TensorFlow.NET/test/TensorFlowNET.Examples/ImageProcess/DigitRecognitionCNN.cs

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   you may not use this file except in compliance with the License.
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using System;
using System.Collections.Generic;
using System.Text;
using Tensorflow;
using TensorFlowNET.Examples.Utility;
using static Tensorflow.Python;

namespace TensorFlowNET.Examples.ImageProcess
{
    /// <summary>
    /// Convolutional Neural Network classifier for Hand Written Digits
    /// CNN architecture with two convolutional layers, followed by two fully-connected layers at the end.
    /// Use Stochastic Gradient Descent (SGD) optimizer. 
    /// http://www.easy-tensorflow.com/tf-tutorials/convolutional-neural-nets-cnns/cnn1
    /// </summary>
    public class DigitRecognitionCNN : IExample
    {
        public bool Enabled { get; set; } = true;
        public bool IsImportingGraph { get; set; } = false;

        public string Name => "MNIST CNN";

        string logs_path = "logs";

        const int img_h = 28, img_w = 28; // MNIST images are 28x28
        int img_size_flat = img_h * img_w; // 784, the total number of pixels
        int n_classes = 10; // Number of classes, one class per digit
        int n_channels = 1;

        // Hyper-parameters
        int epochs = 10;
        int batch_size = 100;
        float learning_rate = 0.001f;
        Datasets<DataSetMnist> mnist;

        // Network configuration
        // 1st Convolutional Layer
        int filter_size1 = 5;  // Convolution filters are 5 x 5 pixels.
        int num_filters1 = 16; //  There are 16 of these filters.
        int stride1 = 1;  // The stride of the sliding window

        // 2nd Convolutional Layer
        int filter_size2 = 5; // Convolution filters are 5 x 5 pixels.
        int num_filters2 = 32;// There are 32 of these filters.
        int stride2 = 1;  // The stride of the sliding window

        // Fully-connected layer.
        int h1 = 128; // Number of neurons in fully-connected layer.


        Tensor x, y;
        Tensor loss, accuracy;
        Operation optimizer;

        int display_freq = 100;
        float accuracy_test = 0f;
        float loss_test = 1f;

        public bool Run()
        {
            PrepareData();
            BuildGraph();

            with(tf.Session(), sess =>
            {
                Train(sess);
                Test(sess);
            });

            return loss_test < 0.09 && accuracy_test > 0.95;
        }

        public Graph BuildGraph()
        {
            var graph = new Graph().as_default();

            // Placeholders for inputs (x) and outputs(y)
            x = tf.placeholder(tf.float32, shape: (-1, img_size_flat), name: "X");
            y = tf.placeholder(tf.float32, shape: (-1, n_classes), name: "Y");

            // Create a fully-connected layer with h1 nodes as hidden layer
            var fc1 = fc_layer(x, h1, "FC1", use_relu: true);
            // Create a fully-connected layer with n_classes nodes as output layer
            var output_logits = fc_layer(fc1, n_classes, "OUT", use_relu: false);
            // Define the loss function, optimizer, and accuracy
            var logits = tf.nn.softmax_cross_entropy_with_logits(labels: y, logits: output_logits);
            loss = tf.reduce_mean(logits, name: "loss");
            optimizer = tf.train.AdamOptimizer(learning_rate: learning_rate, name: "Adam-op").minimize(loss);
            var correct_prediction = tf.equal(tf.argmax(output_logits, 1), tf.argmax(y, 1), name: "correct_pred");
            accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32), name: "accuracy");

            // Network predictions
            var cls_prediction = tf.argmax(output_logits, axis: 1, name: "predictions");

            return graph;
        }

        private Tensor fc_layer(Tensor x, int num_units, string name, bool use_relu = true)
        {
            var in_dim = x.shape[1];

            var initer = tf.truncated_normal_initializer(stddev: 0.01f);
            var W = tf.get_variable("W_" + name,
                        dtype: tf.float32,
                        shape: (in_dim, num_units),
                        initializer: initer);

            var initial = tf.constant(0f, num_units);
            var b = tf.get_variable("b_" + name,
                        dtype: tf.float32,
                        initializer: initial);

            var layer = tf.matmul(x, W) + b;
            if (use_relu)
                layer = tf.nn.relu(layer);

            return layer;
        } 

        public Graph ImportGraph() => throw new NotImplementedException();

        public void Predict(Session sess) => throw new NotImplementedException();
            
        public void PrepareData()
        {
            mnist = MNIST.read_data_sets("mnist", one_hot: true);
            print("Size of:");
            print($"- Training-set:\t\t{len(mnist.train.data)}");
            print($"- Validation-set:\t{len(mnist.validation.data)}");
        }

        public void Train(Session sess)
        {
            // Number of training iterations in each epoch
            var num_tr_iter = mnist.train.labels.len / batch_size;

            var init = tf.global_variables_initializer();
            sess.run(init);

            float loss_val = 100.0f;
            float accuracy_val = 0f;

            foreach (var epoch in range(epochs))
            {
                print($"Training epoch: {epoch + 1}");
                // Randomly shuffle the training data at the beginning of each epoch 
                var (x_train, y_train) = mnist.Randomize(mnist.train.data, mnist.train.labels);

                foreach (var iteration in range(num_tr_iter))
                {
                    var start = iteration * batch_size;
                    var end = (iteration + 1) * batch_size;
                    var (x_batch, y_batch) = mnist.GetNextBatch(x_train, y_train, start, end);

                    // Run optimization op (backprop)
                    sess.run(optimizer, new FeedItem(x, x_batch), new FeedItem(y, y_batch));

                    if (iteration % display_freq == 0)
                    {
                        // Calculate and display the batch loss and accuracy
                        var result = sess.run(new[] { loss, accuracy }, new FeedItem(x, x_batch), new FeedItem(y, y_batch));
                        loss_val = result[0];
                        accuracy_val = result[1];
                        print($"iter {iteration.ToString("000")}: Loss={loss_val.ToString("0.0000")}, Training Accuracy={accuracy_val.ToString("P")}");
                    }
                }

                // Run validation after every epoch
                var results1 = sess.run(new[] { loss, accuracy }, new FeedItem(x, mnist.validation.data), new FeedItem(y, mnist.validation.labels));
                loss_val = results1[0];
                accuracy_val = results1[1];
                print("---------------------------------------------------------");
                print($"Epoch: {epoch + 1}, validation loss: {loss_val.ToString("0.0000")}, validation accuracy: {accuracy_val.ToString("P")}");
                print("---------------------------------------------------------");
            }
        }

        public void Test(Session sess)
        {
            var result = sess.run(new[] { loss, accuracy }, new FeedItem(x, mnist.test.data), new FeedItem(y, mnist.test.labels));
            loss_test = result[0];
            accuracy_test = result[1];
            print("---------------------------------------------------------");
            print($"Test loss: {loss_test.ToString("0.0000")}, test accuracy: {accuracy_test.ToString("P")}");
            print("---------------------------------------------------------");
        }
    }
}