[INFO] fetching crate easynn 0.1.7-beta... [INFO] testing easynn-0.1.7-beta against try#c2e32f1c9652b13ed99608599c1e855462f421f3 for pr-146098-7 [INFO] extracting crate easynn 0.1.7-beta into /workspace/builds/worker-6-tc2/source [INFO] started tweaking crates.io crate easynn 0.1.7-beta [INFO] finished tweaking crates.io crate easynn 0.1.7-beta [INFO] tweaked toml for crates.io crate easynn 0.1.7-beta written to /workspace/builds/worker-6-tc2/source/Cargo.toml [INFO] validating manifest of crates.io crate easynn 0.1.7-beta on toolchain c2e32f1c9652b13ed99608599c1e855462f421f3 [INFO] running `Command { std: CARGO_HOME="/workspace/cargo-home" RUSTUP_HOME="/workspace/rustup-home" "/workspace/cargo-home/bin/cargo" "+c2e32f1c9652b13ed99608599c1e855462f421f3" "metadata" "--manifest-path" "Cargo.toml" "--no-deps", kill_on_drop: false }` [INFO] running `Command { std: CARGO_HOME="/workspace/cargo-home" RUSTUP_HOME="/workspace/rustup-home" "/workspace/cargo-home/bin/cargo" "+c2e32f1c9652b13ed99608599c1e855462f421f3" "generate-lockfile" "--manifest-path" "Cargo.toml", kill_on_drop: false }` [INFO] [stderr] Updating crates.io index [INFO] [stderr] Locking 28 packages to latest compatible versions [INFO] [stderr] Adding itertools v0.10.5 (available: v0.14.0) [INFO] [stderr] Adding rand v0.8.5 (available: v0.9.2) [INFO] running `Command { std: CARGO_HOME="/workspace/cargo-home" RUSTUP_HOME="/workspace/rustup-home" "/workspace/cargo-home/bin/cargo" "+c2e32f1c9652b13ed99608599c1e855462f421f3" "fetch" "--manifest-path" "Cargo.toml", kill_on_drop: false }` [INFO] running `Command { std: "docker" "create" "-v" "/var/lib/crater-agent-workspace/builds/worker-6-tc2/target:/opt/rustwide/target:rw,Z" "-v" "/var/lib/crater-agent-workspace/builds/worker-6-tc2/source:/opt/rustwide/workdir:ro,Z" "-v" "/var/lib/crater-agent-workspace/cargo-home:/opt/rustwide/cargo-home:ro,Z" "-v" "/var/lib/crater-agent-workspace/rustup-home:/opt/rustwide/rustup-home:ro,Z" "-e" "SOURCE_DIR=/opt/rustwide/workdir" "-e" "CARGO_TARGET_DIR=/opt/rustwide/target" "-e" "CARGO_HOME=/opt/rustwide/cargo-home" "-e" "RUSTUP_HOME=/opt/rustwide/rustup-home" "-w" "/opt/rustwide/workdir" "-m" "1610612736" "--user" "0:0" "--network" "none" "ghcr.io/rust-lang/crates-build-env/linux@sha256:4848fb76d95f26979359cc7e45710b1dbc8f3acb7aeedee7c460d7702230f228" "/opt/rustwide/cargo-home/bin/cargo" "+c2e32f1c9652b13ed99608599c1e855462f421f3" "metadata" "--no-deps" "--format-version=1", kill_on_drop: false }` [INFO] [stdout] 2a1da9fb308b2d7d777449db469bcb4c8a09152971ed354a14dac7929645a32b [INFO] running `Command { std: "docker" "start" "-a" "2a1da9fb308b2d7d777449db469bcb4c8a09152971ed354a14dac7929645a32b", kill_on_drop: false }` [INFO] running `Command { std: "docker" "inspect" "2a1da9fb308b2d7d777449db469bcb4c8a09152971ed354a14dac7929645a32b", kill_on_drop: false }` [INFO] running `Command { std: "docker" "rm" "-f" "2a1da9fb308b2d7d777449db469bcb4c8a09152971ed354a14dac7929645a32b", kill_on_drop: false }` [INFO] [stdout] 2a1da9fb308b2d7d777449db469bcb4c8a09152971ed354a14dac7929645a32b [INFO] running `Command { std: "docker" "create" "-v" "/var/lib/crater-agent-workspace/builds/worker-6-tc2/target:/opt/rustwide/target:rw,Z" "-v" "/var/lib/crater-agent-workspace/builds/worker-6-tc2/source:/opt/rustwide/workdir:ro,Z" "-v" "/var/lib/crater-agent-workspace/cargo-home:/opt/rustwide/cargo-home:ro,Z" "-v" "/var/lib/crater-agent-workspace/rustup-home:/opt/rustwide/rustup-home:ro,Z" "-e" "SOURCE_DIR=/opt/rustwide/workdir" "-e" "CARGO_TARGET_DIR=/opt/rustwide/target" "-e" "CARGO_INCREMENTAL=0" "-e" "RUST_BACKTRACE=full" "-e" "RUSTFLAGS=--cap-lints=forbid" "-e" "RUSTDOCFLAGS=--cap-lints=forbid" "-e" "CARGO_HOME=/opt/rustwide/cargo-home" "-e" "RUSTUP_HOME=/opt/rustwide/rustup-home" "-w" "/opt/rustwide/workdir" "-m" "1610612736" "--user" "0:0" "--network" "none" "ghcr.io/rust-lang/crates-build-env/linux@sha256:4848fb76d95f26979359cc7e45710b1dbc8f3acb7aeedee7c460d7702230f228" "/opt/rustwide/cargo-home/bin/cargo" "+c2e32f1c9652b13ed99608599c1e855462f421f3" "build" "--frozen" "--message-format=json", kill_on_drop: false }` [INFO] [stdout] d07ffd796b4d46555751440b8be829b369470c0ee22a28b51f84dd627278f9bc [INFO] running `Command { std: "docker" "start" "-a" "d07ffd796b4d46555751440b8be829b369470c0ee22a28b51f84dd627278f9bc", kill_on_drop: false }` [INFO] [stderr] Compiling zerocopy v0.8.27 [INFO] [stderr] Compiling rayon-core v1.13.0 [INFO] [stderr] Compiling num-traits v0.2.19 [INFO] [stderr] Compiling itertools v0.10.5 [INFO] [stderr] Compiling crossbeam-channel v0.5.15 [INFO] [stderr] Compiling crossbeam-queue v0.3.12 [INFO] [stderr] Compiling getrandom v0.2.16 [INFO] [stderr] Compiling num_cpus v1.17.0 [INFO] [stderr] Compiling rand_core v0.6.4 [INFO] [stderr] Compiling crossbeam v0.8.4 [INFO] [stderr] Compiling rayon v1.11.0 [INFO] [stderr] Compiling ppv-lite86 v0.2.21 [INFO] [stderr] Compiling rand_chacha v0.3.1 [INFO] [stderr] Compiling rand v0.8.5 [INFO] [stderr] Compiling easynn v0.1.7-beta (/opt/rustwide/workdir) [INFO] [stdout] warning: unused variable: `olen` [INFO] [stdout] --> src/layers/dense.rs:96:13 [INFO] [stdout] | [INFO] [stdout] 96 | let olen = output.flattened.len(); [INFO] [stdout] | ^^^^ help: if this is intentional, prefix it with an underscore: `_olen` [INFO] [stdout] | [INFO] [stdout] = note: `#[warn(unused_variables)]` (part of `#[warn(unused)]`) on by default [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `dlen` [INFO] [stdout] --> src/layers/dense.rs:148:13 [INFO] [stdout] | [INFO] [stdout] 148 | let dlen = delta.flattened.len(); [INFO] [stdout] | ^^^^ help: if this is intentional, prefix it with an underscore: `_dlen` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `dlen` [INFO] [stdout] --> src/layers/dense.rs:205:13 [INFO] [stdout] | [INFO] [stdout] 205 | let dlen = delta.flattened.len(); [INFO] [stdout] | ^^^^ help: if this is intentional, prefix it with an underscore: `_dlen` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: variable does not need to be mutable [INFO] [stdout] --> src/models/sequential.rs:137:17 [INFO] [stdout] | [INFO] [stdout] 137 | let mut cum_dw = &dws.par_chunks_mut(1).reduce_with(|dw1, dw2| { [INFO] [stdout] | ----^^^^^^ [INFO] [stdout] | | [INFO] [stdout] | help: remove this `mut` [INFO] [stdout] | [INFO] [stdout] = note: `#[warn(unused_mut)]` (part of `#[warn(unused)]`) on by default [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: variable does not need to be mutable [INFO] [stdout] --> src/models/sequential.rs:146:17 [INFO] [stdout] | [INFO] [stdout] 146 | let mut cum_db = &dbs.par_chunks_mut(1).reduce_with(|db1, db2| { [INFO] [stdout] | ----^^^^^^ [INFO] [stdout] | | [INFO] [stdout] | help: remove this `mut` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: function `determine_thread` is never used [INFO] [stdout] --> src/layers/dense.rs:18:4 [INFO] [stdout] | [INFO] [stdout] 18 | fn determine_thread(len: usize) -> usize { [INFO] [stdout] | ^^^^^^^^^^^^^^^^ [INFO] [stdout] | [INFO] [stdout] = note: `#[warn(dead_code)]` (part of `#[warn(unused)]`) on by default [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: method `pos2index` is never used [INFO] [stdout] --> src/tensor/mod.rs:38:19 [INFO] [stdout] | [INFO] [stdout] 26 | impl Tensor { [INFO] [stdout] | ----------------------- method in this implementation [INFO] [stdout] ... [INFO] [stdout] 38 | pub(crate) fn pos2index(&self, mut pos: usize) -> Result> { [INFO] [stdout] | ^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stderr] Finished `dev` profile [unoptimized + debuginfo] target(s) in 12.02s [INFO] running `Command { std: "docker" "inspect" "d07ffd796b4d46555751440b8be829b369470c0ee22a28b51f84dd627278f9bc", kill_on_drop: false }` [INFO] running `Command { std: "docker" "rm" "-f" "d07ffd796b4d46555751440b8be829b369470c0ee22a28b51f84dd627278f9bc", kill_on_drop: false }` [INFO] [stdout] d07ffd796b4d46555751440b8be829b369470c0ee22a28b51f84dd627278f9bc [INFO] running `Command { std: "docker" "create" "-v" "/var/lib/crater-agent-workspace/builds/worker-6-tc2/target:/opt/rustwide/target:rw,Z" "-v" "/var/lib/crater-agent-workspace/builds/worker-6-tc2/source:/opt/rustwide/workdir:ro,Z" "-v" "/var/lib/crater-agent-workspace/cargo-home:/opt/rustwide/cargo-home:ro,Z" "-v" "/var/lib/crater-agent-workspace/rustup-home:/opt/rustwide/rustup-home:ro,Z" "-e" "SOURCE_DIR=/opt/rustwide/workdir" "-e" "CARGO_TARGET_DIR=/opt/rustwide/target" "-e" "CARGO_INCREMENTAL=0" "-e" "RUST_BACKTRACE=full" "-e" "RUSTFLAGS=--cap-lints=forbid" "-e" "RUSTDOCFLAGS=--cap-lints=forbid" "-e" "CARGO_HOME=/opt/rustwide/cargo-home" "-e" "RUSTUP_HOME=/opt/rustwide/rustup-home" "-w" "/opt/rustwide/workdir" "-m" "1610612736" "--user" "0:0" "--network" "none" "ghcr.io/rust-lang/crates-build-env/linux@sha256:4848fb76d95f26979359cc7e45710b1dbc8f3acb7aeedee7c460d7702230f228" "/opt/rustwide/cargo-home/bin/cargo" "+c2e32f1c9652b13ed99608599c1e855462f421f3" "test" "--frozen" "--no-run" "--message-format=json", kill_on_drop: false }` [INFO] [stdout] 54a2712f80cb77b41b831580fdb651907e43f85794c8a7d80662e32a109a4708 [INFO] running `Command { std: "docker" "start" "-a" "54a2712f80cb77b41b831580fdb651907e43f85794c8a7d80662e32a109a4708", kill_on_drop: false }` [INFO] [stdout] warning: unused variable: `olen` [INFO] [stdout] --> src/layers/dense.rs:96:13 [INFO] [stdout] | [INFO] [stdout] 96 | let olen = output.flattened.len(); [INFO] [stdout] | ^^^^ help: if this is intentional, prefix it with an underscore: `_olen` [INFO] [stdout] | [INFO] [stdout] = note: `#[warn(unused_variables)]` (part of `#[warn(unused)]`) on by default [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `dlen` [INFO] [stdout] --> src/layers/dense.rs:148:13 [INFO] [stdout] | [INFO] [stdout] 148 | let dlen = delta.flattened.len(); [INFO] [stdout] | ^^^^ help: if this is intentional, prefix it with an underscore: `_dlen` [INFO] [stdout] [INFO] [stdout] [INFO] [stderr] Compiling easynn v0.1.7-beta (/opt/rustwide/workdir) [INFO] [stdout] warning: unused variable: `dlen` [INFO] [stdout] --> src/layers/dense.rs:205:13 [INFO] [stdout] | [INFO] [stdout] 205 | let dlen = delta.flattened.len(); [INFO] [stdout] | ^^^^ help: if this is intentional, prefix it with an underscore: `_dlen` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: variable does not need to be mutable [INFO] [stdout] --> src/models/sequential.rs:137:17 [INFO] [stdout] | [INFO] [stdout] 137 | let mut cum_dw = &dws.par_chunks_mut(1).reduce_with(|dw1, dw2| { [INFO] [stdout] | ----^^^^^^ [INFO] [stdout] | | [INFO] [stdout] | help: remove this `mut` [INFO] [stdout] | [INFO] [stdout] = note: `#[warn(unused_mut)]` (part of `#[warn(unused)]`) on by default [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: variable does not need to be mutable [INFO] [stdout] --> src/models/sequential.rs:146:17 [INFO] [stdout] | [INFO] [stdout] 146 | let mut cum_db = &dbs.par_chunks_mut(1).reduce_with(|db1, db2| { [INFO] [stdout] | ----^^^^^^ [INFO] [stdout] | | [INFO] [stdout] | help: remove this `mut` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: function `determine_thread` is never used [INFO] [stdout] --> src/layers/dense.rs:18:4 [INFO] [stdout] | [INFO] [stdout] 18 | fn determine_thread(len: usize) -> usize { [INFO] [stdout] | ^^^^^^^^^^^^^^^^ [INFO] [stdout] | [INFO] [stdout] = note: `#[warn(dead_code)]` (part of `#[warn(unused)]`) on by default [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: method `pos2index` is never used [INFO] [stdout] --> src/tensor/mod.rs:38:19 [INFO] [stdout] | [INFO] [stdout] 26 | impl Tensor { [INFO] [stdout] | ----------------------- method in this implementation [INFO] [stdout] ... [INFO] [stdout] 38 | pub(crate) fn pos2index(&self, mut pos: usize) -> Result> { [INFO] [stdout] | ^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused import: `crate::layers::activation::Activation::*` [INFO] [stdout] --> src/models/sequential.rs:180:9 [INFO] [stdout] | [INFO] [stdout] 180 | use crate::layers::activation::Activation::*; [INFO] [stdout] | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [INFO] [stdout] | [INFO] [stdout] = note: `#[warn(unused_imports)]` (part of `#[warn(unused)]`) on by default [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused import: `rand::Rng` [INFO] [stdout] --> src/models/sequential.rs:207:9 [INFO] [stdout] | [INFO] [stdout] 207 | use rand::Rng; [INFO] [stdout] | ^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `olen` [INFO] [stdout] --> src/layers/dense.rs:96:13 [INFO] [stdout] | [INFO] [stdout] 96 | let olen = output.flattened.len(); [INFO] [stdout] | ^^^^ help: if this is intentional, prefix it with an underscore: `_olen` [INFO] [stdout] | [INFO] [stdout] = note: `#[warn(unused_variables)]` (part of `#[warn(unused)]`) on by default [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `dlen` [INFO] [stdout] --> src/layers/dense.rs:148:13 [INFO] [stdout] | [INFO] [stdout] 148 | let dlen = delta.flattened.len(); [INFO] [stdout] | ^^^^ help: if this is intentional, prefix it with an underscore: `_dlen` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: unused variable: `dlen` [INFO] [stdout] --> src/layers/dense.rs:205:13 [INFO] [stdout] | [INFO] [stdout] 205 | let dlen = delta.flattened.len(); [INFO] [stdout] | ^^^^ help: if this is intentional, prefix it with an underscore: `_dlen` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: variable does not need to be mutable [INFO] [stdout] --> src/models/sequential.rs:137:17 [INFO] [stdout] | [INFO] [stdout] 137 | let mut cum_dw = &dws.par_chunks_mut(1).reduce_with(|dw1, dw2| { [INFO] [stdout] | ----^^^^^^ [INFO] [stdout] | | [INFO] [stdout] | help: remove this `mut` [INFO] [stdout] | [INFO] [stdout] = note: `#[warn(unused_mut)]` (part of `#[warn(unused)]`) on by default [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: variable does not need to be mutable [INFO] [stdout] --> src/models/sequential.rs:146:17 [INFO] [stdout] | [INFO] [stdout] 146 | let mut cum_db = &dbs.par_chunks_mut(1).reduce_with(|db1, db2| { [INFO] [stdout] | ----^^^^^^ [INFO] [stdout] | | [INFO] [stdout] | help: remove this `mut` [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: function `determine_thread` is never used [INFO] [stdout] --> src/layers/dense.rs:18:4 [INFO] [stdout] | [INFO] [stdout] 18 | fn determine_thread(len: usize) -> usize { [INFO] [stdout] | ^^^^^^^^^^^^^^^^ [INFO] [stdout] | [INFO] [stdout] = note: `#[warn(dead_code)]` (part of `#[warn(unused)]`) on by default [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] warning: method `pos2index` is never used [INFO] [stdout] --> src/tensor/mod.rs:38:19 [INFO] [stdout] | [INFO] [stdout] 26 | impl Tensor { [INFO] [stdout] | ----------------------- method in this implementation [INFO] [stdout] ... [INFO] [stdout] 38 | pub(crate) fn pos2index(&self, mut pos: usize) -> Result> { [INFO] [stdout] | ^^^^^^^^^ [INFO] [stdout] [INFO] [stdout] [INFO] [stderr] Finished `test` profile [unoptimized + debuginfo] target(s) in 1.19s [INFO] running `Command { std: "docker" "inspect" "54a2712f80cb77b41b831580fdb651907e43f85794c8a7d80662e32a109a4708", kill_on_drop: false }` [INFO] running `Command { std: "docker" "rm" "-f" "54a2712f80cb77b41b831580fdb651907e43f85794c8a7d80662e32a109a4708", kill_on_drop: false }` [INFO] [stdout] 54a2712f80cb77b41b831580fdb651907e43f85794c8a7d80662e32a109a4708 [INFO] running `Command { std: "docker" "create" "-v" "/var/lib/crater-agent-workspace/builds/worker-6-tc2/target:/opt/rustwide/target:rw,Z" "-v" "/var/lib/crater-agent-workspace/builds/worker-6-tc2/source:/opt/rustwide/workdir:ro,Z" "-v" "/var/lib/crater-agent-workspace/cargo-home:/opt/rustwide/cargo-home:ro,Z" "-v" "/var/lib/crater-agent-workspace/rustup-home:/opt/rustwide/rustup-home:ro,Z" "-e" "SOURCE_DIR=/opt/rustwide/workdir" "-e" "CARGO_TARGET_DIR=/opt/rustwide/target" "-e" "CARGO_INCREMENTAL=0" "-e" "RUST_BACKTRACE=full" "-e" "RUSTFLAGS=--cap-lints=forbid" "-e" "RUSTDOCFLAGS=--cap-lints=forbid" "-e" "CARGO_HOME=/opt/rustwide/cargo-home" "-e" "RUSTUP_HOME=/opt/rustwide/rustup-home" "-w" "/opt/rustwide/workdir" "-m" "1610612736" "--user" "0:0" "--network" "none" "ghcr.io/rust-lang/crates-build-env/linux@sha256:4848fb76d95f26979359cc7e45710b1dbc8f3acb7aeedee7c460d7702230f228" "/opt/rustwide/cargo-home/bin/cargo" "+c2e32f1c9652b13ed99608599c1e855462f421f3" "test" "--frozen", kill_on_drop: false }` [INFO] [stdout] 6b8c8393f44be39bcfd8747f3b4fc88a18869572be70e0347782458744637619 [INFO] running `Command { std: "docker" "start" "-a" "6b8c8393f44be39bcfd8747f3b4fc88a18869572be70e0347782458744637619", kill_on_drop: false }` [INFO] [stderr] warning: unused variable: `olen` [INFO] [stderr] --> src/layers/dense.rs:96:13 [INFO] [stderr] | [INFO] [stderr] 96 | let olen = output.flattened.len(); [INFO] [stderr] | ^^^^ help: if this is intentional, prefix it with an underscore: `_olen` [INFO] [stderr] | [INFO] [stderr] = note: `#[warn(unused_variables)]` (part of `#[warn(unused)]`) on by default [INFO] [stderr] [INFO] [stderr] warning: unused variable: `dlen` [INFO] [stderr] --> src/layers/dense.rs:148:13 [INFO] [stderr] | [INFO] [stderr] 148 | let dlen = delta.flattened.len(); [INFO] [stderr] | ^^^^ help: if this is intentional, prefix it with an underscore: `_dlen` [INFO] [stderr] [INFO] [stderr] warning: unused variable: `dlen` [INFO] [stderr] --> src/layers/dense.rs:205:13 [INFO] [stderr] | [INFO] [stderr] 205 | let dlen = delta.flattened.len(); [INFO] [stderr] | ^^^^ help: if this is intentional, prefix it with an underscore: `_dlen` [INFO] [stderr] [INFO] [stderr] warning: variable does not need to be mutable [INFO] [stderr] --> src/models/sequential.rs:137:17 [INFO] [stderr] | [INFO] [stderr] 137 | let mut cum_dw = &dws.par_chunks_mut(1).reduce_with(|dw1, dw2| { [INFO] [stderr] | ----^^^^^^ [INFO] [stderr] | | [INFO] [stderr] | help: remove this `mut` [INFO] [stderr] | [INFO] [stderr] = note: `#[warn(unused_mut)]` (part of `#[warn(unused)]`) on by default [INFO] [stderr] [INFO] [stderr] warning: variable does not need to be mutable [INFO] [stderr] --> src/models/sequential.rs:146:17 [INFO] [stderr] | [INFO] [stderr] 146 | let mut cum_db = &dbs.par_chunks_mut(1).reduce_with(|db1, db2| { [INFO] [stderr] | ----^^^^^^ [INFO] [stderr] | | [INFO] [stderr] | help: remove this `mut` [INFO] [stderr] [INFO] [stderr] warning: function `determine_thread` is never used [INFO] [stderr] --> src/layers/dense.rs:18:4 [INFO] [stderr] | [INFO] [stderr] 18 | fn determine_thread(len: usize) -> usize { [INFO] [stderr] | ^^^^^^^^^^^^^^^^ [INFO] [stderr] | [INFO] [stderr] = note: `#[warn(dead_code)]` (part of `#[warn(unused)]`) on by default [INFO] [stderr] [INFO] [stderr] warning: method `pos2index` is never used [INFO] [stderr] --> src/tensor/mod.rs:38:19 [INFO] [stderr] | [INFO] [stderr] 26 | impl Tensor { [INFO] [stderr] | ----------------------- method in this implementation [INFO] [stderr] ... [INFO] [stderr] 38 | pub(crate) fn pos2index(&self, mut pos: usize) -> Result> { [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)]` (part of `#[warn(unused)]`) on by default [INFO] [stderr] [INFO] [stderr] warning: unused import: `rand::Rng` [INFO] [stderr] --> src/models/sequential.rs:207:9 [INFO] [stderr] | [INFO] [stderr] 207 | use rand::Rng; [INFO] [stderr] | ^^^^^^^^^ [INFO] [stderr] [INFO] [stderr] warning: `easynn` (lib) 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` profile [unoptimized + debuginfo] target(s) in 0.10s [INFO] [stderr] Running unittests src/lib.rs (/opt/rustwide/target/debug/deps/easynn-206a226d148db962) [INFO] [stdout] [INFO] [stdout] running 7 tests [INFO] [stdout] test layers::dense::test_add_weight_delta_to ... ok [INFO] [stdout] test layers::dense::test_dense_descend ... ok [INFO] [stdout] test layers::dense::test_dense_forward ... ok [INFO] [stdout] test layers::dense::test_dense_backpropagate ... ok [INFO] [stdout] test models::sequential::test_sequential_predict ... ok [INFO] [stdout] test layers::dense::test_dense_activate ... 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): 0.0000000000010975798797043797 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.9999979465983012 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.9603980279130085 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.001568238095657239 [INFO] [stdout] [Epoch 1] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.0015061358670692122 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.9253809188934143 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.888735834505235 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.005795272722011325 [INFO] [stdout] [Epoch 2] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.0055657799222196755 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.8591212315137384 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.8251000307457942 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.012059736968289252 [INFO] [stdout] [Epoch 3] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.011582171384344999 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.8001873431881216 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.7684999243978721 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.019850429102427222 [INFO] [stdout] [Epoch 4] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.0190643521099711 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.7476849128834493 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.7180765903332649 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.02874851840006888 [INFO] [stdout] [Epoch 5] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.02761007707142615 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.7008376243127388 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.6730844543899543 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.03841251159198734 [INFO] [stdout] [Epoch 6] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.03689137613294464 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.6589707712254014 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.6328755286848756 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.04856555978769099 [INFO] [stdout] [Epoch 7] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.04664236362009843 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.62149719930274 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.5968859102103515 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.05898474906599121 [INFO] [stdout] [Epoch 8] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.05664895300297796 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.5879052601917292 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.5646242118881366 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.06949207175651305 [INFO] [stdout] [Epoch 9] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.06674018571495514 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.5577484840563987 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.5356616440877652 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.07994682119008596 [INFO] [stdout] [Epoch 10] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.07678092707095856 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.5306367203277967 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.5096235062028162 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.09023919157990273 [INFO] [stdout] [Epoch 11] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.08666571959333859 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.5062285332131439 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.48618188329790335 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.10028489773778668 [INFO] [stdout] [Epoch 12] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.09631361578737031 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.484224669937642 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.46504937300811144 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.11002065740562249 [INFO] [stdout] [Epoch 13] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.10566383937235986 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.4643624464514998 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.4459736935720204 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.11940040283496711 [INFO] [stdout] [Epoch 14] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.1146721468827024 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.4464109181318734 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.4287330457738512 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.12839210851080343 [INFO] [stdout] [Epoch 15] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.12330778101377561 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.4301667224334773 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.41313212022511175 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.13697513912644474 [INFO] [stdout] [Epoch 16] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.13155092361703752 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.41545049699370035 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.39899865731274986 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.14513803653336257 [INFO] [stdout] [Epoch 17] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.1393905702866414 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.4021037908046805 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.3861804806888153 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.15287667680136302 [INFO] [stdout] [Epoch 18] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.14682276040002903 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.38998639808901686 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.3745429367246919 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.16019273906199785 [INFO] [stdout] [Epoch 19] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.1538491065951427 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.3789740547665829 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.36396668219782613 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.16709243675277172 [INFO] [stdout] [Epoch 20] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.16047557625736197 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.3689564461403221 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.3543457708731653 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.17358546947086534 [INFO] [stdout] [Epoch 21] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.16671148487981907 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.3598354818828222 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.3455859968002624 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.17968416008617613 [INFO] [stdout] [Epoch 22] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.17256866734676357 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.35152380076349216 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.33760345825325794 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.18540274722745737 [INFO] [stdout] [Epoch 23] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.17806079843725006 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.34394347298057926 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.33032331145054833 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.19075680788925548 [INFO] [stdout] [Epoch 24] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.18320283829684095 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.33702487259119424 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.323678687636583 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.19576278883622605 [INFO] [stdout] [Epoch 25] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.1880105823983115 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.3307056964835966 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.31760975090284616 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.20043762881150629 [INFO] [stdout] [Epoch 26] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.1925002987105706 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.324930109709378 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.3120628773648866 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.20479845637746988 [INFO] [stdout] [Epoch 27] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.1966884375049221 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.31964799987416115 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.30698993907914435 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.20886235060715583 [INFO] [stdout] [Epoch 28] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.20059140152311244 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.31481432574662727 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.30234767844706084 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.21264615386819083 [INFO] [stdout] [Epoch 29] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.20422536617501047 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.31038854734895677 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2980971608739381 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.21616632765356267 [INFO] [stdout] [Epoch 30] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.20760614107848158 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.3063341265898104 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.29420329517685395 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.21943884386225604 [INFO] [stdout] [Epoch 31] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.21074906564531073 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.3026180890386574 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.29063441271272644 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2224791051575605 [INFO] [stdout] [Epoch 32] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.21366893259332112 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.29921063875582254 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.28736189746109203 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.22530188906580634 [INFO] [stdout] [Epoch 33] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.21637993425880042 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2960848192186476 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.28435986037758915 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.22792131135225818 [INFO] [stdout] [Epoch 34] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.21889562742270874 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2932162143483947 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2816048522601982 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.23035080494842558 [INFO] [stdout] [Epoch 35] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.22122891307246795 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.29058268446864105 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2790756101636828 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.23260311132697037 [INFO] [stdout] [Epoch 36] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.22339202811842235 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.28816413273406394 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2767528330777951 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.23469028174437684 [INFO] [stdout] [Epoch 37] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.22539654658729952 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2859422981758957 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2746189831681303 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2366236862126238 [INFO] [stdout] [Epoch 38] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.22725338823860394 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.28390057203161057 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.27265810937915874 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.23841402843199208 [INFO] [stdout] [Epoch 39] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2289728329060852 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.28202383447405244 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.27085569062888 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2400713652286696 [INFO] [stdout] [Epoch 40] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.23056453916561429 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2802983092399309 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.26919849619402964 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.24160512930215203 [INFO] [stdout] [Epoch 41] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2320375661817868 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.27871143398845544 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2676744612025126 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.24302415430635343 [INFO] [stdout] [Epoch 42] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.23340039779582183 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.27725174450563833 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2662725754232151 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2443367014714409 [INFO] [stdout] [Epoch 43] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.23466096809317186 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.27590877111508255 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.26498278377892515 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2455504871262722 [INFO] [stdout] [Epoch 44] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.23582668783607183 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.27467294586753666 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2637958972111823 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.24667271060868912 [INFO] [stdout] [Epoch 45] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.23690447126858502 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.27353551926399133 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2627035127011373 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2477100821568126 [INFO] [stdout] [Epoch 46] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2379007629034028 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2724884854247098 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.26169794140189134 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.24866885046229295 [INFO] [stdout] [Epoch 47] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.23882156398398613 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2715245147528846 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2607721439686703 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2495548296390722 [INFO] [stdout] [Epoch 48] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.23967245838536494 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2706368932595771 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25991967228649787 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25037342542104035 [INFO] [stdout] [Epoch 49] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24045863777436718 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2698194678188349 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2591346168932091 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2511296604510785 [INFO] [stdout] [Epoch 50] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2411849258972158 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2690665967105645 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25841155948082617 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25182819856411054 [INFO] [stdout] [Epoch 51] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2418558019009718 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26837310488578164 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25774552993230476 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2524733679994046 [INFO] [stdout] [Epoch 52] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2424754226266282 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26773424345587027 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2571319674150178 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2530691835037107 [INFO] [stdout] [Epoch 53] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24304764383696378 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2671456529658262 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25656668510837943 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2536193673079438 [INFO] [stdout] [Epoch 54] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24357604036254923 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2666033300623584 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.256045838191889 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25412736897689153 [INFO] [stdout] [Epoch 55] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24406392516540665 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26610359721216026 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25556589476255864 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2545963841445965 [INFO] [stdout] [Epoch 56] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24451436733247045 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26564307516452906 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2551236093880137 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2550293721582495 [INFO] [stdout] [Epoch 57] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24493020902078283 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26521865788655796 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25471599903425024 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2554290726611586 [INFO] [stdout] [Epoch 58] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24531408138377672 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26482748972898557 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25434032113571775 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25579802115107 [INFO] [stdout] [Epoch 59] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2456684195134876 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.264466944607027 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2539940536005887 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25613856355418035 [INFO] [stdout] [Epoch 60] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2459954764374348 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2641346070035939 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25367487656625165 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25645286985789656 [INFO] [stdout] [Epoch 61] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24629733621152386 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26382825462266435 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2533806557396068 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2567429468470362 [INFO] [stdout] [Epoch 62] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24657592615189355 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2635458425385196 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2531094271739942 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2570106499889277 [INFO] [stdout] [Epoch 63] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24683302824936615 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2632854887024561 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2528593833498389 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2572576945129423 [INFO] [stdout] [Epoch 64] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24707028981022974 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26304546068264045 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.252628860439608 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25748566572951986 [INFO] [stdout] [Epoch 65] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24728923336663086 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2628241635252594 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25241632664965913 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25769602863286334 [INFO] [stdout] [Epoch 66] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24749126589900192 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2626201286362047 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25222037154221094 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25789013683026163 [INFO] [stdout] [Epoch 67] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24767768741178325 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2624320035923988 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25203969625013983 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2580692408395561 [INFO] [stdout] [Epoch 68] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24784969890230968 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.262258542800669 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2518731045057624 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25823449579464924 [INFO] [stdout] [Epoch 69] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24800840976118113 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26209859892992504 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25171949441229996 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25838696859721955 [INFO] [stdout] [Epoch 70] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24815484464076965 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2619511150494291 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2515778508934716 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.258527644551011 [INFO] [stdout] [Epoch 71] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24828994982679098 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26181511741222496 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25144723876270086 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25865743351322656 [INFO] [stdout] [Epoch 72] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24841459914610278 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2616897088284474 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2513267963588408 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2587771755957183 [INFO] [stdout] [Epoch 73] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24852959944212782 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2615740625782906 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2512157297001902 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2588876464468449 [INFO] [stdout] [Epoch 74] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24863569564754986 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2614674168189788 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2511133071129473 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25898956214307783 [INFO] [stdout] [Epoch 75] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24873357548221195 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2613690694441885 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2510188542941986 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25908358371769874 [INFO] [stdout] [Epoch 76] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24882387380247784 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2612783733580699 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2509317497730904 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2591703213522494 [INFO] [stdout] [Epoch 77] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24890717662670028 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2611947321293684 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25085142073704547 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25925033825477756 [INFO] [stdout] [Epoch 78] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24898402485988838 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26111759599416284 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25077733919279394 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2593241542473749 [INFO] [stdout] [Epoch 79] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24905491773917884 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26104645817848177 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25070901843461385 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.259392249084027 [INFO] [stdout] [Epoch 80] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2491203160202996 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260980851514537 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2506460097945613 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2594550655183977 [INFO] [stdout] [Epoch 81] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24918064492386915 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2609203453265662 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2505878996516342 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25951301213983785 [INFO] [stdout] [Epoch 82] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24923629685910026 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2608645425643221 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2505343066787749 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2595664659946626 [INFO] [stdout] [Epoch 83] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24928763394127396 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26081307716410296 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2504848793084044 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25961577500855365 [INFO] [stdout] [Epoch 84] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24933499031821496 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2607656116189136 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2504392933988047 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25966126022483704 [INFO] [stdout] [Epoch 85] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24937867431993355 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2607218347408863 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2503972500851472 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2597032178723445 [INFO] [stdout] [Epoch 86] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24941897044459962 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2606814596004909 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2503584738003115 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25974192127558743 [INFO] [stdout] [Epoch 87] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24945614119307413 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26064422162835577 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25032271045187293 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2597776226190618 [INFO] [stdout] [Epoch 88] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24949042876334693 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2606098768666783 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2502897257427579 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2598105545766462 [INFO] [stdout] [Epoch 89] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24952205661541102 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2605782003582819 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2502593036240939 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25984093181625834 [INFO] [stdout] [Epoch 90] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24955123091633452 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26054898466235 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.250231244869721 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25986895238919366 [INFO] [stdout] [Epoch 91] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24957814187458163 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2605220384867605 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2502053657626847 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2598947990128744 [INFO] [stdout] [Epoch 92] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24960296497196455 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2604971854277648 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2501814968848253 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2599186402550963 [INFO] [stdout] [Epoch 93] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24962586210099447 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2604742628085027 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25015948200128607 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25994063162725706 [INFO] [stdout] [Epoch 94] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24964698261481766 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2604531206085326 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25013917703243466 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.25996091659349696 [INFO] [stdout] [Epoch 95] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24966646429639447 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2604336204771838 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25012044910628733 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.259979627502162 [INFO] [stdout] [Epoch 96] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2496844342530764 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26041563482411806 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.250103175685083 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2599968864455219 [INFO] [stdout] [Epoch 97] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24970100974227918 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26039904598101243 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25008724376016433 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2600128060532257 [INFO] [stdout] [Epoch 98] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24971629893351793 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2603837454287669 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25007254910978777 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2600274902245713 [INFO] [stdout] [Epoch 99] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24973040161167825 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2603696330850821 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2500589956149128 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26004103480427415 [INFO] [stdout] [Epoch 100] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2497434098260249 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26035661664766474 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2500464946284172 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2600535282060712 [INFO] [stdout] [Epoch 101] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2497554084891108 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26034461098869427 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25003496439354184 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2600650519881652 [INFO] [stdout] [Epoch 102] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24976647592943385 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2603335375965312 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.25002432950770853 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26007568138421094 [INFO] [stdout] [Epoch 103] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2497766844013962 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2603233240609635 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2500145204281495 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26008548579326185 [INFO] [stdout] [Epoch 104] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2497861005558487 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260313903598583 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.250005473016079 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26009452923183635 [INFO] [stdout] [Epoch 105] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24979478587425566 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26030521461514994 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24999712811639 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601028707510222 [INFO] [stdout] [Epoch 106] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24980279706928168 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26029720030205544 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24998943117009398 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260110564821311 [INFO] [stdout] [Epoch 107] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24981018645438707 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602898082642125 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24998233185694965 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26011766168765477 [INFO] [stdout] [Epoch 108] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24981700228482362 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602829901769251 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24997578376591884 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601242076970381 [INFO] [stdout] [Epoch 109] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498232890722354 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26027670146946785 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24996974409127698 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601302456006914 [INFO] [stdout] [Epoch 110] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249829087874904 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602709010332958 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24996417335237733 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26013581483289927 [INFO] [stdout] [Epoch 111] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24983443656551643 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26026555095295895 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24995903513522177 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26014095176821733 [INFO] [stdout] [Epoch 112] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498393700781959 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602606162579533 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499542958541384 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26014568995876064 [INFO] [stdout] [Epoch 113] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498439206363937 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26025606469387613 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24994992453199863 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26015006035310934 [INFO] [stdout] [Epoch 114] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24984811796312623 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26025186651137866 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24994589259752803 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601540914982496 [INFO] [stdout] [Epoch 115] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498519894749189 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602479942715327 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499421736983801 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26015780972586444 [INFO] [stdout] [Epoch 116] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24985556046072022 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26024442266633074 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.249938743528744 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260161239324185 [INFO] [stdout] [Epoch 117] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498588542469473 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602411283531404 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.249935579670356 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601644026965189 [INFO] [stdout] [Epoch 118] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24986189234973677 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26023808980203006 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499326614458697 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601673205074883 [INFO] [stdout] [Epoch 119] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24986469461539176 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602352871549603 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499299697836239 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601700118179292 [INFO] [stdout] [Epoch 120] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24986727934993927 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26023270209591914 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24992748709292073 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601724942093298 [INFO] [stdout] [Epoch 121] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498696634386403 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26023031773115096 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24992519714899733 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26017478389861765 [INFO] [stdout] [Epoch 122] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24987186245623236 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26022811847869115 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24992308498693502 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601768958440455 [INFO] [stdout] [Epoch 123] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24987389076862132 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26022608996648544 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24992113680381256 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26017884384286327 [INFO] [stdout] [Epoch 124] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24987576162668587 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602242189384241 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24991933986846257 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601806406214137 [INFO] [stdout] [Epoch 125] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24987748725280573 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26022249316767737 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499176824382374 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601822979182396 [INFO] [stdout] [Epoch 126] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498790789206773 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602209013767626 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24991615368224276 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26018382656074307 [INFO] [stdout] [Epoch 127] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24988054702893767 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26021943316382157 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24991474361053426 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26018523653589753 [INFO] [stdout] [Epoch 128] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24988190116907602 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26021807893462484 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24991344300881368 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26018653705547345 [INFO] [stdout] [Epoch 129] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498831501880767 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602168298398565 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24991224337819823 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601877366162032 [INFO] [stdout] [Epoch 130] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24988430224620156 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26021567771727133 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499111368796674 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260188843055278 [INFO] [stdout] [Epoch 131] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249885364870289 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26021461503834353 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24991011628282506 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26018986360153723 [INFO] [stdout] [Epoch 132] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498863450029164 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26021363485905996 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990917491864115 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601908049226869 [INFO] [stdout] [Epoch 133] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24988724904774848 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26021273077453433 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990830663586286 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26019167316885333 [INFO] [stdout] [Epoch 134] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498880829113668 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602118968771464 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990750576081153 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26019247401275664 [INFO] [stdout] [Epoch 135] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498888520418515 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602111277179318 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990676706030177 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26019321268676676 [INFO] [stdout] [Epoch 136] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498895614643708 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26021041827097013 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990608570743966 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260193894017084 [INFO] [stdout] [Epoch 137] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989021581400742 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602097639005388 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990545725007743 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601945224552659 [INFO] [stdout] [Epoch 138] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989081936603738 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602091603308175 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990487758171703 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601951021073083 [INFO] [stdout] [Epoch 139] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989137606385886 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602086036179448 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990434291467428 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26019563676046836 [INFO] [stdout] [Epoch 140] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989188954475383 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602080901242449 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990384975532476 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260196129908007 [INFO] [stdout] [Epoch 141] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989236316364988 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602076164944549 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990339488127447 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601965847720091 [INFO] [stdout] [Epoch 142] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498928000150375 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260207179633799 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990297532030062 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601970043244342 [INFO] [stdout] [Epoch 143] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498932029531866 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020677668776493 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990258833092938 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26019739130653247 [INFO] [stdout] [Epoch 144] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989357461079378 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602064050234495 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499022313845209 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601977482467535 [INFO] [stdout] [Epoch 145] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989391741618205 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020606221235393 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499019021487447 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601980774772656 [INFO] [stdout] [Epoch 146] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989423360916585 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602057460145146 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990159847233992 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601983811491918 [INFO] [stdout] [Epoch 147] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989452525568379 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602054543638659 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2499013183710568 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601986612466648 [INFO] [stdout] [Epoch 148] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498947942612969 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020518535473847 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990106001469087 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601989195997892 [INFO] [stdout] [Epoch 149] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989504238363752 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020493722940796 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990082171512343 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26019915789659886 [INFO] [stdout] [Epoch 150] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989527124389355 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602047083666082 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990060191529054 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601993776940855 [INFO] [stdout] [Epoch 151] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989548233739972 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602044972709379 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990039917900883 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601995804283711 [INFO] [stdout] [Epoch 152] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498956770434076 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020430256308896 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990021218159067 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26019976742409107 [INFO] [stdout] [Epoch 153] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989585663409708 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602041229708332 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24990003970118813 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2601999399030489 [INFO] [stdout] [Epoch 154] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989602228288818 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602039573207094 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989988061080937 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602000989921985 [INFO] [stdout] [Epoch 155] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989617507210743 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602038045303565 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989973387095432 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020024573100775 [INFO] [stdout] [Epoch 156] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989631600005985 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602036636014395 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989959852282254 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020038107824994 [INFO] [stdout] [Epoch 157] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989644598755123 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020353361312754 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989947368204765 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260200505918268 [INFO] [stdout] [Epoch 158] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498965658839046 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260203413716076 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989935853291945 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602006210667523 [INFO] [stdout] [Epoch 159] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498966764725089 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602033031268778 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989925232305343 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020072727607046 [INFO] [stdout] [Epoch 160] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989677847593808 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020320112294326 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989915435847476 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260200825240183 [INFO] [stdout] [Epoch 161] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989687256067175 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020310703777977 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989906399908365 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020091559917763 [INFO] [stdout] [Epoch 162] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989695934145015 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020302025663566 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498989806544729 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020099894345106 [INFO] [stdout] [Epoch 163] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498970393852904 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602029402124842 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989890378006982 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201075817567 [INFO] [stdout] [Epoch 164] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989711321519137 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602028663823186 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989883287357875 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020114672381395 [INFO] [stdout] [Epoch 165] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498971813135509 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602027982837337 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498987674716979 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602012121254871 [INFO] [stdout] [Epoch 166] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989724412531777 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602027354717753 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.249898707147093 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020127244991526 [INFO] [stdout] [Epoch 167] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989730206089866 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602026775360314 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989865150560453 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020132809125346 [INFO] [stdout] [Epoch 168] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989735549883976 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020262409795164 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989860018367274 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602013794130573 [INFO] [stdout] [Epoch 169] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989740478830025 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602025748083732 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989855284596157 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602014267506596 [INFO] [stdout] [Epoch 170] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989745025133348 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020252934523963 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989850918316805 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020147041336056 [INFO] [stdout] [Epoch 171] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989749218499152 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020248741149615 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989846891000095 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602015106864489 [INFO] [stdout] [Epoch 172] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989753086326555 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602024487331494 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498984317633167 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602015478330662 [INFO] [stdout] [Epoch 173] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989756653887676 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602024130574763 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989839750040033 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020158209592553 [INFO] [stdout] [Epoch 174] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989759944492687 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020238015137365 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989836589737932 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020161369889794 [INFO] [stdout] [Epoch 175] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989762979642158 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020234979983436 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989833674776085 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602016428484752 [INFO] [stdout] [Epoch 176] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989765779167555 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602023218045422 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498983098610824 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602016697351185 [INFO] [stdout] [Epoch 177] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989768361360784 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020229598257755 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989828506166745 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020169453450365 [INFO] [stdout] [Epoch 178] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498977074309373 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020227216522057 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989826218747788 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602017174086678 [INFO] [stdout] [Epoch 179] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989772939928462 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020225019684984 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989824108905462 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602017385070694 [INFO] [stdout] [Epoch 180] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989774966218947 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020222993392506 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989822162854153 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602017579675642 [INFO] [stdout] [Epoch 181] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498977683520486 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602022112440489 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989820367878457 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602017759173055 [INFO] [stdout] [Epoch 182] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989778559098022 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020219400510286 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989818712250084 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020179247357583 [INFO] [stdout] [Epoch 183] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989780149162225 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602021781044486 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498981718515124 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201807744553 [INFO] [stdout] [Epoch 184] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989781615786866 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020216343819164 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989815776603935 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602018218300164 [INFO] [stdout] [Epoch 185] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989782968554777 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602021499105037 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498981447740478 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020183482199977 [INFO] [stdout] [Epoch 186] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989784216304858 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602021374329953 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498981327906487 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602018468053919 [INFO] [stdout] [Epoch 187] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989785367189837 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020212592413905 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989812173754322 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020185785849137 [INFO] [stdout] [Epoch 188] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498978642872951 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602021153087369 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498981115425109 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020186805351864 [INFO] [stdout] [Epoch 189] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989787407859929 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260202105517428 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989810213893787 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020187745708745 [INFO] [stdout] [Epoch 190] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989788310978678 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020209648623654 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989809346538167 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020188613063994 [INFO] [stdout] [Epoch 191] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989789143986657 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020881561534 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498980854651698 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602018941308488 [INFO] [stdout] [Epoch 192] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989789912326718 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020804727499 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989807808602907 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020190150998684 [INFO] [stdout] [Epoch 193] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989790621019134 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020733858234 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989807127974475 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019083162688 [INFO] [stdout] [Epoch 194] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989791274694462 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260202066849068 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989806500184492 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019145941668 [INFO] [stdout] [Epoch 195] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989791877623782 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260202060819773 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989805921131006 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019203847 [INFO] [stdout] [Epoch 196] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979243374659 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020552585435 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989805387030514 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020192572570355 [INFO] [stdout] [Epoch 197] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989792946696573 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020205012904235 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498980489439323 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020193065207525 [INFO] [stdout] [Epoch 198] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979341982531 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020204539775393 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989804440000288 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020193519600365 [INFO] [stdout] [Epoch 199] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979385622419 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020410337642 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989804020882717 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019393871785 [INFO] [stdout] [Epoch 200] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989794258744627 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020203700855904 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989803634302013 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020194325298485 [INFO] [stdout] [Epoch 201] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989794630016665 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260202033295838 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498980327773228 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020194681868153 [INFO] [stdout] [Epoch 202] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989794972466176 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020298713424 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498980294884371 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019501075667 [INFO] [stdout] [Epoch 203] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989795288330707 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020202671269643 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989802645487372 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019531411297 [INFO] [stdout] [Epoch 204] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989795579674096 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020202379926216 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989802365681146 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020195593919154 [INFO] [stdout] [Epoch 205] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989795848399957 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020211120033 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498980210759679 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020195852003475 [INFO] [stdout] [Epoch 206] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989796096264133 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020186333612 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989801869548003 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020196090052233 [INFO] [stdout] [Epoch 207] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989796324886163 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020163471406 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989801649979385 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019630962083 [INFO] [stdout] [Epoch 208] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989796535759845 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020201423840356 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989801447456286 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020196512143906 [INFO] [stdout] [Epoch 209] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989796730263006 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020201229337175 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989801260655423 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020196698944753 [INFO] [stdout] [Epoch 210] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989796909666542 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020104993362 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989801088356253 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019687124391 [INFO] [stdout] [Epoch 211] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979707514265 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020200884457517 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989800929432998 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019703016715 [INFO] [stdout] [Epoch 212] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989797227772534 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020200731827614 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989800782847232 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019717675291 [INFO] [stdout] [Epoch 213] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989797368553496 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020059104664 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989800647641197 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019731195894 [INFO] [stdout] [Epoch 214] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989797498405367 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020046119477 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989800522931455 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020197436668674 [INFO] [stdout] [Epoch 215] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989797618176593 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020200341423527 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498980040790316 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019755169696 [INFO] [stdout] [Epoch 216] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989797728649762 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020023095035 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989800301804718 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020197657795396 [INFO] [stdout] [Epoch 217] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249897978305467 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602020012905341 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989800203942897 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020197755657215 [INFO] [stdout] [Epoch 218] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979792453319 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020200035066915 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989800113678268 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020197845921833 [INFO] [stdout] [Epoch 219] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798011223327 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019994837677 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989800030421058 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020197929179045 [INFO] [stdout] [Epoch 220] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798091183554 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199868416544 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799953627248 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019800597285 [INFO] [stdout] [Epoch 221] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798164936322 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019979466378 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799882795097 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198076804996 [INFO] [stdout] [Epoch 222] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798232963523 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019972663656 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799817461766 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198142138323 [INFO] [stdout] [Epoch 223] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798295709648 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019966389044 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979975720038 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019820239972 [INFO] [stdout] [Epoch 224] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798353584686 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019960601541 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799701617194 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019825798289 [INFO] [stdout] [Epoch 225] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979840696677 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199552633316 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979965034904 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198309251047 [INFO] [stdout] [Epoch 226] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798456204704 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019950339538 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799603060925 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198356539165 [INFO] [stdout] [Epoch 227] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979850162021 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199457979876 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979955944388 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201984001562 [INFO] [stdout] [Epoch 228] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798543510017 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019941609007 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.249897995192129 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019844038719 [INFO] [stdout] [Epoch 229] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798582147857 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019937745223 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799482105116 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019847749497 [INFO] [stdout] [Epoch 230] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798617786171 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199341813915 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799447878086 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198511722 [INFO] [stdout] [Epoch 231] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798650657807 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019930894227 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799416308164 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198543291917 [INFO] [stdout] [Epoch 232] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798680977554 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199278622536 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799387189082 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019857241101 [INFO] [stdout] [Epoch 233] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798708943534 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019925065655 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799360330553 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198599269534 [INFO] [stdout] [Epoch 234] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798734738464 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199224861623 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799335557095 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198624042995 [INFO] [stdout] [Epoch 235] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979875853089 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019920106919 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979931270685 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198646893233 [INFO] [stdout] [Epoch 236] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798780476263 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019917912382 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799291630516 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198667969563 [INFO] [stdout] [Epoch 237] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798800717972 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019915888212 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979927219038 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201986874097 [INFO] [stdout] [Epoch 238] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979881938828 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019914021181 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979925425942 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198705340664 [INFO] [stdout] [Epoch 239] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798836609176 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201991229909 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799237720473 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019872187961 [INFO] [stdout] [Epoch 240] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979885249318 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199107106895 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799222465467 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198737134614 [INFO] [stdout] [Epoch 241] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798867144078 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019909245601 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799208394742 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019875120535 [INFO] [stdout] [Epoch 242] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798880657615 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019907894247 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979919541635 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198764183733 [INFO] [stdout] [Epoch 243] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798893122053 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199066478034 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799183445505 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019877615458 [INFO] [stdout] [Epoch 244] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979890461886 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199054981225 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979917240397 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019878719611 [INFO] [stdout] [Epoch 245] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798915223144 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019904437694 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979916221961 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019879738047 [INFO] [stdout] [Epoch 246] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979892500421 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199034595876 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799152825876 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019880677421 [INFO] [stdout] [Epoch 247] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798934025947 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019902557414 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799144161398 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019881543868 [INFO] [stdout] [Epoch 248] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798942347308 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019901725277 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799136169566 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019882343052 [INFO] [stdout] [Epoch 249] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979895002267 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199009577416 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799128798146 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019883080194 [INFO] [stdout] [Epoch 250] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979895710218 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020199002497907 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799121998985 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198837601094 [INFO] [stdout] [Epoch 251] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798963632096 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019899596799 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979911572766 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198843872416 [INFO] [stdout] [Epoch 252] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979896965507 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019898994501 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799109943192 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198849656895 [INFO] [stdout] [Epoch 253] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798975210478 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198984389603 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979910460777 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198854992305 [INFO] [stdout] [Epoch 254] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798980334613 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198979265474 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799099686558 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198859913524 [INFO] [stdout] [Epoch 255] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798985060943 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019897453913 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799095147394 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019886445269 [INFO] [stdout] [Epoch 256] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798989420367 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198970179725 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799090960596 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019886863949 [INFO] [stdout] [Epoch 257] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798993441362 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198966158725 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979908709883 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198872501255 [INFO] [stdout] [Epoch 258] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989798997150203 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198962449875 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799083536862 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019887606322 [INFO] [stdout] [Epoch 259] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979900057112 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198959028973 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799080251418 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198879348666 [INFO] [stdout] [Epoch 260] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799003726457 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198955873625 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799077221028 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019888237906 [INFO] [stdout] [Epoch 261] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799006636845 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198952963236 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979907442589 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019888517419 [INFO] [stdout] [Epoch 262] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799009321287 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989502788 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799071847757 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019888775232 [INFO] [stdout] [Epoch 263] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799011797328 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019894780276 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799069469767 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019889013031 [INFO] [stdout] [Epoch 264] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799014081152 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019894551893 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979906727638 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201988923237 [INFO] [stdout] [Epoch 265] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799016187675 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019894341241 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979906525328 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201988943468 [INFO] [stdout] [Epoch 266] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979901813067 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198941469413 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979906338723 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198896212854 [INFO] [stdout] [Epoch 267] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799019922826 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019893967726 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799061666035 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019889793405 [INFO] [stdout] [Epoch 268] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979902157586 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198938024225 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799060078463 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019889952162 [INFO] [stdout] [Epoch 269] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799023100562 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198936499517 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799058614134 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019890098595 [INFO] [stdout] [Epoch 270] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799024506906 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198935093175 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979905726349 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019890233659 [INFO] [stdout] [Epoch 271] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799025804063 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019893379602 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799056017697 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019890358238 [INFO] [stdout] [Epoch 272] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799027000517 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019893259957 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799054868617 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019890473146 [INFO] [stdout] [Epoch 273] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799028104098 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019893149598 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799053808737 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019890579134 [INFO] [stdout] [Epoch 274] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799029122003 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019893047807 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979905283114 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198906768943 [INFO] [stdout] [Epoch 275] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799030060889 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198929539196 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799051929446 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198907670633 [INFO] [stdout] [Epoch 276] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799030926876 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198928673205 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979905109775 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019890850234 [INFO] [stdout] [Epoch 277] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799031725646 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892787444 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799050330608 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019890926947 [INFO] [stdout] [Epoch 278] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249897990324624 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198927137683 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904962303 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019890997705 [INFO] [stdout] [Epoch 279] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799033141957 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198926458116 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799048970385 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198910629705 [INFO] [stdout] [Epoch 280] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799033768767 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198925831306 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799048368389 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198911231696 [INFO] [stdout] [Epoch 281] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799034346918 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892525316 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799047813135 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891178694 [INFO] [stdout] [Epoch 282] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799034880178 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198924719906 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799047300995 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891229909 [INFO] [stdout] [Epoch 283] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979903537205 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198924228033 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.249897990468286 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198912771475 [INFO] [stdout] [Epoch 284] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799035825722 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892377436 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799046392894 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891320718 [INFO] [stdout] [Epoch 285] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979903624418 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198923355903 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904599101 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891360907 [INFO] [stdout] [Epoch 286] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799036630148 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892296994 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799045620326 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198913979753 [INFO] [stdout] [Epoch 287] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799036986152 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892261393 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799045278413 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891432166 [INFO] [stdout] [Epoch 288] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799037314528 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892228555 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799044963043 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891463704 [INFO] [stdout] [Epoch 289] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799037617413 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892198267 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799044672156 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891492793 [INFO] [stdout] [Epoch 290] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799037896782 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198921703297 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904440385 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891519623 [INFO] [stdout] [Epoch 291] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799038154456 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892144562 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799044156386 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989154437 [INFO] [stdout] [Epoch 292] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979903839213 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892120795 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799043928115 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198915671966 [INFO] [stdout] [Epoch 293] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799038611354 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198920988735 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904371757 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198915882514 [INFO] [stdout] [Epoch 294] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799038813568 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892078651 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799043523364 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198916076714 [INFO] [stdout] [Epoch 295] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979903900008 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892060001 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799043344246 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891625583 [INFO] [stdout] [Epoch 296] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249897990391721 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198920427984 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799043179034 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891642105 [INFO] [stdout] [Epoch 297] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799039330782 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989202693 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799043026642 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891657344 [INFO] [stdout] [Epoch 298] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799039477129 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019892012296 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799042886082 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198916714 [INFO] [stdout] [Epoch 299] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799039612126 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198919987947 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799042756425 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198916843656 [INFO] [stdout] [Epoch 300] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799039736646 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198919863435 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799042636845 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891696324 [INFO] [stdout] [Epoch 301] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799039851496 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198919748583 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799042526545 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891707354 [INFO] [stdout] [Epoch 302] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799039957425 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198919642656 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799042424812 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198917175275 [INFO] [stdout] [Epoch 303] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904005513 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198919544946 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799042330973 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891726911 [INFO] [stdout] [Epoch 304] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040145258 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891945483 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799042244415 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891735567 [INFO] [stdout] [Epoch 305] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040228386 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198919371695 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799042164573 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891743551 [INFO] [stdout] [Epoch 306] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040305063 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891929501 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799042090938 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198917509146 [INFO] [stdout] [Epoch 307] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040375782 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891922429 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799042023017 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891757707 [INFO] [stdout] [Epoch 308] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040441016 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198919159065 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041960367 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198917639714 [INFO] [stdout] [Epoch 309] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040501184 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198919098897 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041902583 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989176975 [INFO] [stdout] [Epoch 310] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040556684 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198919043397 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904184928 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198917750803 [INFO] [stdout] [Epoch 311] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040607877 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891899221 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041800118 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198917799964 [INFO] [stdout] [Epoch 312] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040655084 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918945003 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041754773 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198917845305 [INFO] [stdout] [Epoch 313] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904069863 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918901455 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041712962 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891788712 [INFO] [stdout] [Epoch 314] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904073879 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989188613 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041674388 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198917925697 [INFO] [stdout] [Epoch 315] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904077584 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891882424 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041638802 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198917961274 [INFO] [stdout] [Epoch 316] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040810007 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891879008 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904160599 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198917994086 [INFO] [stdout] [Epoch 317] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904084152 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891875857 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041575722 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891802436 [INFO] [stdout] [Epoch 318] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040870594 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891872948 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041547806 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918052273 [INFO] [stdout] [Epoch 319] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040897404 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918702686 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041522054 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891807803 [INFO] [stdout] [Epoch 320] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904092214 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918677945 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041498298 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891810178 [INFO] [stdout] [Epoch 321] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904094495 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918655135 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041476388 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989181237 [INFO] [stdout] [Epoch 322] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040966 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891863408 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041456173 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918143905 [INFO] [stdout] [Epoch 323] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799040985403 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891861468 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041437538 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918162546 [INFO] [stdout] [Epoch 324] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041003308 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891859678 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041420344 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891817974 [INFO] [stdout] [Epoch 325] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041019825 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918580256 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904140448 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989181956 [INFO] [stdout] [Epoch 326] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041035052 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918565024 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904138985 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891821023 [INFO] [stdout] [Epoch 327] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041049104 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891855098 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041376354 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891822373 [INFO] [stdout] [Epoch 328] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904106207 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891853802 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904136391 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891823617 [INFO] [stdout] [Epoch 329] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041074015 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891852607 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041352434 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918247645 [INFO] [stdout] [Epoch 330] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041085037 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891851505 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041341845 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891825823 [INFO] [stdout] [Epoch 331] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904109521 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891850488 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041332078 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918268006 [INFO] [stdout] [Epoch 332] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041104593 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891849549 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041323063 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891827702 [INFO] [stdout] [Epoch 333] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041113253 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918486825 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904131475 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918285337 [INFO] [stdout] [Epoch 334] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041121238 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891847885 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904130708 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918293 [INFO] [stdout] [Epoch 335] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041128605 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918471477 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904130001 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918300075 [INFO] [stdout] [Epoch 336] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904113539 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918464693 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041293492 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918306586 [INFO] [stdout] [Epoch 337] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041141647 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891845844 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041287478 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989183126 [INFO] [stdout] [Epoch 338] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041147423 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918452664 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041281927 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891831815 [INFO] [stdout] [Epoch 339] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041152752 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891844733 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904127681 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891832327 [INFO] [stdout] [Epoch 340] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041157668 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891844241 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041272093 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891832799 [INFO] [stdout] [Epoch 341] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041162203 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891843787 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904126774 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918332343 [INFO] [stdout] [Epoch 342] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041166383 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989184337 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041263724 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891833636 [INFO] [stdout] [Epoch 343] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041170244 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918429827 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041260016 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918340065 [INFO] [stdout] [Epoch 344] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041173802 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918426274 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041256597 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918343485 [INFO] [stdout] [Epoch 345] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041177083 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918423 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041253444 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891834664 [INFO] [stdout] [Epoch 346] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041180108 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918419973 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904125055 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918349535 [INFO] [stdout] [Epoch 347] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041182895 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918417187 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041247873 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891835221 [INFO] [stdout] [Epoch 348] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041185462 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918414617 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041245397 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918354687 [INFO] [stdout] [Epoch 349] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041187843 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891841224 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904124311 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891835697 [INFO] [stdout] [Epoch 350] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904119003 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918410054 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041241012 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891835907 [INFO] [stdout] [Epoch 351] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904119205 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840804 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904123907 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891836101 [INFO] [stdout] [Epoch 352] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041193916 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840617 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041237284 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918362797 [INFO] [stdout] [Epoch 353] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041195632 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840446 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904123564 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891836444 [INFO] [stdout] [Epoch 354] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041197205 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840287 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904123412 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891836596 [INFO] [stdout] [Epoch 355] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041198665 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840142 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041232721 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918367365 [INFO] [stdout] [Epoch 356] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041200014 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891840006 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041231422 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918368653 [INFO] [stdout] [Epoch 357] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041201258 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839882 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041230235 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918369847 [INFO] [stdout] [Epoch 358] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041202396 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918397686 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041229135 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891837094 [INFO] [stdout] [Epoch 359] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041203456 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839662 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041228114 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891837196 [INFO] [stdout] [Epoch 360] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041204433 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839565 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041227181 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989183729 [INFO] [stdout] [Epoch 361] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041205338 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839474 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041226315 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891837376 [INFO] [stdout] [Epoch 362] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041206165 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839392 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041225516 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918374565 [INFO] [stdout] [Epoch 363] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041206937 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918393156 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041224783 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891837529 [INFO] [stdout] [Epoch 364] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904120763 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839245 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041224106 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891837597 [INFO] [stdout] [Epoch 365] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041208285 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918391796 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041223476 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891837661 [INFO] [stdout] [Epoch 366] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041208896 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918391185 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.249897990412229 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918377185 [INFO] [stdout] [Epoch 367] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041209446 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839064 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041222366 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891837772 [INFO] [stdout] [Epoch 368] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041209962 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891839012 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041221866 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891837821 [INFO] [stdout] [Epoch 369] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121044 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891838964 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904122141 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891837867 [INFO] [stdout] [Epoch 370] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121087 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918389215 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041221 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918379084 [INFO] [stdout] [Epoch 371] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041211275 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918388804 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041220612 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891837947 [INFO] [stdout] [Epoch 372] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121164 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918388443 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041220256 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891837982 [INFO] [stdout] [Epoch 373] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121198 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918388104 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041219934 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838015 [INFO] [stdout] [Epoch 374] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041212296 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891838778 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041219635 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918380444 [INFO] [stdout] [Epoch 375] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121258 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989183875 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041219357 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918380727 [INFO] [stdout] [Epoch 376] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121285 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918387233 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041219102 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918380977 [INFO] [stdout] [Epoch 377] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121309 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918386994 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121887 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918381215 [INFO] [stdout] [Epoch 378] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041213318 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918386767 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041218647 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838143 [INFO] [stdout] [Epoch 379] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041213523 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891838656 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041218447 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838164 [INFO] [stdout] [Epoch 380] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041213723 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918386356 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041218258 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918381826 [INFO] [stdout] [Epoch 381] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041213906 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891838618 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121808 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918382 [INFO] [stdout] [Epoch 382] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041214067 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918386017 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041217925 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918382154 [INFO] [stdout] [Epoch 383] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041214222 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891838586 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121778 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918382303 [INFO] [stdout] [Epoch 384] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121436 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918385723 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041217647 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918382437 [INFO] [stdout] [Epoch 385] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041214494 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891838559 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041217514 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918382564 [INFO] [stdout] [Epoch 386] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121461 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918385473 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041217403 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918382675 [INFO] [stdout] [Epoch 387] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041214722 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891838536 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041217303 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838278 [INFO] [stdout] [Epoch 388] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041214822 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918385257 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041217203 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838288 [INFO] [stdout] [Epoch 389] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041214916 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918385157 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041217114 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838297 [INFO] [stdout] [Epoch 390] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215005 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891838508 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041217026 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918383053 [INFO] [stdout] [Epoch 391] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215083 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918385 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216948 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838313 [INFO] [stdout] [Epoch 392] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215155 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891838493 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121688 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918383197 [INFO] [stdout] [Epoch 393] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215222 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891838486 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216815 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918383264 [INFO] [stdout] [Epoch 394] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215288 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384796 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121676 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918383325 [INFO] [stdout] [Epoch 395] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215344 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891838474 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216704 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838338 [INFO] [stdout] [Epoch 396] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249897990412154 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891838468 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216648 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918383436 [INFO] [stdout] [Epoch 397] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121545 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384635 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216604 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838348 [INFO] [stdout] [Epoch 398] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215494 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891838459 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121656 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918383525 [INFO] [stdout] [Epoch 399] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215538 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384546 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216515 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838357 [INFO] [stdout] [Epoch 400] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215582 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.260201989183845 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121647 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838361 [INFO] [stdout] [Epoch 401] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215616 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891838447 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216437 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838364 [INFO] [stdout] [Epoch 402] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121565 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384435 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216404 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918383675 [INFO] [stdout] [Epoch 403] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215682 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384396 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121637 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838371 [INFO] [stdout] [Epoch 404] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121571 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384363 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216348 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838373 [INFO] [stdout] [Epoch 405] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215732 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891838434 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216326 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838375 [INFO] [stdout] [Epoch 406] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215754 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891838432 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216304 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918383775 [INFO] [stdout] [Epoch 407] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215777 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384296 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216282 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918383797 [INFO] [stdout] [Epoch 408] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249897990412158 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384285 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121627 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838381 [INFO] [stdout] [Epoch 409] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121581 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384274 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121626 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838382 [INFO] [stdout] [Epoch 410] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121582 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384263 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216249 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838383 [INFO] [stdout] [Epoch 411] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215832 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891838425 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216237 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838384 [INFO] [stdout] [Epoch 412] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215843 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891838424 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216226 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838385 [INFO] [stdout] [Epoch 413] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215854 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891838423 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216215 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838387 [INFO] [stdout] [Epoch 414] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215865 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891838422 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216204 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838388 [INFO] [stdout] [Epoch 415] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215877 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891838421 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216193 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838389 [INFO] [stdout] [Epoch 416] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215888 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384196 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216182 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.260201989183839 [INFO] [stdout] [Epoch 417] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.249897990412159 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384185 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121617 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918383913 [INFO] [stdout] [Epoch 418] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121591 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384174 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.2498979904121616 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918383924 [INFO] [stdout] [Epoch 419] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121592 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384163 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216149 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918383936 [INFO] [stdout] [Epoch 420] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215932 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.2602019891838415 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216137 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.26020198918383947 [INFO] [stdout] [Epoch 421] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215943 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384135 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216126 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838396 [INFO] [stdout] [Epoch 422] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215954 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384124 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216115 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838397 [INFO] [stdout] [Epoch 423] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.24989799041215965 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 424] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 425] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 426] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 427] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 428] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 429] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 430] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 431] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 432] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 433] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 434] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 435] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 436] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 437] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 438] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 439] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 440] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 441] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 442] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 443] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 444] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 445] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 446] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 447] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 448] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 449] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 450] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 451] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 452] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 453] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 454] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 455] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 456] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 457] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 458] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 459] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 460] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 461] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 462] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 463] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 464] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 465] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 466] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 467] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 468] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 469] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 470] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 471] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 472] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 473] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 474] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 475] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 476] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 477] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 478] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 479] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 480] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 481] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 482] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 483] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 484] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 485] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 486] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 487] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 488] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 489] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 490] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 491] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 492] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 493] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 494] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 495] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 496] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 497] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 498] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 499] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 500] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 501] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 502] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 503] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 504] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 505] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 506] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 507] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 508] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 509] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 510] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 511] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 512] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 513] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 514] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 515] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 516] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 517] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 518] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 519] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 520] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 521] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 522] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 523] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 524] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 525] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 526] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 527] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 528] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 529] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 530] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 531] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 532] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 533] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 534] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 535] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 536] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 537] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 538] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 539] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 540] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 541] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 542] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 543] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 544] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 545] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 546] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 547] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 548] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 549] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 550] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 551] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 552] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 553] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 554] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 555] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 556] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 557] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 558] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 559] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 560] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 561] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 562] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 563] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 564] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 565] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 566] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 567] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 568] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 569] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 570] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 571] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 572] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 573] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 574] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 575] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 576] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 577] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 578] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 579] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 580] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 581] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 582] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 583] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 584] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 585] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 586] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 587] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 588] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 589] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 590] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 591] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 592] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 593] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 594] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 595] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 596] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 597] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 598] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 599] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 600] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 601] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 602] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 603] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 604] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 605] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 606] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 607] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 608] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 609] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 610] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 611] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 612] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 613] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 614] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 615] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 616] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 617] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 618] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 619] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 620] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 621] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 622] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 623] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 624] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 625] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 626] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 627] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 628] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 629] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 630] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 631] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 632] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 633] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 634] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 635] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 636] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 637] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 638] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 639] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 640] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 641] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 642] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 643] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 644] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 645] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 646] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 647] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 648] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 649] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 650] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 651] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 652] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 653] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 654] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 655] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 656] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 657] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 658] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 659] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 660] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 661] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 662] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 663] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 664] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 665] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 666] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 667] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 668] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 669] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 670] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 671] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 672] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 673] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 674] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 675] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 676] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 677] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 678] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 679] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 680] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 681] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 682] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 683] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 684] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 685] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 686] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 687] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 688] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 689] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 690] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 691] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 692] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 693] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 694] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 695] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 696] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 697] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 698] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 699] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 700] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 701] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 702] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 703] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 704] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 705] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 706] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 707] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 708] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 709] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 710] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 711] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 712] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 713] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 714] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 715] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 716] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 717] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 718] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 719] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 720] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 721] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 722] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 723] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 724] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 725] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 726] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 727] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 728] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 729] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 730] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 731] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 732] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 733] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 734] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 735] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 736] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 737] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 738] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 739] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 740] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 741] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 742] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 743] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 744] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 745] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 746] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 747] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 748] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 749] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 750] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 751] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 752] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 753] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 754] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 755] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 756] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 757] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 758] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 759] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 760] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 761] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 762] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 763] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 764] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 765] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 766] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 767] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 768] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 769] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 770] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 771] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 772] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 773] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 774] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 775] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 776] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 777] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 778] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 779] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 780] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 781] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 782] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 783] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 784] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 785] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 786] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 787] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 788] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 789] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 790] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 791] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 792] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 793] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 794] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 795] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 796] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 797] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 798] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 799] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 800] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 801] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 802] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 803] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 804] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 805] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 806] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 807] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 808] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 809] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 810] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 811] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 812] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 813] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 814] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 815] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 816] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 817] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 818] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 819] [INFO] [stderr] error: test failed, to rerun pass `--lib` [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 820] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 821] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 822] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 823] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 824] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 825] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 826] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 827] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 828] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 829] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 830] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 831] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 832] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 833] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 834] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 835] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 836] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 837] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 838] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 839] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 840] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 841] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 842] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 843] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 844] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 845] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 846] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 847] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 848] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 849] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 850] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 851] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 852] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 853] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 854] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 855] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 856] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 857] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 858] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 859] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 860] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 861] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 862] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 863] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 864] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 865] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 866] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 867] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 868] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 869] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 870] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 871] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 872] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 873] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 874] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 875] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 876] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 877] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 878] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 879] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 880] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 881] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 882] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 883] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 884] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 885] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 886] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 887] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 888] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 889] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 890] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 891] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 892] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 893] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 894] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 895] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 896] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 897] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 898] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 899] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 900] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 901] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 902] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 903] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 904] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 905] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 906] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 907] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 908] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 909] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 910] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 911] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 912] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 913] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 914] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 915] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 916] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 917] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 918] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 919] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 920] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 921] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 922] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 923] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 924] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 925] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 926] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 927] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 928] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 929] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 930] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 931] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 932] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 933] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 934] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 935] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 936] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 937] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 938] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 939] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 940] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 941] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 942] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 943] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 944] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 945] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 946] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 947] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 948] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 949] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 950] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 951] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 952] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 953] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 954] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 955] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 956] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 957] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 958] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 959] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 960] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 961] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 962] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 963] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 964] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 965] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 966] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 967] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 968] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 969] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 970] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 971] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 972] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 973] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 974] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 975] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 976] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 977] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 978] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 979] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 980] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 981] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 982] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 983] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 984] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 985] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 986] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 987] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 988] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 989] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 990] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 991] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 992] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 993] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 994] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 995] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 996] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 997] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 998] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [Epoch 999] [INFO] [stdout] Trainning batch 0 ... Ok, Mean loss (MeanSquare): 0.2498979904121597 [INFO] [stdout] Trainning batch 1 ... Ok, Mean loss (MeanSquare): 0.26020198918384113 [INFO] [stdout] Trainning batch 2 ... Ok, Mean loss (MeanSquare): 0.24989799041216104 [INFO] [stdout] Trainning batch 3 ... Ok, Mean loss (MeanSquare): 0.2602019891838398 [INFO] [stdout] [INFO] [stdout] thread 'models::sequential::test_sequential_xor1' (23) panicked at src/models/sequential.rs:242:5: [INFO] [stdout] assertion `left == right` failed [INFO] [stdout] left: [0.0, 0.0, 0.0, 0.0] [INFO] [stdout] right: [0.0, 1.0, 1.0, 0.0] [INFO] [stdout] stack backtrace: [INFO] [stdout] 0: 0x5cbac21afde2 - std::backtrace_rs::backtrace::libunwind::trace::h9ea1e07ac77a25d7 [INFO] [stdout] at /rustc/c2e32f1c9652b13ed99608599c1e855462f421f3/library/std/src/../../backtrace/src/backtrace/libunwind.rs:117:9 [INFO] [stdout] 1: 0x5cbac21afde2 - std::backtrace_rs::backtrace::trace_unsynchronized::h72b75eb83b53d15a [INFO] [stdout] at /rustc/c2e32f1c9652b13ed99608599c1e855462f421f3/library/std/src/../../backtrace/src/backtrace/mod.rs:66:14 [INFO] [stdout] 2: 0x5cbac21afde2 - std::sys::backtrace::_print_fmt::h255777b7ec902439 [INFO] [stdout] at /rustc/c2e32f1c9652b13ed99608599c1e855462f421f3/library/std/src/sys/backtrace.rs:66:9 [INFO] [stdout] 3: 0x5cbac21afde2 - ::fmt::h8a1ac49fcd7ee8ce [INFO] [stdout] at /rustc/c2e32f1c9652b13ed99608599c1e855462f421f3/library/std/src/sys/backtrace.rs:39:26 [INFO] [stdout] 4: 0x5cbac21c092f - core::fmt::rt::Argument::fmt::h635cbf3c6754cc90 [INFO] [stdout] at /rustc/c2e32f1c9652b13ed99608599c1e855462f421f3/library/core/src/fmt/rt.rs:173:76 [INFO] [stdout] 5: 0x5cbac21c092f - core::fmt::write::hbdcc0ec4f1bab1db [INFO] [stdout] at /rustc/c2e32f1c9652b13ed99608599c1e855462f421f3/library/core/src/fmt/mod.rs:1469:25 [INFO] [stdout] 6: 0x5cbac217d4e3 - std::io::default_write_fmt::h2580ece0d4b58e51 [INFO] [stdout] at /rustc/c2e32f1c9652b13ed99608599c1e855462f421f3/library/std/src/io/mod.rs:639:11 [INFO] [stdout] 7: 0x5cbac217d4e3 - std::io::Write::write_fmt::hac9e42858f273b87 [INFO] [stdout] at /rustc/c2e32f1c9652b13ed99608599c1e855462f421f3/library/std/src/io/mod.rs:1954:13 [INFO] [stdout] 8: 0x5cbac2189492 - std::sys::backtrace::BacktraceLock::print::h052febbd86f7bf07 [INFO] [stdout] at /rustc/c2e32f1c9652b13ed99608599c1e855462f421f3/library/std/src/sys/backtrace.rs:42:9 [INFO] [stdout] 9: 0x5cbac218dfbf - std::panicking::default_hook::{{closure}}::h66aeee20dd470abb [INFO] [stdout] at /rustc/c2e32f1c9652b13ed99608599c1e855462f421f3/library/std/src/panicking.rs:301:27 [INFO] [stdout] 10: 0x5cbac218de51 - std::panicking::default_hook::h9d66b9642014ce48 [INFO] [stdout] at /rustc/c2e32f1c9652b13ed99608599c1e855462f421f3/library/std/src/panicking.rs:325:9 [INFO] [stdout] 11: 0x5cbac20f1a1e - as core::ops::function::Fn>::call::h8c698701e444e564 [INFO] [stdout] at /rustc/c2e32f1c9652b13ed99608599c1e855462f421f3/library/alloc/src/boxed.rs:2099:9 [INFO] [stdout] 12: 0x5cbac20f1a1e - test::test_main_with_exit_callback::{{closure}}::ha1c98e7b69c4defb [INFO] [stdout] at /rustc/c2e32f1c9652b13ed99608599c1e855462f421f3/library/test/src/lib.rs:145:21 [INFO] [stdout] 13: 0x5cbac218e67f - as core::ops::function::Fn>::call::hc4f222a8a57c637a [INFO] [stdout] at /rustc/c2e32f1c9652b13ed99608599c1e855462f421f3/library/alloc/src/boxed.rs:2099:9 [INFO] [stdout] 14: 0x5cbac218e67f - std::panicking::panic_with_hook::h6d5921a1a1fa5a8e [INFO] [stdout] at /rustc/c2e32f1c9652b13ed99608599c1e855462f421f3/library/std/src/panicking.rs:842:13 [INFO] [stdout] 15: 0x5cbac218e42a - std::panicking::panic_handler::{{closure}}::hafeef6f707d43542 [INFO] [stdout] at /rustc/c2e32f1c9652b13ed99608599c1e855462f421f3/library/std/src/panicking.rs:707:13 [INFO] [stdout] 16: 0x5cbac21895c9 - std::sys::backtrace::__rust_end_short_backtrace::h08cbc3319a3d0120 [INFO] [stdout] at /rustc/c2e32f1c9652b13ed99608599c1e855462f421f3/library/std/src/sys/backtrace.rs:174:18 [INFO] [stdout] 17: 0x5cbac2171dcd - __rustc[ce5c411ff86ab1b2]::rust_begin_unwind [INFO] [stdout] at /rustc/c2e32f1c9652b13ed99608599c1e855462f421f3/library/std/src/panicking.rs:698:5 [INFO] [stdout] 18: 0x5cbac21c8410 - core::panicking::panic_fmt::h774fb860369a0f7b [INFO] [stdout] at /rustc/c2e32f1c9652b13ed99608599c1e855462f421f3/library/core/src/panicking.rs:80:14 [INFO] [stdout] 19: 0x5cbac21c8213 - core::panicking::assert_failed_inner::hbdde3a71af798a31 [INFO] [stdout] at /rustc/c2e32f1c9652b13ed99608599c1e855462f421f3/library/core/src/panicking.rs:444:17 [INFO] [stdout] 20: 0x5cbac20c0e1e - core::panicking::assert_failed::ha378c6a33245b5b3 [INFO] [stdout] at /rustc/c2e32f1c9652b13ed99608599c1e855462f421f3/library/core/src/panicking.rs:399:5 [INFO] [stdout] 21: 0x5cbac20a35ff - easynn::models::sequential::test_sequential_xor1::hdfe6661e197f4e3a [INFO] [stdout] at /opt/rustwide/workdir/src/models/sequential.rs:242:5 [INFO] [stdout] 22: 0x5cbac20a39d7 - easynn::models::sequential::test_sequential_xor1::{{closure}}::h7446c7021e898b9d [INFO] [stdout] at /opt/rustwide/workdir/src/models/sequential.rs:205:26 [INFO] [stdout] 23: 0x5cbac20b95d6 - core::ops::function::FnOnce::call_once::h0c3d6d48a79b3246 [INFO] [stdout] at /rustc/c2e32f1c9652b13ed99608599c1e855462f421f3/library/core/src/ops/function.rs:250:5 [INFO] [stdout] 24: 0x5cbac20f186b - core::ops::function::FnOnce::call_once::h4f6eabe90cccb47d [INFO] [stdout] at /rustc/c2e32f1c9652b13ed99608599c1e855462f421f3/library/core/src/ops/function.rs:250:5 [INFO] [stdout] 25: 0x5cbac20f186b - test::__rust_begin_short_backtrace::h94208530f2b4c8c7 [INFO] [stdout] at /rustc/c2e32f1c9652b13ed99608599c1e855462f421f3/library/test/src/lib.rs:663:18 [INFO] [stdout] 26: 0x5cbac210587d - test::run_test_in_process::{{closure}}::hbdc6a45a78c46404 [INFO] [stdout] at /rustc/c2e32f1c9652b13ed99608599c1e855462f421f3/library/test/src/lib.rs:686:74 [INFO] [stdout] 27: 0x5cbac210587d - as core::ops::function::FnOnce<()>>::call_once::h7f9d8efc7eedd165 [INFO] [stdout] at /rustc/c2e32f1c9652b13ed99608599c1e855462f421f3/library/core/src/panic/unwind_safe.rs:274:9 [INFO] [stdout] 28: 0x5cbac210587d - std::panicking::catch_unwind::do_call::he5edbaf126c13b99 [INFO] [stdout] at /rustc/c2e32f1c9652b13ed99608599c1e855462f421f3/library/std/src/panicking.rs:590:40 [INFO] [stdout] 29: 0x5cbac210587d - std::panicking::catch_unwind::h3d6a8652eceeabce [INFO] [stdout] at /rustc/c2e32f1c9652b13ed99608599c1e855462f421f3/library/std/src/panicking.rs:553:19 [INFO] [stdout] 30: 0x5cbac210587d - std::panic::catch_unwind::hbef0c501dd7bb498 [INFO] [stdout] at /rustc/c2e32f1c9652b13ed99608599c1e855462f421f3/library/std/src/panic.rs:359:14 [INFO] [stdout] 31: 0x5cbac210587d - test::run_test_in_process::h400008a46a5006a0 [INFO] [stdout] at /rustc/c2e32f1c9652b13ed99608599c1e855462f421f3/library/test/src/lib.rs:686:27 [INFO] [stdout] 32: 0x5cbac210587d - test::run_test::{{closure}}::h9a131655b31d9427 [INFO] [stdout] at /rustc/c2e32f1c9652b13ed99608599c1e855462f421f3/library/test/src/lib.rs:607:43 [INFO] [stdout] 33: 0x5cbac20dedc4 - test::run_test::{{closure}}::he4a7177c216308c0 [INFO] [stdout] at /rustc/c2e32f1c9652b13ed99608599c1e855462f421f3/library/test/src/lib.rs:637:41 [INFO] [stdout] 34: 0x5cbac20dedc4 - std::sys::backtrace::__rust_begin_short_backtrace::hd3980e9c55d6c539 [INFO] [stdout] at /rustc/c2e32f1c9652b13ed99608599c1e855462f421f3/library/std/src/sys/backtrace.rs:158:18 [INFO] [stdout] 35: 0x5cbac20e26fa - std::thread::Builder::spawn_unchecked_::{{closure}}::{{closure}}::h18552df67c7aa9b8 [INFO] [stdout] at /rustc/c2e32f1c9652b13ed99608599c1e855462f421f3/library/std/src/thread/mod.rs:562:17 [INFO] [stdout] 36: 0x5cbac20e26fa - as core::ops::function::FnOnce<()>>::call_once::h3d7b285234641a08 [INFO] [stdout] at /rustc/c2e32f1c9652b13ed99608599c1e855462f421f3/library/core/src/panic/unwind_safe.rs:274:9 [INFO] [stdout] 37: 0x5cbac20e26fa - std::panicking::catch_unwind::do_call::h5a2b5cf36528a5b2 [INFO] [stdout] at /rustc/c2e32f1c9652b13ed99608599c1e855462f421f3/library/std/src/panicking.rs:590:40 [INFO] [stdout] 38: 0x5cbac20e26fa - std::panicking::catch_unwind::h7e8b531bbda77d20 [INFO] [stdout] at /rustc/c2e32f1c9652b13ed99608599c1e855462f421f3/library/std/src/panicking.rs:553:19 [INFO] [stdout] 39: 0x5cbac20e26fa - std::panic::catch_unwind::hf9bbb2040b4f1e6c [INFO] [stdout] at /rustc/c2e32f1c9652b13ed99608599c1e855462f421f3/library/std/src/panic.rs:359:14 [INFO] [stdout] 40: 0x5cbac20e26fa - std::thread::Builder::spawn_unchecked_::{{closure}}::h600f92cf68549574 [INFO] [stdout] at /rustc/c2e32f1c9652b13ed99608599c1e855462f421f3/library/std/src/thread/mod.rs:560:30 [INFO] [stdout] 41: 0x5cbac20e26fa - core::ops::function::FnOnce::call_once{{vtable.shim}}::hb6607307df8a1847 [INFO] [stdout] at /rustc/c2e32f1c9652b13ed99608599c1e855462f421f3/library/core/src/ops/function.rs:250:5 [INFO] [stdout] 42: 0x5cbac21849cf - as core::ops::function::FnOnce>::call_once::h6891ad53a5ed6f52 [INFO] [stdout] at /rustc/c2e32f1c9652b13ed99608599c1e855462f421f3/library/alloc/src/boxed.rs:2085:9 [INFO] [stdout] 43: 0x5cbac21849cf - std::sys::thread::unix::Thread::new::thread_start::h7f3e77fa86df70af [INFO] [stdout] at /rustc/c2e32f1c9652b13ed99608599c1e855462f421f3/library/std/src/sys/thread/unix.rs:124:17 [INFO] [stdout] 44: 0x79cb2c3f4aa4 - [INFO] [stdout] 45: 0x79cb2c481a64 - clone [INFO] [stdout] 46: 0x0 - [INFO] [stdout] [INFO] [stdout] [INFO] [stdout] failures: [INFO] [stdout] models::sequential::test_sequential_xor1 [INFO] [stdout] [INFO] [stdout] test result: FAILED. 6 passed; 1 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.90s [INFO] [stdout] [INFO] running `Command { std: "docker" "inspect" "6b8c8393f44be39bcfd8747f3b4fc88a18869572be70e0347782458744637619", kill_on_drop: false }` [INFO] running `Command { std: "docker" "rm" "-f" "6b8c8393f44be39bcfd8747f3b4fc88a18869572be70e0347782458744637619", kill_on_drop: false }` [INFO] [stdout] 6b8c8393f44be39bcfd8747f3b4fc88a18869572be70e0347782458744637619