TensorFlow.NET/test/TensorFlowNET.Examples/Keras.cs at master · siayou/TensorFlow.NET · GitHub

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using System;

using System.Collections.Generic;

using Tensorflow;

using Keras.Layers;

using NumSharp;

using Keras;

namespace TensorFlowNET.Examples

{

public class Keras : IExample

{

public bool Enabled { get; set; } = true;

public bool IsImportingGraph { get; set; } = false;

public string Name => "Keras";

public bool Run()

{

Console.WriteLine("================================== Keras ==================================");

#region data

var batch_size = 1000;

var (X, Y) = XOR(batch_size);

//var (X, Y, batch_size) = (np.array(new float[,]{{1, 0 },{1, 1 },{0, 0 },{0, 1 }}), np.array(new int[] { 0, 1, 1, 0 }), 4);

#endregion

#region features

var (features, labels) = (new Tensor(X), new Tensor(Y));

var num_steps = 10000;

#endregion

#region model

var m = new Model();

//m.Add(new Dense(8, name: "Hidden", activation: tf.nn.relu())).Add(new Dense(1, name:"Output"));

m.Add(

new ILayer[] {

new Dense(8, name: "Hidden_1", activation: tf.nn.relu()),

new Dense(1, name: "Output")

});

m.train(num_steps, (X, Y));

#endregion

return true;

}

static (NDArray, NDArray) XOR(int samples)

{

var X = new List<float[]>();

var Y = new List<float>();

var r = new Random();

for (int i = 0; i < samples; i++)

{

var x1 = (float)r.Next(0, 2);

var x2 = (float)r.Next(0, 2);

var y = 0.0f;

if (x1 == x2)

y = 1.0f;

X.Add(new float[] { x1, x2 });

Y.Add(y);

}

return (np.array(X.ToArray()), np.array(Y.ToArray()));

}

public Graph BuildGraph()

{