548
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25981054827763156
[INFO] [stdout] [Epoch 89]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24952205056583737
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2605782064167861
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2502593094426815
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25984092600590813
[INFO] [stdout] [Epoch 90]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24955122533607418
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2605489902502081
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2502312502363
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25986894702962265
[INFO] [stdout] [Epoch 91]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24957813672724963
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26052204364055637
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25020537071239035
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25989479406913096
[INFO] [stdout] [Epoch 92]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24960296022399342
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2604971901812353
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2501815014500584
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25991863569493573
[INFO] [stdout] [Epoch 93]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24962585772141624
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2604742671927595
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2501594862119263
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25994062742093216
[INFO] [stdout] [Epoch 94]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24964697857506324
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2604531246522674
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25013918091603754
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2599609127135653
[INFO] [stdout] [Epoch 95]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24966646057010816
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2604336242068564
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2501204526882649
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2599796239233079
[INFO] [stdout] [Epoch 96]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2496844308159449
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26041563826413067
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.250103178988871
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2599968831443911
[INFO] [stdout] [Epoch 97]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24970100657187322
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2603990491538694
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25008724680737626
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2600128030082745
[INFO] [stdout] [Epoch 98]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2497162960091468
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2603837483552233
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2500725519203565
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260027487415926
[INFO] [stdout] [Epoch 99]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2497303989142553
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2603696357842792
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2500589982072217
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26004103221360164
[INFO] [stdout] [Epoch 100]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24974340733794306
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26035661913725583
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2500464970194205
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2600535258164599
[INFO] [stdout] [Epoch 101]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24975540619412814
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260344613284961
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25003496659887653
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260065049784015
[INFO] [stdout] [Epoch 102]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249766473812568
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2603335397144896
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2500243315417958
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2600756793511314
[INFO] [stdout] [Epoch 103]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24977668244882664
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2603233260144626
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2500145223042899
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2600854839179786
[INFO] [stdout] [Epoch 104]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24978609875482663
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2603139054003957
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25000547474653995
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26009452750210477
[INFO] [stdout] [Epoch 105]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2497947842130214
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2603052162770569
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24999712971248542
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601028691555473
[INFO] [stdout] [Epoch 106]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24980279553698764
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602972018349218
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499894326422589
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26011056334967403
[INFO] [stdout] [Epoch 107]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24981018504102692
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26028980967805954
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24998233321480834
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601176603302452
[INFO] [stdout] [Epoch 108]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498170009811675
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602829914809954
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24997578501834797
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601242064449911
[INFO] [stdout] [Epoch 109]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24982328786976948
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26027670267228625
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24996974524646373
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26013024444582966
[INFO] [stdout] [Epoch 110]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24982908676577478
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602709021427248
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24996417441787294
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26013581376768025
[INFO] [stdout] [Epoch 111]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24983443554248014
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26026555197625034
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24995903611799083
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601409507856835
[INFO] [stdout] [Epoch 112]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24983936913457042
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602606172017958
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499542967606047
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601456890524946
[INFO] [stdout] [Epoch 113]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24984391976601575
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26025606556443875
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499499253680869
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26015005951719133
[INFO] [stdout] [Epoch 114]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24984811716031052
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602518673143514
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24994589336870307
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26015409072721934
[INFO] [stdout] [Epoch 115]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498519887344215
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26024799501216384
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499421744096821
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601578090146857
[INFO] [stdout] [Epoch 116]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24985555977770418
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26024442334946035
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24993874418482184
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260161238668212
[INFO] [stdout] [Epoch 117]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24985885361695082
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602411289832336
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24993558027549756
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601644020914665
[INFO] [stdout] [Epoch 118]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498618917686444
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602380903832047
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24993266200402986
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26016731994940406
[INFO] [stdout] [Epoch 119]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24986469407940767
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602352876910144
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499299702984502
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26017001130316747
[INFO] [stdout] [Epoch 120]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24986727885556204
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602327025903559
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24992748756777786
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601724937345277
[INFO] [stdout] [Epoch 121]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24986966298264043
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602303181872015
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499251975869884
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601747834606734
[INFO] [stdout] [Epoch 122]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24987186203563072
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26022811889933595
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24992308539092223
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601768954400981
[INFO] [stdout] [Epoch 123]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24987389038067023
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26022609035447325
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499211371764361
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601788434702736
[INFO] [stdout] [Epoch 124]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498757612688508
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602242192962905
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24991934021215734
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26018064027774773
[INFO] [stdout] [Epoch 125]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24987748692274894
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602224934977608
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24991768275524934
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601822976012522
[INFO] [stdout] [Epoch 126]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24987907861624264
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602209016812198
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24991615397464356
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601838262683631
[INFO] [stdout] [Epoch 127]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24988054674813595
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26021943344464254
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499147438802346
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601852362662149
[INFO] [stdout] [Epoch 128]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498819009100728
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602180791936443
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24991344325757603
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601865368067262
[INFO] [stdout] [Epoch 129]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24988314994917984
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26021683007876717
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24991224360764805
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26018773638676634
[INFO] [stdout] [Epoch 130]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24988430202585038
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26021567793763445
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24991113709130403
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26018884284365223
[INFO] [stdout] [Epoch 131]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24988536466704359
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260214615241599
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24991011647803166
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26018986340634
[INFO] [stdout] [Epoch 132]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24988634481544889
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260213635046536
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990917509869315
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601908047426429
[INFO] [stdout] [Epoch 133]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24988724887483424
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602127309474559
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990830680193662
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26019167300278634
[INFO] [stdout] [Epoch 134]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249888082751876
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602118970366435
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499075059139924
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601924738595815
[INFO] [stdout] [Epoch 135]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24988885189474205
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602111278650465
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990676720159058
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601932125454828
[INFO] [stdout] [Epoch 136]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24988956132868162
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26021041840666376
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499060858377599
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601938938867679
[INFO] [stdout] [Epoch 137]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498902156888519
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020976402569806
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499054573702805
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26019452233506635
[INFO] [stdout] [Epoch 138]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498908192505977
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602091604462604
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990487769258846
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601951019964399
[INFO] [stdout] [Epoch 139]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989137595738092
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602086037244255
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990434301693823
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260195636658207
[INFO] [stdout] [Epoch 140]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249891889446542
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602080902224591
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499038498496497
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601961298136843
[INFO] [stdout] [Epoch 141]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989236307306237
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602076165850444
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499033949682766
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601965846850088
[INFO] [stdout] [Epoch 142]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989279993148245
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602071797173559
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499029754005485
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601970042441879
[INFO] [stdout] [Epoch 143]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989320287611805
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020677676483495
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499025884049474
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26019739123251584
[INFO] [stdout] [Epoch 144]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989357453970823
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602064050945362
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990223145279256
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26019774817848296
[INFO] [stdout] [Epoch 145]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249893917350615
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020606227792203
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499019022117163
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601980774142949
[INFO] [stdout] [Epoch 146]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498942335486888
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020574607499264
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499015985304229
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601983810911097
[INFO] [stdout] [Epoch 147]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989452519990174
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602054544196488
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499013184246306
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26019866119309165
[INFO] [stdout] [Epoch 148]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989479420984517
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020518540619086
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990106006410567
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26019891955037494
[INFO] [stdout] [Epoch 149]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989504233618007
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260204937276866
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990082176070202
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601991578510207
[INFO] [stdout] [Epoch 150]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498952712001203
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020470841038196
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990060195733085
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26019937765204565
[INFO] [stdout] [Epoch 151]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989548229702466
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020449731131345
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990039921778542
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601995803895949
[INFO] [stdout] [Epoch 152]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989567700616694
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020430260033
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499002122173569
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26019976738832523
[INFO] [stdout] [Epoch 153]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989585659974753
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020412300518303
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499000397341778
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26019993987005946
[INFO] [stdout] [Epoch 154]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498960222512051
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020395735239277
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989988064123803
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020009896177004
[INFO] [stdout] [Epoch 155]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989617504288397
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020380455958014
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989973389902082
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602002457029415
[INFO] [stdout] [Epoch 156]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989631597310502
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602036636283945
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989959854871016
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020038105236254
[INFO] [stdout] [Epoch 157]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989644596268895
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602035336379899
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989947370592563
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020050589439014
[INFO] [stdout] [Epoch 158]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989656586097225
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602034137390085
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989935855494375
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602006210447281
[INFO] [stdout] [Epoch 159]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989667645135685
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020330314803
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989925234336796
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260200727255756
[INFO] [stdout] [Epoch 160]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989677845642805
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020320114245343
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989915437721233
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602008252214456
[INFO] [stdout] [Epoch 161]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989687254267637
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602031070557753
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989906401636655
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602009155818948
[INFO] [stdout] [Epoch 162]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989695932485173
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602030202732341
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989898067041402
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020099892751
[INFO] [stdout] [Epoch 163]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989703936998056
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020294022779406
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989890379477342
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020107580286356
[INFO] [stdout] [Epoch 164]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498971132010702
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602028663964398
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498988328871407
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602011467102521
[INFO] [stdout] [Epoch 165]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989718130052613
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020279829675863
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.249898767484207
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020121211297803
[INFO] [stdout] [Epoch 166]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989724411330408
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602027354837891
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.249898707158631
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602012724383773
[INFO] [stdout] [Epoch 167]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989730204981758
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020267754711257
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989865151624685
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602013280806111
[INFO] [stdout] [Epoch 168]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989735548861888
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602026241081726
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498986001934889
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602013794032411
[INFO] [stdout] [Epoch 169]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498974047788728
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020257481780074
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989855285501572
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020142674160557
[INFO] [stdout] [Epoch 170]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249897450242638
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020252935393506
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498985091915192
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020147040500946
[INFO] [stdout] [Epoch 171]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989749217697108
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020248741951657
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989846891770376
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020151067874614
[INFO] [stdout] [Epoch 172]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989753085586774
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602024487405472
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989843177042156
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020154782596133
[INFO] [stdout] [Epoch 173]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989756653205325
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020241306429986
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989839750695364
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602015820893723
[INFO] [stdout] [Epoch 174]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989759943863313
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020238015766745
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989836590342382
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020161369285344
[INFO] [stdout] [Epoch 175]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989762979061647
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602023498056394
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989833675333606
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602016428429001
[INFO] [stdout] [Epoch 176]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498976577863212
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602023218098966
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498983098662247
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020166972997627
[INFO] [stdout] [Epoch 177]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989768360866924
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020229598751615
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498982850664106
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020169452976055
[INFO] [stdout] [Epoch 178]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989770742638204
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020227216977576
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498982621918527
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201717404293
[INFO] [stdout] [Epoch 179]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249897729395083
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602022502010514
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498982410930898
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020173850303424
[INFO] [stdout] [Epoch 180]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989774965831407
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020222993780046
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989822163226358
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020175796384204
[INFO] [stdout] [Epoch 181]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498977683484739
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020221124762366
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989820368221777
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602017759138722
[INFO] [stdout] [Epoch 182]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989778558768286
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602021940084003
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989818712566755
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602017924704092
[INFO] [stdout] [Epoch 183]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989780148858096
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602021781074898
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989817185443322
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602018077416321
[INFO] [stdout] [Epoch 184]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989781615506348
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602021634409969
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989815776873342
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602018218273223
[INFO] [stdout] [Epoch 185]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989782968296032
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020214991309115
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989814477653283
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020183481951475
[INFO] [stdout] [Epoch 186]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989784216066196
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020213743538195
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989813279294085
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020184680309977
[INFO] [stdout] [Epoch 187]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989785366969697
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020212592634046
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989812173965745
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020185785637717
[INFO] [stdout] [Epoch 188]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498978642852646
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020211531076737
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989811154446107
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602018680515686
[INFO] [stdout] [Epoch 189]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989787407672645
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020210551930095
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498981021407366
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020187745528867
[INFO] [stdout] [Epoch 190]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989788310805922
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020964879642
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989809346704075
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602018861289808
[INFO] [stdout] [Epoch 191]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989789143827318
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020881577468
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989808546670014
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602018941293184
[INFO] [stdout] [Epoch 192]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989789912179738
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020804742198
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989807808744066
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020190150857525
[INFO] [stdout] [Epoch 193]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989790620883565
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020733871791
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498980712810468
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020190831496687
[INFO] [stdout] [Epoch 194]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989791274569415
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020206685031844
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989806500304595
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020191459296577
[INFO] [stdout] [Epoch 195]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979187750843
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020608209265
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989805921241784
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019203835923
[INFO] [stdout] [Epoch 196]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249897924336402
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020552596074
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498980538713269
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019257246818
[INFO] [stdout] [Epoch 197]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989792946598438
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020501300238
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498980489448748
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019306511327
[INFO] [stdout] [Epoch 198]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989793419734782
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020453986591
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989804440087232
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020193519513424
[INFO] [stdout] [Epoch 199]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979385614069
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020204103459915
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989804020962902
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019393863766
[INFO] [stdout] [Epoch 200]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979425866761
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020203700932926
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989803634375973
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019432522452
[INFO] [stdout] [Epoch 201]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979462994563
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020332965483
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.249898032778005
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020194681799935
[INFO] [stdout] [Epoch 202]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989794972400653
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020298719975
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989802948906648
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020195010693736
[INFO] [stdout] [Epoch 203]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989795288270267
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020202671330084
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989802645545414
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019531405492
[INFO] [stdout] [Epoch 204]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989795579618346
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020202379981966
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498980236573468
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020195593865614
[INFO] [stdout] [Epoch 205]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989795848348537
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020202111251756
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989802107646183
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019585195408
[INFO] [stdout] [Epoch 206]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249897960962167
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020186338355
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498980186959357
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020196090006664
[INFO] [stdout] [Epoch 207]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989796324842403
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020163475782
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498980165002141
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019630957881
[INFO] [stdout] [Epoch 208]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979653571949
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020142388071
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989801447495036
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019651210516
[INFO] [stdout] [Epoch 209]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989796730225794
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020201229374396
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989801260691163
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019669890902
[INFO] [stdout] [Epoch 210]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989796909632223
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020201049967945
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989801088389219
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019687121095
[INFO] [stdout] [Epoch 211]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989797075110998
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020200884489164
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498980092946339
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020197030136766
[INFO] [stdout] [Epoch 212]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989797227743346
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260202007318568
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498980078287527
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019717672487
[INFO] [stdout] [Epoch 213]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989797368526562
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020059107357
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498980064766706
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019731193307
[INFO] [stdout] [Epoch 214]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989797498380525
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260202004612196
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989800522955308
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019743664482
[INFO] [stdout] [Epoch 215]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979761815369
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020034144643
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989800407925147
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019755167497
[INFO] [stdout] [Epoch 216]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989797728628638
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020023097148
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498980030182501
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201976577751
[INFO] [stdout] [Epoch 217]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989797830527208
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260202001290729
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498980020396161
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019775563849
[INFO] [stdout] [Epoch 218]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979792451521
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020003508489
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498980011369553
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020197845904575
[INFO] [stdout] [Epoch 219]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979801120675
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199948393347
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989800030436976
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019792916312
[INFO] [stdout] [Epoch 220]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979809116826
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019986843185
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799953641934
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198005958156
[INFO] [stdout] [Epoch 221]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798164922214
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019979467788
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979988280864
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019807679146
[INFO] [stdout] [Epoch 222]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979823295052
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019972664958
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799817474254
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198142125844
[INFO] [stdout] [Epoch 223]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979829569766
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019966390244
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.249897997572119
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019820238819
[INFO] [stdout] [Epoch 224]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798353573614
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199606026473
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799701627827
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019825797227
[INFO] [stdout] [Epoch 225]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798406956568
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199552643525
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979965035883
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019830924126
[INFO] [stdout] [Epoch 226]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798456195308
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019950340478
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799603069948
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019835653014
[INFO] [stdout] [Epoch 227]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979850161154
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199457988546
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799559452203
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198400147876
[INFO] [stdout] [Epoch 228]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979854350202
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019941609807
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979951922058
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019844037951
[INFO] [stdout] [Epoch 229]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798582140482
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019937745961
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979948211221
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019847748788
[INFO] [stdout] [Epoch 230]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979861777936
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019934182073
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799447884622
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019851171546
[INFO] [stdout] [Epoch 231]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979865065153
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019930894855
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979941631419
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019854328589
[INFO] [stdout] [Epoch 232]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798680971767
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199278628314
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799387194642
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198572405445
[INFO] [stdout] [Epoch 233]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798708938188
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199250661896
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979936033568
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198599264405
[INFO] [stdout] [Epoch 234]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798734733537
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019922486654
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799335561835
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019862403825
[INFO] [stdout] [Epoch 235]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798758526333
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019920107375
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799312711236
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019864688885
[INFO] [stdout] [Epoch 236]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979878047205
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199179128023
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799291634557
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198667965533
[INFO] [stdout] [Epoch 237]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798800714097
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019915888599
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.249897992721941
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019868740599
[INFO] [stdout] [Epoch 238]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979881938471
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019914021538
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799254262848
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198705337233
[INFO] [stdout] [Epoch 239]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798836605878
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019912299421
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799237723634
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198721876453
[INFO] [stdout] [Epoch 240]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798852490144
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019910710993
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799222468387
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201987371317
[INFO] [stdout] [Epoch 241]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798867141289
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199092458796
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799208397429
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019875120265
[INFO] [stdout] [Epoch 242]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798880655023
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019907894506
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799195418838
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019876418124
[INFO] [stdout] [Epoch 243]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979889311966
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199066480415
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799183447792
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019877615229
[INFO] [stdout] [Epoch 244]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979890461666
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199054983423
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979917240608
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198787194004
[INFO] [stdout] [Epoch 245]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798915221118
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199044378955
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799162221552
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019879737853
[INFO] [stdout] [Epoch 246]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798925002335
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199034597746
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799152827674
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198806772404
[INFO] [stdout] [Epoch 247]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979893402422
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199025575863
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799144163052
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019881543703
[INFO] [stdout] [Epoch 248]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979894234572
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019901725436
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799136171087
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019882342899
[INFO] [stdout] [Epoch 249]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798950021203
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019900957888
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799128799556
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019883080052
[INFO] [stdout] [Epoch 250]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979895710082
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199002499267
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799122000295
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198837599784
[INFO] [stdout] [Epoch 251]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979896363083
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198995969246
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979911572887
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198843871206
[INFO] [stdout] [Epoch 252]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979896965391
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019898994618
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799109944302
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198849655785
[INFO] [stdout] [Epoch 253]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798975209412
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019898439067
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799104608804
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019885499128
[INFO] [stdout] [Epoch 254]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798980333625
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019897926646
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799099687512
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019885991257
[INFO] [stdout] [Epoch 255]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798985060033
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019897454005
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979909514826
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019886445182
[INFO] [stdout] [Epoch 256]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979898941953
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198970180564
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799090961407
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019886863867
[INFO] [stdout] [Epoch 257]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979899344058
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198966159497
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799087099585
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198872500494
[INFO] [stdout] [Epoch 258]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798997149476
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989624506
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799083537562
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019887606252
[INFO] [stdout] [Epoch 259]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799000570448
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019895902963
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799080252062
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198879348017
[INFO] [stdout] [Epoch 260]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979900372584
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198955874246
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799077221617
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019888237847
[INFO] [stdout] [Epoch 261]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979900663628
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989529638
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799074426433
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198885173645
[INFO] [stdout] [Epoch 262]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799009320765
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019895027932
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799071848257
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019888775183
[INFO] [stdout] [Epoch 263]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979901179685
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198947803236
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799069470223
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019889012986
[INFO] [stdout] [Epoch 264]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799014080713
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019894551936
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799067276802
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198892323276
[INFO] [stdout] [Epoch 265]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799016187275
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198943412803
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799065253657
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019889434643
[INFO] [stdout] [Epoch 266]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979901813031
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019894146978
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799063387574
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198896212515
[INFO] [stdout] [Epoch 267]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799019922498
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198939677586
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799061666357
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019889793372
[INFO] [stdout] [Epoch 268]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799021575543
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019893802454
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799060078763
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198899521324
[INFO] [stdout] [Epoch 269]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979902310028
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989364998
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799058614412
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019890098567
[INFO] [stdout] [Epoch 270]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799024506634
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198935093447
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799057263745
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019890233634
[INFO] [stdout] [Epoch 271]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979902580382
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198933796257
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979905601793
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198903582153
[INFO] [stdout] [Epoch 272]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249897990270003
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198932599775
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799054868828
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019890473125
[INFO] [stdout] [Epoch 273]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799028103893
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198931496186
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799053808937
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019890579115
[INFO] [stdout] [Epoch 274]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979902912182
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019893047825
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799052831319
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019890676876
[INFO] [stdout] [Epoch 275]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799030060716
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892953937
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.249897990519296
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019890767047
[INFO] [stdout] [Epoch 276]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799030926726
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198928673355
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979905109789
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019890850219
[INFO] [stdout] [Epoch 277]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799031725501
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892787458
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799050330752
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019890926933
[INFO] [stdout] [Epoch 278]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799032462268
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892713782
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799049623151
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019890997693
[INFO] [stdout] [Epoch 279]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799033141846
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892645823
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799048970485
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989106296
[INFO] [stdout] [Epoch 280]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799033768667
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892583142
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799048368488
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891123159
[INFO] [stdout] [Epoch 281]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799034346818
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198925253263
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799047813235
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891178685
[INFO] [stdout] [Epoch 282]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799034880095
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892471999
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799047301073
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198912299014
[INFO] [stdout] [Epoch 283]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799035371973
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892422811
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799046828678
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891277141
[INFO] [stdout] [Epoch 284]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979903582566
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892377442
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904639295
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198913207127
[INFO] [stdout] [Epoch 285]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799036244126
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892335596
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799045991065
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891360901
[INFO] [stdout] [Epoch 286]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799036630092
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892296999
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904562037
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891397971
[INFO] [stdout] [Epoch 287]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799036986107
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198922613974
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799045278457
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891432162
[INFO] [stdout] [Epoch 288]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799037314483
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892228559
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799044963087
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198914636994
[INFO] [stdout] [Epoch 289]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979903761737
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892198271
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.249897990446722
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198914927883
[INFO] [stdout] [Epoch 290]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799037896737
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198921703347
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799044403893
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198915196197
[INFO] [stdout] [Epoch 291]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799038154423
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198921445653
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799044156408
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198915443676
[INFO] [stdout] [Epoch 292]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799038392108
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892120797
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799043928138
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891567194
[INFO] [stdout] [Epoch 293]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799038611332
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892098876
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799043717592
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198915882486
[INFO] [stdout] [Epoch 294]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799038813545
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892078653
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799043523386
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891607669
[INFO] [stdout] [Epoch 295]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799039000057
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892060003
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799043344268
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891625581
[INFO] [stdout] [Epoch 296]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799039172078
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198920428006
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799043179056
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891642103
[INFO] [stdout] [Epoch 297]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979903933076
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892026932
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799043026664
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198916573417
[INFO] [stdout] [Epoch 298]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799039477112
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892012297
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799042886104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198916713977
[INFO] [stdout] [Epoch 299]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799039612104
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891998797
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799042756447
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198916843634
[INFO] [stdout] [Epoch 300]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799039736624
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891986346
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799042636868
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198916963216
[INFO] [stdout] [Epoch 301]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799039851474
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198919748605
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799042526567
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198917073517
[INFO] [stdout] [Epoch 302]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799039957403
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198919642684
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799042424835
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891717525
[INFO] [stdout] [Epoch 303]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040055108
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891954497
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799042330996
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891726909
[INFO] [stdout] [Epoch 304]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040145236
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198919454857
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799042244437
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198917355647
[INFO] [stdout] [Epoch 305]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040228364
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891937172
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799042164595
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891743549
[INFO] [stdout] [Epoch 306]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904030504
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891929504
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904209096
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891750912
[INFO] [stdout] [Epoch 307]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904037576
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198919224313
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904202304
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891757705
[INFO] [stdout] [Epoch 308]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040440994
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891915909
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904196039
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891763969
[INFO] [stdout] [Epoch 309]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040501162
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891909892
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041902605
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891769748
[INFO] [stdout] [Epoch 310]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040556662
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891904342
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041849303
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891775078
[INFO] [stdout] [Epoch 311]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040607855
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891899223
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904180014
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891779994
[INFO] [stdout] [Epoch 312]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904065506
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918945026
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041754795
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891784528
[INFO] [stdout] [Epoch 313]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040698607
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918901477
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041712973
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891788711
[INFO] [stdout] [Epoch 314]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040738778
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989188613
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041674388
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198917925697
[INFO] [stdout] [Epoch 315]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904077584
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891882424
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041638802
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198917961274
[INFO] [stdout] [Epoch 316]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040810007
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891879008
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904160599
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198917994086
[INFO] [stdout] [Epoch 317]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904084152
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891875857
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041575722
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891802436
[INFO] [stdout] [Epoch 318]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040870594
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891872948
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041547806
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918052273
[INFO] [stdout] [Epoch 319]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040897404
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918702686
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041522054
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891807803
[INFO] [stdout] [Epoch 320]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904092214
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918677945
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041498298
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891810178
[INFO] [stdout] [Epoch 321]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904094495
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918655135
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041476388
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989181237
[INFO] [stdout] [Epoch 322]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040966
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891863408
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041456173
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918143905
[INFO] [stdout] [Epoch 323]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040985403
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891861468
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041437538
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918162546
[INFO] [stdout] [Epoch 324]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041003308
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891859678
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041420344
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891817974
[INFO] [stdout] [Epoch 325]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041019825
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918580256
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904140448
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989181956
[INFO] [stdout] [Epoch 326]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041035052
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918565024
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904138985
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891821023
[INFO] [stdout] [Epoch 327]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041049104
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891855098
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041376354
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891822373
[INFO] [stdout] [Epoch 328]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904106207
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891853802
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904136391
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891823617
[INFO] [stdout] [Epoch 329]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041074015
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891852607
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041352434
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918247645
[INFO] [stdout] [Epoch 330]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041085037
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891851505
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041341845
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891825823
[INFO] [stdout] [Epoch 331]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904109521
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891850488
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041332078
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918268006
[INFO] [stdout] [Epoch 332]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041104593
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891849549
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041323063
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891827702
[INFO] [stdout] [Epoch 333]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041113253
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918486825
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904131475
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918285337
[INFO] [stdout] [Epoch 334]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041121238
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891847885
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904130708
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918293
[INFO] [stdout] [Epoch 335]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041128605
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918471477
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904130001
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918300075
[INFO] [stdout] [Epoch 336]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904113539
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918464693
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041293492
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918306586
[INFO] [stdout] [Epoch 337]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041141647
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891845844
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041287478
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989183126
[INFO] [stdout] [Epoch 338]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041147423
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918452664
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041281927
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891831815
[INFO] [stdout] [Epoch 339]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041152752
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891844733
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904127681
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891832327
[INFO] [stdout] [Epoch 340]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041157668
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891844241
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041272093
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891832799
[INFO] [stdout] [Epoch 341]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041162203
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843787
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126774
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918332343
[INFO] [stdout] [Epoch 342]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041166383
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184337
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041263724
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891833636
[INFO] [stdout] [Epoch 343]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041170244
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918429827
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041260016
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918340065
[INFO] [stdout] [Epoch 344]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041173802
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918426274
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041256597
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918343485
[INFO] [stdout] [Epoch 345]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041177083
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918423
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041253444
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891834664
[INFO] [stdout] [Epoch 346]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041180108
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918419973
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904125055
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918349535
[INFO] [stdout] [Epoch 347]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041182895
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918417187
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041247873
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891835221
[INFO] [stdout] [Epoch 348]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041185462
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918414617
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041245397
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918354687
[INFO] [stdout] [Epoch 349]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041187843
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841224
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904124311
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891835697
[INFO] [stdout] [Epoch 350]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904119003
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918410054
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041241012
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891835907
[INFO] [stdout] [Epoch 351]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904119205
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840804
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904123907
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891836101
[INFO] [stdout] [Epoch 352]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041193916
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840617
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041237284
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918362797
[INFO] [stdout] [Epoch 353]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041195632
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840446
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904123564
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891836444
[INFO] [stdout] [Epoch 354]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041197205
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840287
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904123412
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891836596
[INFO] [stdout] [Epoch 355]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041198665
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840142
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041232721
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918367365
[INFO] [stdout] [Epoch 356]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041200014
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840006
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041231422
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918368653
[INFO] [stdout] [Epoch 357]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041201258
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839882
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041230235
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918369847
[INFO] [stdout] [Epoch 358]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041202396
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918397686
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041229135
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891837094
[INFO] [stdout] [Epoch 359]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041203456
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839662
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041228114
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891837196
[INFO] [stdout] [Epoch 360]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041204433
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839565
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041227181
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989183729
[INFO] [stdout] [Epoch 361]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041205338
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839474
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041226315
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891837376
[INFO] [stdout] [Epoch 362]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041206165
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839392
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041225516
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918374565
[INFO] [stdout] [Epoch 363]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041206937
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918393156
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041224783
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891837529
[INFO] [stdout] [Epoch 364]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904120763
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839245
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041224106
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891837597
[INFO] [stdout] [Epoch 365]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041208285
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918391796
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041223476
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891837661
[INFO] [stdout] [Epoch 366]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041208896
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918391185
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.249897990412229
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918377185
[INFO] [stdout] [Epoch 367]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041209446
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839064
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041222366
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891837772
[INFO] [stdout] [Epoch 368]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041209962
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839012
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041221866
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891837821
[INFO] [stdout] [Epoch 369]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121044
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891838964
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904122141
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891837867
[INFO] [stdout] [Epoch 370]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121087
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918389215
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041221
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918379084
[INFO] [stdout] [Epoch 371]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041211275
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918388804
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041220612
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891837947
[INFO] [stdout] [Epoch 372]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121164
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918388443
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041220256
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891837982
[INFO] [stdout] [Epoch 373]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121198
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918388104
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041219934
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838015
[INFO] [stdout] [Epoch 374]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041212296
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891838778
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041219635
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918380444
[INFO] [stdout] [Epoch 375]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121258
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989183875
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041219357
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918380727
[INFO] [stdout] [Epoch 376]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121285
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918387233
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041219102
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918380977
[INFO] [stdout] [Epoch 377]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121309
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918386994
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121887
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918381215
[INFO] [stdout] [Epoch 378]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041213318
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918386767
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041218647
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838143
[INFO] [stdout] [Epoch 379]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041213523
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891838656
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041218447
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838164
[INFO] [stdout] [Epoch 380]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041213723
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918386356
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041218258
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918381826
[INFO] [stdout] [Epoch 381]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041213906
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891838618
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121808
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918382
[INFO] [stdout] [Epoch 382]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041214067
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918386017
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041217925
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918382154
[INFO] [stdout] [Epoch 383]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041214222
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891838586
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121778
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918382303
[INFO] [stdout] [Epoch 384]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121436
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918385723
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041217647
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918382437
[INFO] [stdout] [Epoch 385]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041214494
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891838559
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041217514
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918382564
[INFO] [stdout] [Epoch 386]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121461
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918385473
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041217403
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918382675
[INFO] [stdout] [Epoch 387]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041214722
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891838536
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041217303
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838278
[INFO] [stdout] [Epoch 388]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041214822
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918385257
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041217203
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838288
[INFO] [stdout] [Epoch 389]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041214916
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918385157
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041217114
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838297
[INFO] [stdout] [Epoch 390]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215005
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891838508
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041217026
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918383053
[INFO] [stdout] [Epoch 391]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215083
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918385
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216948
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838313
[INFO] [stdout] [Epoch 392]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215155
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891838493
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121688
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918383197
[INFO] [stdout] [Epoch 393]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215222
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891838486
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216815
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918383264
[INFO] [stdout] [Epoch 394]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215288
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384796
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121676
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918383325
[INFO] [stdout] [Epoch 395]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215344
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891838474
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216704
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838338
[INFO] [stdout] [Epoch 396]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249897990412154
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891838468
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216648
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918383436
[INFO] [stdout] [Epoch 397]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121545
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384635
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216604
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838348
[INFO] [stdout] [Epoch 398]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215494
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891838459
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121656
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918383525
[INFO] [stdout] [Epoch 399]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215538
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384546
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216515
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838357
[INFO] [stdout] [Epoch 400]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215582
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989183845
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121647
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838361
[INFO] [stdout] [Epoch 401]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215616
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891838447
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216437
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838364
[INFO] [stdout] [Epoch 402]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121565
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384435
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216404
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918383675
[INFO] [stdout] [Epoch 403]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215682
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384396
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121637
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838371
[INFO] [stdout] [Epoch 404]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121571
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384363
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216348
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838373
[INFO] [stdout] [Epoch 405]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215732
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891838434
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216326
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838375
[INFO] [stdout] [Epoch 406]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215754
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891838432
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216304
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918383775
[INFO] [stdout] [Epoch 407]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215777
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384296
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216282
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918383797
[INFO] [stdout] [Epoch 408]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249897990412158
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384285
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121627
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838381
[INFO] [stdout] [Epoch 409]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121581
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384274
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121626
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838382
[INFO] [stdout] [Epoch 410]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121582
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384263
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216249
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838383
[INFO] [stdout] [Epoch 411]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215832
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891838425
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216237
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838384
[INFO] [stdout] [Epoch 412]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215843
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891838424
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216226
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838385
[INFO] [stdout] [Epoch 413]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215854
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891838423
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216215
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838387
[INFO] [stdout] [Epoch 414]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215865
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891838422
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216204
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838388
[INFO] [stdout] [Epoch 415]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215877
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891838421
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216193
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838389
[INFO] [stdout] [Epoch 416]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215888
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384196
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216182
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989183839
[INFO] [stdout] [Epoch 417]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249897990412159
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384185
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121617
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918383913
[INFO] [stdout] [Epoch 418]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121591
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384174
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121616
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918383924
[INFO] [stdout] [Epoch 419]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121592
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384163
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216149
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918383936
[INFO] [stdout] [Epoch 420]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215932
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891838415
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216137
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918383947
[INFO] [stdout] [Epoch 421]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215943
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384135
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216126
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838396
[INFO] [stdout] [Epoch 422]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215954
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384124
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216115
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838397
[INFO] [stdout] [Epoch 423]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215965
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 424]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 425]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 426]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 427]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 428]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 429]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 430]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 431]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 432]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 433]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 434]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 435]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 436]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 437]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 438]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 439]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 440]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 441]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 442]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 443]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 444]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 445]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 446]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 447]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 448]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 449]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 450]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 451]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 452]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 453]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 454]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 455]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 456]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 457]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 458]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 459]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 460]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 461]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 462]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 463]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 464]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 465]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 466]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 467]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 468]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 469]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 470]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 471]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 472]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 473]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 474]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 475]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 476]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 477]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 478]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 479]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 480]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 481]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 482]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 483]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 484]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 485]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 486]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 487]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 488]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 489]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 490]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 491]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 492]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 493]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 494]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 495]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 496]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 497]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 498]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 499]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 500]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 501]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 502]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 503]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 504]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 505]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 506]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 507]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 508]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 509]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 510]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 511]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 512]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 513]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 514]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 515]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 516]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 517]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 518]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 519]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 520]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 521]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 522]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 523]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 524]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 525]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 526]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 527]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 528]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 529]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 530]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 531]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 532]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 533]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 534]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 535]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 536]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 537]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 538]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 539]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 540]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 541]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 542]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 543]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 544]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 545]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 546]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 547]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 548]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 549]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 550]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 551]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 552]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 553]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 554]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 555]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 556]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 557]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 558]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 559]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 560]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 561]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 562]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 563]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 564]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 565]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 566]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 567]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 568]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 569]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 570]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 571]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 572]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 573]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 574]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 575]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 576]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 577]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 578]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 579]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 580]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 581]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 582]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 583]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 584]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 585]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 586]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 587]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 588]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 589]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 590]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 591]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 592]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 593]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 594]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 595]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 596]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 597]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 598]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 599]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 600]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 601]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 602]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 603]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 604]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 605]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 606]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 607]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 608]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 609]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 610]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 611]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 612]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 613]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 614]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 615]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 616]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 617]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 618]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 619]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 620]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 621]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 622]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 623]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 624]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 625]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 626]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 627]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 628]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 629]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 630]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 631]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 632]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 633]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 634]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 635]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 636]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 637]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 638]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 639]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 640]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 641]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 642]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 643]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 644]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 645]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 646]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 647]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 648]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 649]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 650]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 651]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 652]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 653]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 654]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 655]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 656]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 657]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 658]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 659]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 660]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 661]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 662]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 663]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 664]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 665]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 666]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 667]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 668]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 669]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 670]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 671]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 672]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 673]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 674]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 675]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 676]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 677]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 678]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 679]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 680]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 681]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 682]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 683]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 684]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 685]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 686]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 687]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 688]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 689]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 690]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 691]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 692]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 693]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 694]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 695]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 696]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 697]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 698]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 699]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 700]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 701]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 702]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 703]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 704]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 705]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 706]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 707]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 708]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 709]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 710]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 711]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 712]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 713]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 714]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 715]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 716]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 717]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 718]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 719]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 720]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 721]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 722]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 723]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 724]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 725]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 726]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 727]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 728]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 729]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 730]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 731]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 732]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 733]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 734]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 735]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 736]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 737]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 738]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 739]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 740]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 741]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 742]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 743]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 744]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 745]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 746]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 747]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 748]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 749]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 750]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 751]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 752]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 753]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 754]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 755]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 756]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 757]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 758]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 759]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 760]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 761]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 762]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 763]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 764]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 765]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 766]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 767]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 768]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 769]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 770]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 771]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 772]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 773]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 774]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 775]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 776]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 777]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 778]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 779]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 780]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 781]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 782]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 783]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 784]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 785]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 786]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 787]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 788]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 789]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 790]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 791]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 792]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 793]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stderr] error: test failed, to rerun pass `--lib`
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 794]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 795]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 796]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 797]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 798]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 799]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 800]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 801]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 802]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 803]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 804]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 805]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 806]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 807]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 808]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 809]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 810]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 811]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 812]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 813]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 814]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 815]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 816]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 817]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 818]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 819]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 820]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 821]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 822]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 823]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 824]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 825]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 826]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 827]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 828]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 829]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 830]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 831]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 832]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 833]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 834]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 835]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 836]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 837]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 838]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 839]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 840]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 841]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 842]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 843]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 844]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 845]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 846]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 847]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 848]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 849]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 850]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 851]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 852]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 853]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 854]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 855]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 856]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 857]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 858]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 859]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 860]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 861]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 862]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 863]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 864]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 865]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 866]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 867]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 868]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 869]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 870]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 871]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 872]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 873]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 874]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 875]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 876]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 877]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 878]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 879]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 880]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 881]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 882]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 883]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 884]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 885]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 886]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 887]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 888]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 889]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 890]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 891]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 892]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 893]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 894]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 895]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 896]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 897]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 898]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 899]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 900]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 901]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 902]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 903]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 904]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 905]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 906]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 907]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 908]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 909]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 910]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 911]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 912]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 913]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 914]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 915]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 916]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 917]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 918]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 919]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 920]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 921]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 922]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 923]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 924]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 925]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 926]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 927]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 928]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 929]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 930]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 931]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 932]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 933]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 934]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 935]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 936]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 937]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 938]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 939]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 940]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 941]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 942]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 943]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 944]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 945]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 946]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 947]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 948]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 949]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 950]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 951]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 952]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 953]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 954]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 955]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 956]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 957]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 958]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 959]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 960]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 961]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 962]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 963]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 964]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 965]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 966]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 967]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 968]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 969]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 970]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 971]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 972]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 973]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 974]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 975]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 976]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 977]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 978]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 979]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 980]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 981]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 982]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 983]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 984]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 985]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 986]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 987]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 988]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 989]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 990]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 991]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 992]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 993]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 994]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 995]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 996]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 997]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 998]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] [Epoch 999]
[INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597
[INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113
[INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104
[INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398
[INFO] [stdout] thread 'models::sequential::test_sequential_xor1' panicked at src/models/sequential.rs:242:5:
[INFO] [stdout] assertion `left == right` failed
[INFO] [stdout]   left: [0.0, 0.0, 0.0, 0.0]
[INFO] [stdout]  right: [0.0, 1.0, 1.0, 0.0]
[INFO] [stdout] stack backtrace:
[INFO] [stdout]    0:     0x55deda990636 - std::backtrace_rs::backtrace::libunwind::trace::hbee8a7973eeb6c93
[INFO] [stdout]                                at /rustc/07dca489ac2d933c78d3c5158e3f43beefeb02ce/library/std/src/../../backtrace/src/backtrace/libunwind.rs:104:5
[INFO] [stdout]    1:     0x55deda990636 - std::backtrace_rs::backtrace::trace_unsynchronized::hc8ac75eea3aa6899
[INFO] [stdout]                                at /rustc/07dca489ac2d933c78d3c5158e3f43beefeb02ce/library/std/src/../../backtrace/src/backtrace/mod.rs:66:5
[INFO] [stdout]    2:     0x55deda990636 - std::sys_common::backtrace::_print_fmt::hc7f3e3b5298b1083
[INFO] [stdout]                                at /rustc/07dca489ac2d933c78d3c5158e3f43beefeb02ce/library/std/src/sys_common/backtrace.rs:68:5
[INFO] [stdout]    3:     0x55deda990636 - <std::sys_common::backtrace::_print::DisplayBacktrace as core::fmt::Display>::fmt::hbb235daedd7c6190
[INFO] [stdout]                                at /rustc/07dca489ac2d933c78d3c5158e3f43beefeb02ce/library/std/src/sys_common/backtrace.rs:44:22
[INFO] [stdout]    4:     0x55deda9b9a80 - core::fmt::rt::Argument::fmt::h76c38a80d925a410
[INFO] [stdout]                                at /rustc/07dca489ac2d933c78d3c5158e3f43beefeb02ce/library/core/src/fmt/rt.rs:142:9
[INFO] [stdout]    5:     0x55deda9b9a80 - core::fmt::write::h3ed6aeaa977c8e45
[INFO] [stdout]                                at /rustc/07dca489ac2d933c78d3c5158e3f43beefeb02ce/library/core/src/fmt/mod.rs:1120:17
[INFO] [stdout]    6:     0x55deda98e04f - std::io::Write::write_fmt::h1299aa7741865f2b
[INFO] [stdout]                                at /rustc/07dca489ac2d933c78d3c5158e3f43beefeb02ce/library/std/src/io/mod.rs:1810:15
[INFO] [stdout]    7:     0x55deda990414 - std::sys_common::backtrace::_print::h5d645a07e0fcfdbb
[INFO] [stdout]                                at /rustc/07dca489ac2d933c78d3c5158e3f43beefeb02ce/library/std/src/sys_common/backtrace.rs:47:5
[INFO] [stdout]    8:     0x55deda990414 - std::sys_common::backtrace::print::h85035a511aafe7a8
[INFO] [stdout]                                at /rustc/07dca489ac2d933c78d3c5158e3f43beefeb02ce/library/std/src/sys_common/backtrace.rs:34:9
[INFO] [stdout]    9:     0x55deda992187 - std::panicking::default_hook::{{closure}}::hcce8cea212785a25
[INFO] [stdout]   10:     0x55deda991e6d - std::panicking::default_hook::hf5fcb0f213fe709a
[INFO] [stdout]                                at /rustc/07dca489ac2d933c78d3c5158e3f43beefeb02ce/library/std/src/panicking.rs:289:9
[INFO] [stdout]   11:     0x55deda8f4837 - <alloc::boxed::Box<F,A> as core::ops::function::Fn<Args>>::call::h60265c2dfa87ee34
[INFO] [stdout]                                at /rustc/07dca489ac2d933c78d3c5158e3f43beefeb02ce/library/alloc/src/boxed.rs:2029:9
[INFO] [stdout]   12:     0x55deda8f4837 - test::test_main::{{closure}}::h77865bd3127078c6
[INFO] [stdout]                                at /rustc/07dca489ac2d933c78d3c5158e3f43beefeb02ce/library/test/src/lib.rs:138:21
[INFO] [stdout]   13:     0x55deda9927a6 - <alloc::boxed::Box<F,A> as core::ops::function::Fn<Args>>::call::hbc5ccf4eb663e1e5
[INFO] [stdout]                                at /rustc/07dca489ac2d933c78d3c5158e3f43beefeb02ce/library/alloc/src/boxed.rs:2029:9
[INFO] [stdout]   14:     0x55deda9927a6 - std::panicking::rust_panic_with_hook::h095fccf1dc9379ee
[INFO] [stdout]                                at /rustc/07dca489ac2d933c78d3c5158e3f43beefeb02ce/library/std/src/panicking.rs:783:13
[INFO] [stdout]   15:     0x55deda9924f2 - std::panicking::begin_panic_handler::{{closure}}::h032ba12139b353db
[INFO] [stdout]                                at /rustc/07dca489ac2d933c78d3c5158e3f43beefeb02ce/library/std/src/panicking.rs:657:13
[INFO] [stdout]   16:     0x55deda990b36 - std::sys_common::backtrace::__rust_end_short_backtrace::h9259bc2ff8fd0f76
[INFO] [stdout]                                at /rustc/07dca489ac2d933c78d3c5158e3f43beefeb02ce/library/std/src/sys_common/backtrace.rs:171:18
[INFO] [stdout]   17:     0x55deda992250 - rust_begin_unwind
[INFO] [stdout]                                at /rustc/07dca489ac2d933c78d3c5158e3f43beefeb02ce/library/std/src/panicking.rs:645:5
[INFO] [stdout]   18:     0x55deda881135 - core::panicking::panic_fmt::h784f20a50eaab275
[INFO] [stdout]                                at /rustc/07dca489ac2d933c78d3c5158e3f43beefeb02ce/library/core/src/panicking.rs:72:14
[INFO] [stdout]   19:     0x55deda88156b - core::panicking::assert_failed_inner::hbf94b40c37b92af0
[INFO] [stdout]                                at /rustc/07dca489ac2d933c78d3c5158e3f43beefeb02ce/library/core/src/panicking.rs:342:17
[INFO] [stdout]   20:     0x55deda89787e - core::panicking::assert_failed::h1c168fdeeef782f7
[INFO] [stdout]                                at /rustc/07dca489ac2d933c78d3c5158e3f43beefeb02ce/library/core/src/panicking.rs:297:5
[INFO] [stdout]   21:     0x55deda8b5352 - easynn::models::sequential::test_sequential_xor1::h0f3312d41ea051f6
[INFO] [stdout]                                at /opt/rustwide/workdir/src/models/sequential.rs:242:5
[INFO] [stdout]   22:     0x55deda8b3f67 - easynn::models::sequential::test_sequential_xor1::{{closure}}::h341b723dc379e71c
[INFO] [stdout]                                at /opt/rustwide/workdir/src/models/sequential.rs:205:26
[INFO] [stdout]   23:     0x55deda8924f6 - core::ops::function::FnOnce::call_once::hd55dfbe029b7cb18
[INFO] [stdout]                                at /rustc/07dca489ac2d933c78d3c5158e3f43beefeb02ce/library/core/src/ops/function.rs:250:5
[INFO] [stdout]   24:     0x55deda8fa10f - core::ops::function::FnOnce::call_once::h8dc6907944022cf6
[INFO] [stdout]                                at /rustc/07dca489ac2d933c78d3c5158e3f43beefeb02ce/library/core/src/ops/function.rs:250:5
[INFO] [stdout]   25:     0x55deda8fa10f - test::__rust_begin_short_backtrace::haae1a87433f1efb3
[INFO] [stdout]                                at /rustc/07dca489ac2d933c78d3c5158e3f43beefeb02ce/library/test/src/lib.rs:627:18
[INFO] [stdout]   26:     0x55deda8f8e91 - test::run_test_in_process::{{closure}}::h8c7decfa7c14e152
[INFO] [stdout]                                at /rustc/07dca489ac2d933c78d3c5158e3f43beefeb02ce/library/test/src/lib.rs:650:60
[INFO] [stdout]   27:     0x55deda8f8e91 - <core::panic::unwind_safe::AssertUnwindSafe<F> as core::ops::function::FnOnce<()>>::call_once::h19e6ff056d9d21e9
[INFO] [stdout]                                at /rustc/07dca489ac2d933c78d3c5158e3f43beefeb02ce/library/core/src/panic/unwind_safe.rs:272:9
[INFO] [stdout]   28:     0x55deda8f8e91 - std::panicking::try::do_call::h89c848fcaa37c035
[INFO] [stdout]                                at /rustc/07dca489ac2d933c78d3c5158e3f43beefeb02ce/library/std/src/panicking.rs:552:40
[INFO] [stdout]   29:     0x55deda8f8e91 - std::panicking::try::h57ab3dc74e2839b8
[INFO] [stdout]                                at /rustc/07dca489ac2d933c78d3c5158e3f43beefeb02ce/library/std/src/panicking.rs:516:19
[INFO] [stdout]   30:     0x55deda8f8e91 - std::panic::catch_unwind::hfb6a1b1abc120fb9
[INFO] [stdout]                                at /rustc/07dca489ac2d933c78d3c5158e3f43beefeb02ce/library/std/src/panic.rs:142:14
[INFO] [stdout]   31:     0x55deda8f8e91 - test::run_test_in_process::h5ae2f9875edd562d
[INFO] [stdout]                                at /rustc/07dca489ac2d933c78d3c5158e3f43beefeb02ce/library/test/src/lib.rs:650:27
[INFO] [stdout]   32:     0x55deda8f8e91 - test::run_test::{{closure}}::h35d7300d8928a067
[INFO] [stdout]                                at /rustc/07dca489ac2d933c78d3c5158e3f43beefeb02ce/library/test/src/lib.rs:573:43
[INFO] [stdout]   33:     0x55deda8c01c6 - test::run_test::{{closure}}::h7525ced405d23d1b
[INFO] [stdout]                                at /rustc/07dca489ac2d933c78d3c5158e3f43beefeb02ce/library/test/src/lib.rs:601:41
[INFO] [stdout]   34:     0x55deda8c01c6 - std::sys_common::backtrace::__rust_begin_short_backtrace::h4e7db78ce05afad8
[INFO] [stdout]                                at /rustc/07dca489ac2d933c78d3c5158e3f43beefeb02ce/library/std/src/sys_common/backtrace.rs:155:18
[INFO] [stdout]   35:     0x55deda8c5227 - std::thread::Builder::spawn_unchecked_::{{closure}}::{{closure}}::hcfbcb64f1a1b3482
[INFO] [stdout]                                at /rustc/07dca489ac2d933c78d3c5158e3f43beefeb02ce/library/std/src/thread/mod.rs:529:17
[INFO] [stdout]   36:     0x55deda8c5227 - <core::panic::unwind_safe::AssertUnwindSafe<F> as core::ops::function::FnOnce<()>>::call_once::h9d89c5c4108bd689
[INFO] [stdout]                                at /rustc/07dca489ac2d933c78d3c5158e3f43beefeb02ce/library/core/src/panic/unwind_safe.rs:272:9
[INFO] [stdout]   37:     0x55deda8c5227 - std::panicking::try::do_call::h8a4869bc94ec50c9
[INFO] [stdout]                                at /rustc/07dca489ac2d933c78d3c5158e3f43beefeb02ce/library/std/src/panicking.rs:552:40
[INFO] [stdout]   38:     0x55deda8c5227 - std::panicking::try::h9a576f20ff81ac30
[INFO] [stdout]                                at /rustc/07dca489ac2d933c78d3c5158e3f43beefeb02ce/library/std/src/panicking.rs:516:19
[INFO] [stdout]   39:     0x55deda8c5227 - std::panic::catch_unwind::hbcb4e3f860ef9830
[INFO] [stdout]                                at /rustc/07dca489ac2d933c78d3c5158e3f43beefeb02ce/library/std/src/panic.rs:142:14
[INFO] [stdout]   40:     0x55deda8c5227 - std::thread::Builder::spawn_unchecked_::{{closure}}::h93c79a6be1505948
[INFO] [stdout]                                at /rustc/07dca489ac2d933c78d3c5158e3f43beefeb02ce/library/std/src/thread/mod.rs:528:30
[INFO] [stdout]   41:     0x55deda8c5227 - core::ops::function::FnOnce::call_once{{vtable.shim}}::h426d96740c81bdaf
[INFO] [stdout]                                at /rustc/07dca489ac2d933c78d3c5158e3f43beefeb02ce/library/core/src/ops/function.rs:250:5
[INFO] [stdout]   42:     0x55deda997335 - <alloc::boxed::Box<F,A> as core::ops::function::FnOnce<Args>>::call_once::h12de4fc57affb195
[INFO] [stdout]                                at /rustc/07dca489ac2d933c78d3c5158e3f43beefeb02ce/library/alloc/src/boxed.rs:2015:9
[INFO] [stdout]   43:     0x55deda997335 - <alloc::boxed::Box<F,A> as core::ops::function::FnOnce<Args>>::call_once::h3c619f45059d5cf1
[INFO] [stdout]                                at /rustc/07dca489ac2d933c78d3c5158e3f43beefeb02ce/library/alloc/src/boxed.rs:2015:9
[INFO] [stdout]   44:     0x55deda997335 - std::sys::unix::thread::Thread::new::thread_start::hbac657605e4b7389
[INFO] [stdout]                                at /rustc/07dca489ac2d933c78d3c5158e3f43beefeb02ce/library/std/src/sys/unix/thread.rs:108:17
[INFO] [stdout]   45:     0x7f562d817ac3 - <unknown>
[INFO] [stdout]   46:     0x7f562d8a8bf4 - __clone
[INFO] [stdout]   47:                0x0 - <unknown>
[INFO] [stdout] 
[INFO] [stdout] 
[INFO] [stdout] failures:
[INFO] [stdout]     models::sequential::test_sequential_xor1
[INFO] [stdout] 
[INFO] [stdout] test result: FAILED. 6 passed; 1 failed; 0 ignored; 0 measured; 0 filtered out; finished in 1.43s
[INFO] [stdout] 
[INFO] running `Command { std: "docker" "inspect" "0dda0c006e42beb458f5ea6aff011718cf6f7bc0ef6995759245fb17b7568376", kill_on_drop: false }`
[INFO] running `Command { std: "docker" "rm" "-f" "0dda0c006e42beb458f5ea6aff011718cf6f7bc0ef6995759245fb17b7568376", kill_on_drop: false }`
[INFO] [stdout] 0dda0c006e42beb458f5ea6aff011718cf6f7bc0ef6995759245fb17b7568376
