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Fix Sequential model. · MSavameri/TensorFlow.NET@fd64ad1 · GitHub

  1. MSavameri
  2. TensorFlow.NET

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Commit fd64ad1

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Oceania2018
committed
Fix Sequential model.
1 parent 400cde2 commit fd64ad1

21 files changed

Lines changed: 240 additions & 76 deletions

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‎README.md‎

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@@ -26,12 +26,12 @@ In comparison to other projects, like for instance [TensorFlowSharp](https://www
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### How to use
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| TensorFlow | tf native1.14 | tf native 1.15 | tf native 2.3 |
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| -------------------------- | ------------- | -------------- | ------------- |
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| tf.net 0.3x, tf.keras 0.2 | | | x |
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| tf.net 0.2x | | x | x |
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| tf.net 0.15 | x | x | |
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| tf.net 0.14 | x | | |
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| TensorFlow | tf native1.14, cuda 10.0 | tf native 1.15, cuda 10.0 | tf native 2.3, cuda 10.1 | tf native 2.4, cuda 11 |
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| -------------------------- | ------------- | -------------- | ------------- | ------------- |
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| tf.net 0.3x, tf.keras 0.2 | | | x | not compatible |
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| tf.net 0.2x | | x | x | |
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| tf.net 0.15 | x | x | | |
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| tf.net 0.14 | x | | | |
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Troubleshooting of running example or installation, please refer [here](tensorflowlib/README.md).
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‎src/SciSharp.TensorFlow.Redist/README.md‎

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@@ -22,11 +22,19 @@ https://www.nuget.org/packages/SciSharp.TensorFlow.Redist
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Related merged [commits](https://github.com/SciSharp/TensorFlow.NET/commit/854a5ba61ad0e400623821236bd117cc24c6cb77).
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#### Download pre-build package
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[Mac OSX CPU](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-cpu-darwin-x86_64-2.4.0.tar.gz), [Linux CPU](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-cpu-linux-x86_64-2.4.0.tar.gz), [Linux GPU](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-gpu-linux-x86_64-2.4.0.tar.gz), [Windows CPU](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-cpu-windows-x86_64-2.4.0.tar.gz), [Windows GPU](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-gpu-windows-x86_64-2.4.0.zip)
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#### Pack and Deploy ####
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On Windows, the tar command does not support extracting archives with symlinks. So when `dotnet pack` runs on Windows it will only package the Windows binaries.
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1. Run `dotnet pack SciSharp.TensorFlow.Redist.nupkgproj` under `src/SciSharp.TensorFlow.Redist` directory in Linux.
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2. Run `dotnet nuget push SciSharp.TensorFlow.Redist.2.3.1.nupkg -k APIKEY -s https://api.nuget.org/v3/index.json -t 600`
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2. Run `dotnet nuget push SciSharp.TensorFlow.Redist.2.4.0.nupkg -k APIKEY -s https://api.nuget.org/v3/index.json -t 600`
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‎src/TensorFlowNET.Console/TensorFlowNET.Console.csproj‎

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@@ -8,7 +8,7 @@
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</PropertyGroup>
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<ItemGroup>
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<PackageReference Include="SciSharp.TensorFlow.Redist" Version="2.3.0" />
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<PackageReference Include="SciSharp.TensorFlow.Redist" Version="2.3.1" />
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</ItemGroup>
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<ItemGroup>

‎src/TensorFlowNET.Core/Tensors/tensor_util.cs‎

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@@ -574,7 +574,7 @@ public static string to_numpy_string(Tensor tensor)
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return string.Join(string.Empty, nd.ToArray<byte>()
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.Select(x => x < 32 || x > 127 ? "\\x" + x.ToString("x") : Convert.ToChar(x).ToString()));
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case TF_DataType.TF_BOOL:
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return (nd.GetByte(0) > 0).ToString();
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return nd.GetBoolean(0).ToString();
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case TF_DataType.TF_VARIANT:
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case TF_DataType.TF_RESOURCE:
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return "<unprintable>";

‎src/TensorFlowNET.Keras/Engine/DataAdapters/DataHandler.cs‎

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@@ -37,19 +37,38 @@ public DataHandler(DataHandlerArgs args)
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_steps_per_execution_value = args.StepsPerExecution.numpy();
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}
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_adapter = new TensorLikeDataAdapter(new TensorLikeDataAdapterArgs
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if(args.Dataset == null)
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{
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X = args.X,
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Y = args.Y,
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BatchSize = args.BatchSize,
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Steps = args.StepsPerEpoch,
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Epochs = args.Epochs - args.InitialEpoch,
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Shuffle = args.Shuffle,
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MaxQueueSize = args.MaxQueueSize,
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Worker = args.Workers,
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UseMultiprocessing = args.UseMultiprocessing,
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Model = args.Model
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});
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_adapter = new TensorLikeDataAdapter(new DataAdapterArgs
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{
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X = args.X,
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Y = args.Y,
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BatchSize = args.BatchSize,
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Steps = args.StepsPerEpoch,
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Epochs = args.Epochs - args.InitialEpoch,
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Shuffle = args.Shuffle,
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MaxQueueSize = args.MaxQueueSize,
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Worker = args.Workers,
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UseMultiprocessing = args.UseMultiprocessing,
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Model = args.Model
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});
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}
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else
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{
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_adapter = new DatasetAdapter(new DataAdapterArgs
59+
{
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Dataset = args.Dataset,
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BatchSize = args.BatchSize,
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Steps = args.StepsPerEpoch,
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Epochs = args.Epochs - args.InitialEpoch,
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Shuffle = args.Shuffle,
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MaxQueueSize = args.MaxQueueSize,
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Worker = args.Workers,
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UseMultiprocessing = args.UseMultiprocessing,
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Model = args.Model
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});
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}
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_dataset = _adapter.GetDataset();
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_inferred_steps = _infer_steps(args.StepsPerEpoch, _dataset);
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_current_step = 0;
@@ -66,7 +85,8 @@ int _infer_steps(int steps_per_epoch, IDatasetV2 dataset)
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if (adapter_steps > -1)
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return adapter_steps;
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69-
throw new NotImplementedException("");
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var size = dataset.dataset_cardinality();
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return size.numpy();
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}
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public IEnumerable<(int, OwnedIterator)> enumerate_epochs()

‎src/TensorFlowNET.Keras/Engine/DataAdapters/DatasetAdapter.cs‎

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@@ -0,0 +1,35 @@
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using System;
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using System.Collections.Generic;
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using System.Text;
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using Tensorflow.Keras.ArgsDefinition;
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namespace Tensorflow.Keras.Engine.DataAdapters
7+
{
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public class DatasetAdapter : IDataAdapter
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{
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DataAdapterArgs args;
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IDatasetV2 _dataset => args.Dataset;
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public DatasetAdapter(DataAdapterArgs args)
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{
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this.args = args;
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}
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public bool CanHandle(Tensor x, Tensor y = null)
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{
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throw new NotImplementedException();
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}
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public IDatasetV2 GetDataset()
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=> _dataset;
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public int GetSize()
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=> -1;
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public (Tensor, Tensor) Expand1d(Tensor x, Tensor y)
29+
{
30+
if (y.TensorShape.ndim == 1)
31+
y = array_ops.expand_dims(y, axis: -1);
32+
return (x, y);
33+
}
34+
}
35+
}

‎src/TensorFlowNET.Keras/Engine/DataAdapters/TensorLikeDataAdapter.cs‎

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Original file line numberDiff line numberDiff line change
@@ -9,14 +9,14 @@ namespace Tensorflow.Keras.Engine.DataAdapters
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/// </summary>
1010
public class TensorLikeDataAdapter : IDataAdapter
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{
12-
TensorLikeDataAdapterArgs args;
12+
DataAdapterArgs args;
1313
int _size;
1414
int _batch_size;
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int num_samples;
1616
int num_full_batches;
1717
IDatasetV2 _dataset;
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19-
public TensorLikeDataAdapter(TensorLikeDataAdapterArgs args)
19+
public TensorLikeDataAdapter(DataAdapterArgs args)
2020
{
2121
this.args = args;
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_process_tensorlike();

‎src/TensorFlowNET.Keras/Engine/Functional.cs‎

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@@ -39,10 +39,12 @@ public Functional(Tensors inputs, Tensors outputs, string name = null)
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_input_coordinates = new List<KerasHistory>();
4040
_output_coordinates = new List<KerasHistory>();
4141
tensor_usage_count = new Dictionary<int, int>();
42+
if (this is Sequential)
43+
return;
4244
_init_graph_network(inputs, outputs);
4345
}
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45-
void _init_graph_network(Tensors inputs, Tensors outputs)
47+
protected void _init_graph_network(Tensors inputs, Tensors outputs)
4648
{
4749
_is_graph_network = true;
4850
this.inputs = inputs;

‎src/TensorFlowNET.Keras/Engine/Model.Compile.cs‎

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@@ -9,10 +9,6 @@ public partial class Model
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{
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LossesContainer compiled_loss;
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MetricsContainer compiled_metrics;
12-
public void compile(string optimizerName, ILossFunc lossName)
13-
{
14-
throw new NotImplementedException("");
15-
}
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public void compile(ILossFunc loss, OptimizerV2 optimizer, string[] metrics)
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{
@@ -29,12 +25,12 @@ public void compile(ILossFunc loss, OptimizerV2 optimizer, string[] metrics)
2925
this.loss = loss;
3026
}
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public void compile(string optimizerName, string lossName)
28+
public void compile(string optimizer, string loss, string[] metrics)
3329
{
34-
switch (optimizerName)
30+
switch (optimizer)
3531
{
3632
case "rmsprop":
37-
optimizer = new RMSprop(new RMSpropArgs
33+
this.optimizer = new RMSprop(new RMSpropArgs
3834
{
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});

‎src/TensorFlowNET.Keras/Engine/Model.Fit.cs‎

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Original file line numberDiff line numberDiff line change
@@ -68,5 +68,49 @@ public void fit(NDArray x, NDArray y,
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Console.WriteLine($"epoch: {epoch + 1}, " + string.Join(", ", results.Select(x => $"{x.Item1}: {(float)x.Item2}")));
6969
}
7070
}
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public void fit(IDatasetV2 dataset,
73+
IDatasetV2 validation_data = null,
74+
int batch_size = -1,
75+
int epochs = 1,
76+
int verbose = 1,
77+
float validation_split = 0f,
78+
bool shuffle = true,
79+
int initial_epoch = 0,
80+
int max_queue_size = 10,
81+
int workers = 1,
82+
bool use_multiprocessing = false)
83+
{
84+
data_handler = new DataHandler(new DataHandlerArgs
85+
{
86+
Dataset = dataset,
87+
BatchSize = batch_size,
88+
InitialEpoch = initial_epoch,
89+
Epochs = epochs,
90+
Shuffle = shuffle,
91+
MaxQueueSize = max_queue_size,
92+
Workers = workers,
93+
UseMultiprocessing = use_multiprocessing,
94+
Model = this,
95+
StepsPerExecution = _steps_per_execution
96+
});
97+
98+
stop_training = false;
99+
_train_counter.assign(0);
100+
Console.WriteLine($"Training...");
101+
foreach (var (epoch, iterator) in data_handler.enumerate_epochs())
102+
{
103+
// reset_metrics();
104+
// callbacks.on_epoch_begin(epoch)
105+
// data_handler.catch_stop_iteration();
106+
IEnumerable<(string, Tensor)> results = null;
107+
foreach (var step in data_handler.steps())
108+
{
109+
// callbacks.on_train_batch_begin(step)
110+
results = step_function(iterator);
111+
}
112+
Console.WriteLine($"epoch: {epoch + 1}, " + string.Join(", ", results.Select(x => $"{x.Item1}: {(float)x.Item2}")));
113+
}
114+
}
71115
}
72116
}

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