TensorFlow.NET/test/TensorFlowNET.Examples/LinearRegression.cs at master · uanLy/TensorFlow.NET · GitHub

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using NumSharp.Core;

using System;

using System.Collections.Generic;

using System.Text;

using Tensorflow;

namespace TensorFlowNET.Examples

{

/// <summary>

/// A linear regression learning algorithm example using TensorFlow library.

/// https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2_BasicModels/linear_regression.py

/// </summary>

public class LinearRegression : Python, IExample

{

NumPyRandom rng = np.random;

// Parameters

float learning_rate = 0.01f;

int training_epochs = 1000;

int display_step = 50;

public void Run()

{

// Training Data

var train_X = np.array(3.3f, 4.4f, 5.5f, 6.71f, 6.93f, 4.168f, 9.779f, 6.182f, 7.59f, 2.167f,

7.042f, 10.791f, 5.313f, 7.997f, 5.654f, 9.27f, 3.1f);

var train_Y = np.array(1.7f, 2.76f, 2.09f, 3.19f, 1.694f, 1.573f, 3.366f, 2.596f, 2.53f, 1.221f,

2.827f, 3.465f, 1.65f, 2.904f, 2.42f, 2.94f, 1.3f);

var n_samples = train_X.shape[0];

// tf Graph Input

var X = tf.placeholder(tf.float32);

var Y = tf.placeholder(tf.float32);

// Set model weights

// We can set a fixed init value in order to debug

// var rnd1 = rng.randn<float>();

// var rnd2 = rng.randn<float>();

var W = tf.Variable(-0.06f, name: "weight");

var b = tf.Variable(-0.73f, name: "bias");

// Construct a linear model

var pred = tf.add(tf.multiply(X, W), b);

// Mean squared error

var cost = tf.reduce_sum(tf.pow(pred - Y, 2.0f)) / (2.0f * n_samples);

// Gradient descent

// Note, minimize() knows to modify W and b because Variable objects are trainable=True by default

var optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost);

// Initialize the variables (i.e. assign their default value)

var init = tf.global_variables_initializer();

// Start training

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

{

// Run the initializer

sess.run(init);

// Fit all training data

for (int epoch = 0; epoch < training_epochs; epoch++)

{

foreach (var (x, y) in zip<float>(train_X, train_Y))

{

sess.run(optimizer,

new FeedItem(X, x),

new FeedItem(Y, y));

}

// Display logs per epoch step

if ((epoch + 1) % display_step == 0)

{

var c = sess.run(cost,

new FeedItem(X, train_X),

new FeedItem(Y, train_Y));

Console.WriteLine($"Epoch: {epoch + 1} cost={c} " + $"W={sess.run(W)} b={sess.run(b)}");

}

Console.WriteLine("Optimization Finished!");

var training_cost = sess.run(cost,

new FeedItem(X, train_X),

new FeedItem(Y, train_Y));

Console.WriteLine($"Training cost={training_cost} W={sess.run(W)} b={sess.run(b)}");

// Testing example

var test_X = np.array(6.83f, 4.668f, 8.9f, 7.91f, 5.7f, 8.7f, 3.1f, 2.1f);

var test_Y = np.array(1.84f, 2.273f, 3.2f, 2.831f, 2.92f, 3.24f, 1.35f, 1.03f);

Console.WriteLine("Testing... (Mean square loss Comparison)");

var testing_cost = sess.run(tf.reduce_sum(tf.pow(pred - Y, 2.0f)) / (2.0f * test_X.shape[0]),

new FeedItem(X, test_X),

new FeedItem(Y, test_Y));

Console.WriteLine($"Testing cost={testing_cost}");

Console.WriteLine($"Absolute mean square loss difference: {Math.Abs((float)training_cost - (float)testing_cost)}");

});

}

TensorFlow.NET/test/TensorFlowNET.Examples/LinearRegression.cs at master · uanLy/TensorFlow.NET · GitHub

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