using System;

using System.Collections.Generic;

using System.Text;

using System.Threading;

using Tensorflow.Framework.Models;

using Tensorflow.Keras.Engine;

using Tensorflow.Keras.Layers;

using Tensorflow.Keras.Losses;

using Tensorflow.Keras.Metrics;

using Tensorflow.Keras.Models;

namespace Tensorflow.Keras

{

public interface IKerasApi

{

IInitializersApi initializers { get; }

ILayersApi layers { get; }

ILossesApi losses { get; }

IActivationsApi activations { get; }

IOptimizerApi optimizers { get; }

IMetricsApi metrics { get; }

IModelsApi models { get; }

/// <summary>

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

/// </summary>

/// <param name="input"></param>

/// <param name="output"></param>

/// <returns></returns>

IModel Model(Tensors inputs, Tensors outputs, string name = null);

/// <summary>

/// Instantiate a Keras tensor.

/// </summary>

/// <param name="shape"></param>

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

/// <param name="dtype"></param>

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

/// <param name="sparse">

/// A boolean specifying whether the placeholder to be created is sparse.

/// </param>

/// <param name="ragged">

/// A boolean specifying whether the placeholder to be created is ragged.

/// </param>

/// <param name="tensor">

/// Optional existing tensor to wrap into the `Input` layer.

/// If set, the layer will not create a placeholder tensor.

/// </param>

/// <returns></returns>

Tensors Input(Shape shape = null,

int batch_size = -1,

string name = null,

TF_DataType dtype = TF_DataType.DtInvalid,

bool sparse = false,

Tensor tensor = null,

bool ragged = false,

TypeSpec type_spec = null,

Shape batch_input_shape = null,

Shape batch_shape = null);

}

}