using System.Collections.Generic;

using Tensorflow.Keras.ArgsDefinition;

using Tensorflow.Keras.Engine.DataAdapters;

using Tensorflow.Keras.Losses;

using Tensorflow.Keras.Optimizers;

using static Tensorflow.Binding;

using static Tensorflow.KerasApi;

namespace Tensorflow.Keras.Engine

{

/// <summary>

/// `Model` groups layers into an object with training and inference features.

/// </summary>

public partial class Model : Layer, IModel

{

#pragma warning disable CS0169 // The field 'Model._cloning' is never used

bool _cloning;

#pragma warning restore CS0169 // The field 'Model._cloning' is never used

#pragma warning disable CS0108 // Member hides inherited member; missing new keyword

#pragma warning disable CS0414 // The field 'Model._is_compiled' is assigned but its value is never used

bool _is_compiled;

#pragma warning restore CS0414 // The field 'Model._is_compiled' is assigned but its value is never used

#pragma warning restore CS0108 // Member hides inherited member; missing new keyword

ILossFunc loss;

OptimizerV2 optimizer;

IVariableV1 _steps_per_execution;

protected bool _is_graph_network;

protected Tensors inputs;

protected Tensors outputs;

public string[] output_names;

IVariableV1 _train_counter;

IVariableV1 _test_counter;

IVariableV1 _predict_counter;

bool _base_model_initialized;

bool stop_training;

DataHandler data_handler;

public Model(ModelArgs args)

: base(args)

{

_init_batch_counters();

}

void _configure_steps_per_execution(int steps_per_execution)

{

_steps_per_execution = tf.Variable(steps_per_execution,

dtype: TF_DataType.TF_INT64,

aggregation: VariableAggregation.OnlyFirstReplica);

}

void _reset_compile_cache()

{

// Used to cache `trainable` attr of `Layer`s for `fit`.

_compiled_trainable_state = _get_trainable_state();

keras.backend._GRAPH = null;

}

void _init_batch_counters()

{

_train_counter = tf.Variable(0L,

dtype: TF_DataType.TF_INT64,

aggregation: VariableAggregation.OnlyFirstReplica);

_test_counter = tf.Variable(0L,

dtype: TF_DataType.TF_INT64,

aggregation: VariableAggregation.OnlyFirstReplica);

_predict_counter = tf.Variable(0L,

dtype: TF_DataType.TF_INT64,

aggregation: VariableAggregation.OnlyFirstReplica);

}

public override List<IVariableV1> trainable_variables

{

get

{

var variables = new List<IVariableV1>();

if (!Trainable)

{

return variables;

}

foreach (var trackable_obj in _self_tracked_trackables)

{

if (trackable_obj.Trainable)

variables.AddRange(trackable_obj.trainable_variables);

}

foreach (var layer in _layers)

{

if (layer.Trainable)

variables.AddRange(layer.trainable_variables);

}

// variables.AddRange(_trainable_weights);

return variables;

}

}

}

}