TensorFlow.NET/test/TensorFlowNET.Examples/BasicOperations.cs at master · Nucs/TensorFlow.NET · GitHub

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using NumSharp;

using System;

using System.Collections.Generic;

using System.Text;

using Tensorflow;

using static Tensorflow.Python;

namespace TensorFlowNET.Examples

{

/// <summary>

/// Basic Operations example using TensorFlow library.

/// https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/1_Introduction/basic_operations.py

/// </summary>

public class BasicOperations : IExample

{

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

public string Name => "Basic Operations";

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

private Session sess;

public bool Run()

{

// Basic constant operations

// The value returned by the constructor represents the output

// of the Constant op.

var a = tf.constant(2);

var b = tf.constant(3);

// Launch the default graph.

using (sess = tf.Session())

{

Console.WriteLine("a=2, b=3");

Console.WriteLine($"Addition with constants: {sess.run(a + b)}");

Console.WriteLine($"Multiplication with constants: {sess.run(a * b)}");

}

// Basic Operations with variable as graph input

// The value returned by the constructor represents the output

// of the Variable op. (define as input when running session)

// tf Graph input

a = tf.placeholder(tf.int16);

b = tf.placeholder(tf.int16);

// Define some operations

var add = tf.add(a, b);

var mul = tf.multiply(a, b);

// Launch the default graph.

using(sess = tf.Session())

{

var feed_dict = new FeedItem[]

{

new FeedItem(a, (short)2),

new FeedItem(b, (short)3)

};

// Run every operation with variable input

Console.WriteLine($"Addition with variables: {sess.run(add, feed_dict)}");

Console.WriteLine($"Multiplication with variables: {sess.run(mul, feed_dict)}");

}

// ----------------

// More in details:

// Matrix Multiplication from TensorFlow official tutorial

// Create a Constant op that produces a 1x2 matrix. The op is

// added as a node to the default graph.

// The value returned by the constructor represents the output

// of the Constant op.

var nd1 = np.array(3, 3).reshape(1, 2);

var matrix1 = tf.constant(nd1);

// Create another Constant that produces a 2x1 matrix.

var nd2 = np.array(2, 2).reshape(2, 1);

var matrix2 = tf.constant(nd2);

// Create a Matmul op that takes 'matrix1' and 'matrix2' as inputs.

// The returned value, 'product', represents the result of the matrix

// multiplication.

var product = tf.matmul(matrix1, matrix2);

// To run the matmul op we call the session 'run()' method, passing 'product'

// which represents the output of the matmul op. This indicates to the call

// that we want to get the output of the matmul op back.

// All inputs needed by the op are run automatically by the session. They

// typically are run in parallel.

// The call 'run(product)' thus causes the execution of threes ops in the

// graph: the two constants and matmul.

// The output of the op is returned in 'result' as a numpy `ndarray` object.

return with(tf.Session(), sess =>

{

var result = sess.run(product);

Console.WriteLine(result.ToString()); // ==> [[ 12.]]

return result.Data<int>()[0] == 12;

});

}

public void PrepareData()

{

}

public Graph ImportGraph()

{

throw new NotImplementedException();

}

public Graph BuildGraph()

{

throw new NotImplementedException();

}

public bool Train()

{

throw new NotImplementedException();

}

public bool Predict()

{

throw new NotImplementedException();

}

TensorFlow.NET/test/TensorFlowNET.Examples/BasicOperations.cs at master · Nucs/TensorFlow.NET · GitHub

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