using System;

using System.Collections.Generic;

using System.Text;

using System.Linq;

using Tensorflow;

using static Keras.Keras;

using Keras;

using NumSharp;

using Tensorflow.Operations.Activation;

namespace Keras.Layers

{

public class Dense : ILayer

{

RefVariable W;

int units;

TensorShape WShape;

string name;

IActivation activation;

public Dense(int units, string name = null, IActivation activation = null)

{

this.activation = activation;

this.units = units;

this.name = (string.IsNullOrEmpty(name) || string.IsNullOrWhiteSpace(name))?this.GetType().Name + "_" + this.GetType().GUID:name;

}

public ILayer __build__(TensorShape input_shape, int seed = 1, float stddev = -1f)

{

Console.WriteLine("Building Layer \"" + name + "\" ...");

if (stddev == -1)

stddev = (float)(1 / Math.Sqrt(2));

var dim = input_shape.Dimensions;

var input_dim = dim[dim.Length - 1];

W = tf.Variable(create_tensor(new int[] { input_dim, units }, seed: seed, stddev: (float)stddev));

WShape = new TensorShape(W.shape);

return this;

}

public Tensor __call__(Tensor x)

{

var dot = tf.matmul(x, W);

if (this.activation != null)

dot = activation.Activate(dot);

Console.WriteLine("Calling Layer \"" + name + "(" + np.array(dot.GetShape().Dimensions).ToString() + ")\" ...");

return dot;

}

public TensorShape __shape__()

{

return WShape;

}

public TensorShape output_shape(TensorShape input_shape)

{

var output_shape = input_shape.Dimensions;

output_shape[output_shape.Length - 1] = units;

return new TensorShape(output_shape);

}

}

}