TensorFlow.NET/src/TensorFlowNET.Keras/Engine/Model.Fit.cs at master · feelsyt/TensorFlow.NET · GitHub

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using Tensorflow.NumPy;

using System;

using System.Collections.Generic;

using System.Linq;

using Tensorflow.Keras.ArgsDefinition;

using Tensorflow.Keras.Engine.DataAdapters;

using System.Diagnostics;

using Tensorflow.Keras.Callbacks;

using Tensorflow.Util;

using OneOf;

namespace Tensorflow.Keras.Engine

{

public partial class Model

{

/// <summary>

/// Trains the model for a fixed number of epochs (iterations on a dataset).

/// </summary>

/// <param name="x"></param>

/// <param name="y"></param>

/// <param name="batch_size"></param>

/// <param name="epochs"></param>

/// <param name="verbose"></param>

/// <param name="callbacks"></param>

/// <param name="validation_split"></param>

/// <param name="validation_data"></param>

/// <param name="shuffle"></param>

/// <param name="class_weight"></param>

/// <param name="sample_weight"></param>

/// <param name="initial_epoch"></param>

/// <param name="max_queue_size"></param>

/// <param name="workers"></param>

/// <param name="use_multiprocessing"></param>

/// <returns></returns>

/// <exception cref="InvalidArgumentError"></exception>

public ICallback fit(NDArray x, NDArray y,

int batch_size = -1,

int epochs = 1,

int verbose = 1,

List<ICallback> callbacks = null,

float validation_split = 0f,

ValidationDataPack validation_data = null,

int validation_step = 10,

bool shuffle = true,

Dictionary<int, float> class_weight = null,

NDArray sample_weight = null,

int initial_epoch = 0,

int max_queue_size = 10,

int workers = 1,

bool use_multiprocessing = false)

{

if (x.dims[0] != y.dims[0])

{

throw new InvalidArgumentError(

$"The array x and y should have same value at dim 0, but got {x.dims[0]} and {y.dims[0]}");

}

// The default dtype in NDArray is double, so we need to cast sample_weight to float to mul with loss which's dtype is float.

sample_weight = sample_weight?.astype(TF_DataType.TF_FLOAT);

if (validation_split != 0f && validation_data == null)

{

((x, y, sample_weight), validation_data) = DataAdapter.train_validation_split((x, y, sample_weight), validation_split);

}

var data_handler = new DataHandler(new DataHandlerArgs

{

X = x,

Y = y,

SampleWeight = sample_weight,

BatchSize = batch_size,

InitialEpoch = initial_epoch,

Epochs = epochs,

Shuffle = shuffle,

ClassWeight = class_weight,

MaxQueueSize = max_queue_size,

Workers = workers,

UseMultiprocessing = use_multiprocessing,

Model = this,

StepsPerExecution = _steps_per_execution

});

return FitInternal(data_handler, epochs, verbose, callbackList: callbacks, validation_data: validation_data,

train_step_func: train_step_function);

}

public ICallback fit(IEnumerable<NDArray> x, NDArray y,

int batch_size = -1,

int epochs = 1,

int verbose = 1,

List<ICallback> callbacks = null,

float validation_split = 0f,

ValidationDataPack validation_data = null,

bool shuffle = true,

Dictionary<int, float> class_weight = null,

NDArray sample_weight = null,

int initial_epoch = 0,

int max_queue_size = 10,

int workers = 1,

bool use_multiprocessing = false)

{

foreach(var tx in x)

{

if (tx.dims[0] != y.dims[0])

{

throw new InvalidArgumentError(

$"The array x and y should have same value at dim 0, but got {tx.dims[0]} and {y.dims[0]}");

}

sample_weight = sample_weight?.astype(TF_DataType.TF_FLOAT);

if (validation_split != 0f && validation_data == null)

{

((x, y, sample_weight), validation_data) = DataAdapter.train_validation_split((x, y, sample_weight), validation_split);

}

var data_handler = new DataHandler(new DataHandlerArgs

{

X = new Tensors(x.ToArray()),

Y = y,

SampleWeight = sample_weight,

BatchSize = batch_size,

InitialEpoch = initial_epoch,

Epochs = epochs,

Shuffle = shuffle,

ClassWeight = class_weight,

MaxQueueSize = max_queue_size,

Workers = workers,

UseMultiprocessing = use_multiprocessing,

Model = this,

StepsPerExecution = _steps_per_execution

});

if (data_handler.DataAdapter.GetDataset().structure.Length > 2 ||

data_handler.DataAdapter.GetDataset().FirstInputTensorCount > 1)

{

return FitInternal(data_handler, epochs, verbose, callbackList: callbacks, validation_data: validation_data,

train_step_func: train_step_multi_inputs_function);

}

else

{

return FitInternal(data_handler, epochs, verbose, callbackList: callbacks, validation_data: validation_data,

train_step_func: train_step_function);

}

public ICallback fit(IDatasetV2 dataset,

int batch_size = -1,

int epochs = 1,

int verbose = 1,

List<ICallback> callbacks = null,

IDatasetV2 validation_data = null,

int validation_step = 10,

bool shuffle = true,

Dictionary<int, float> class_weight = null,

int initial_epoch = 0,

int max_queue_size = 10,

int workers = 1,

bool use_multiprocessing = false)

{

var data_handler = new DataHandler(new DataHandlerArgs

{

Dataset = dataset,

BatchSize = batch_size,

InitialEpoch = initial_epoch,

Epochs = epochs,

Shuffle = shuffle,

ClassWeight = class_weight,

MaxQueueSize = max_queue_size,

Workers = workers,

UseMultiprocessing = use_multiprocessing,

Model = this,

StepsPerExecution = _steps_per_execution

});

Func<DataHandler, OwnedIterator, Dictionary<string, float>> trainStepFunction;

if (data_handler.DataAdapter.GetDataset().structure.Length > 2 ||

data_handler.DataAdapter.GetDataset().FirstInputTensorCount > 1)

{

trainStepFunction = train_step_multi_inputs_function;

}

else

{

trainStepFunction = train_step_function;

}

return FitInternal(data_handler, epochs, validation_step, verbose, callbacks, validation_data: validation_data,

train_step_func: trainStepFunction);

}

History FitInternal(DataHandler data_handler, int epochs, int validation_step, int verbose, List<ICallback> callbackList, IDatasetV2 validation_data,

Func<DataHandler, OwnedIterator, Dictionary<string, float>> train_step_func)

{

stop_training = false;

_train_counter.assign(0);

var callbacks = new CallbackList(new CallbackParams

{

Model = this,

Verbose = verbose,

Epochs = epochs,

Steps = data_handler.Inferredsteps

});

if (callbackList != null)

{

foreach(var callback in callbackList)

callbacks.callbacks.add(callback);

}

callbacks.on_train_begin();

foreach (var (epoch, iterator) in data_handler.enumerate_epochs())

{

reset_metrics();

callbacks.on_epoch_begin(epoch);

// data_handler.catch_stop_iteration();

var logs = new Dictionary<string, float>();

long End_step = 0;

foreach (var step in data_handler.steps())

{

callbacks.on_train_batch_begin(step);

logs = train_step_func(data_handler, iterator);

var end_step = step + data_handler.StepIncrement;

End_step = end_step;

callbacks.on_train_batch_end(end_step, logs);

GC.Collect();

}

if (validation_data != null)

{

if (validation_step > 0 && epoch ==0 || (epoch) % validation_step != 0)

continue;

var val_logs = evaluate(validation_data);

foreach(var log in val_logs)

{

logs["val_" + log.Key] = log.Value;

}

callbacks.on_train_batch_end(End_step, logs);

}

GC.Collect();

callbacks.on_epoch_end(epoch, logs);

if (stop_training)

{

break;

}

return callbacks.History;

}

History FitInternal(DataHandler data_handler, int epochs, int verbose, List<ICallback> callbackList, ValidationDataPack validation_data,

Func<DataHandler, OwnedIterator, Dictionary<string, float>> train_step_func)

{

stop_training = false;

_train_counter.assign(0);

var callbacks = new CallbackList(new CallbackParams

{

Model = this,

Verbose = verbose,

Epochs = epochs,

Steps = data_handler.Inferredsteps

});

if (callbackList != null)

{

foreach (var callback in callbackList)

callbacks.callbacks.add(callback);

}

callbacks.on_train_begin();

foreach (var (epoch, iterator) in data_handler.enumerate_epochs())

{

reset_metrics();

callbacks.on_epoch_begin(epoch);

// data_handler.catch_stop_iteration();

var logs = new Dictionary<string, float>();

long End_step = 0;

foreach (var step in data_handler.steps())

{

callbacks.on_train_batch_begin(step);

logs = train_step_func(data_handler, iterator);

var end_step = step + data_handler.StepIncrement;

End_step = end_step;

callbacks.on_train_batch_end(end_step, logs);

GC.Collect();

}

if (validation_data != null)

{

NDArray val_x;

NDArray[] val_x_array;

NDArray val_y;

NDArray val_sample_weight;

Dictionary<string, float> val_logs;

if (!validation_data.val_x_is_array)

{

(val_x, val_y, val_sample_weight) = validation_data;

// Because evaluate calls call_test_batch_end, this interferes with our output on the screen

// so we need to pass a is_val parameter to stop on_test_batch_end

val_logs = evaluate(val_x, val_y, sample_weight: val_sample_weight, is_val: true);

}

else

{

(val_x_array, val_y, val_sample_weight, _) = validation_data;

val_logs = evaluate(val_x_array, val_y, sample_weight: val_sample_weight, is_val: true);

}

foreach (var log in val_logs)

{

logs["val_" + log.Key] = log.Value;

}

// because after evaluate, logs add some new log which we need to print

callbacks.on_train_batch_end(End_step, logs);

}

callbacks.on_epoch_end(epoch, logs);

GC.Collect();

if (stop_training)

{

break;

}

return callbacks.History;

}

TensorFlow.NET/src/TensorFlowNET.Keras/Engine/Model.Fit.cs at master · feelsyt/TensorFlow.NET · GitHub

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