using NumSharp;

using System;

using Tensorflow.Keras.ArgsDefinition;

using Tensorflow.Keras.Optimizers;

namespace Tensorflow.Keras.Engine

{

/// <summary>

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

/// </summary>

public class Model : Layer

{

#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

string loss;

IOptimizer optimizer;

public Model(ModelArgs args)

: base(args)

{

}

public void compile(string optimizerName, string lossName)

{

switch (optimizerName)

{

case "rmsprop":

optimizer = new RMSprop();

break;

}

loss = lossName;

_is_compiled = true;

// Prepare list of loss functions, same size of model outputs.

}

/// <summary>

/// Generates output predictions for the input samples.

/// </summary>

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

/// <param name="batch_size">Number of samples per batch</param>

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

/// <param name="steps">

/// Total number of steps (batches of samples)

/// before declaring the prediction round finished.

/// </param>

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

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

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

/// <returns></returns>

public Tensor predict(Tensor x,

int batch_size = 32,

int verbose = 0,

int steps = -1,

int max_queue_size = 10,

int workers = 1,

bool use_multiprocessing = false)

{

throw new NotImplementedException("");

}

}

}