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

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using NumSharp;
using System;
using System.Diagnostics;
using Tensorflow;
using Tensorflow.Hub;
using static Tensorflow.Binding;

namespace TensorFlowNET.Examples
{
    /// <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-tf.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 n_classes = 10; // Number of classes, one class per digit
        int n_channels = 1;

        // Hyper-parameters
        int epochs = 5; // accuracy > 98%
        int batch_size = 100;
        float learning_rate = 0.001f;
        Datasets<MnistDataSet> 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, cls_prediction;
        Operation optimizer;

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

        NDArray x_train, y_train;
        NDArray x_valid, y_valid;
        NDArray x_test, y_test;

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

            using (var sess = tf.Session())
            {
                Train(sess);
                Test(sess);
            }

            return loss_test < 0.05 && accuracy_test > 0.98;
        }

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

            tf_with(tf.name_scope("Input"), delegate
            {
                // Placeholders for inputs (x) and outputs(y)
                x = tf.placeholder(tf.float32, shape: (-1, img_h, img_w, n_channels), name: "X");
                y = tf.placeholder(tf.float32, shape: (-1, n_classes), name: "Y");
            });

            var conv1 = conv_layer(x, filter_size1, num_filters1, stride1, name: "conv1");
            var pool1 = max_pool(conv1, ksize: 2, stride: 2, name: "pool1");
            var conv2 = conv_layer(pool1, filter_size2, num_filters2, stride2, name: "conv2");
            var pool2 = max_pool(conv2, ksize: 2, stride: 2, name: "pool2");
            var layer_flat = flatten_layer(pool2);
            var fc1 = fc_layer(layer_flat, h1, "FC1", use_relu: true);
            var output_logits = fc_layer(fc1, n_classes, "OUT", use_relu: false);

            tf_with(tf.variable_scope("Train"), delegate
            {
                tf_with(tf.variable_scope("Loss"), delegate
                {
                    loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels: y, logits: output_logits), name: "loss");
                });

                tf_with(tf.variable_scope("Optimizer"), delegate
                {
                    optimizer = tf.train.AdamOptimizer(learning_rate: learning_rate, name: "Adam-op").minimize(loss);
                });

                tf_with(tf.variable_scope("Accuracy"), delegate
                {
                    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");
                });

                tf_with(tf.variable_scope("Prediction"), delegate
                {
                    cls_prediction = tf.argmax(output_logits, axis: 1, name: "predictions");
                });
            });

            return graph;
        }

        public void Train(Session sess)
        {
            // Number of training iterations in each epoch
            var num_tr_iter = y_train.shape[0] / batch_size;

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

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

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

                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, (x, x_batch), (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")} {sw.ElapsedMilliseconds}ms");
                        sw.Restart();
                    }
                }

                // Run validation after every epoch
                (loss_val, accuracy_val) = sess.run((loss, accuracy), (x, x_valid), (y, y_valid));
                print("---------------------------------------------------------");
                print($"Epoch: {epoch + 1}, validation loss: {loss_val.ToString("0.0000")}, validation accuracy: {accuracy_val.ToString("P")}");
                print("---------------------------------------------------------");
            }
        }

        public void Test(Session sess)
        {
            (loss_test, accuracy_test) = sess.run((loss, accuracy), (x, x_test), (y, y_test));
            print("---------------------------------------------------------");
            print($"Test loss: {loss_test.ToString("0.0000")}, test accuracy: {accuracy_test.ToString("P")}");
            print("---------------------------------------------------------");
        }

        /// <summary>
        /// Create a 2D convolution layer
        /// </summary>
        /// <param name="x">input from previous layer</param>
        /// <param name="filter_size">size of each filter</param>
        /// <param name="num_filters">number of filters(or output feature maps)</param>
        /// <param name="stride">filter stride</param>
        /// <param name="name">layer name</param>
        /// <returns>The output array</returns>
        private Tensor conv_layer(Tensor x, int filter_size, int num_filters, int stride, string name)
        {
            return tf_with(tf.variable_scope(name), delegate {

                var num_in_channel = x.shape[x.NDims - 1];
                var shape = new[] { filter_size, filter_size, num_in_channel, num_filters };
                var W = weight_variable("W", shape);
                // var tf.summary.histogram("weight", W);
                var b = bias_variable("b", new[] { num_filters });
                // tf.summary.histogram("bias", b);
                var layer = tf.nn.conv2d(x, W,
                                     strides: new[] { 1, stride, stride, 1 },
                                     padding: "SAME");
                layer += b;
                return tf.nn.relu(layer);
            });
        }

        /// <summary>
        /// Create a max pooling layer
        /// </summary>
        /// <param name="x">input to max-pooling layer</param>
        /// <param name="ksize">size of the max-pooling filter</param>
        /// <param name="stride">stride of the max-pooling filter</param>
        /// <param name="name">layer name</param>
        /// <returns>The output array</returns>
        private Tensor max_pool(Tensor x, int ksize, int stride, string name)
        {
            return tf.nn.max_pool(x,
                ksize: new[] { 1, ksize, ksize, 1 },
                strides: new[] { 1, stride, stride, 1 },
                padding: "SAME",
                name: name);
        }

        /// <summary>
        /// Flattens the output of the convolutional layer to be fed into fully-connected layer
        /// </summary>
        /// <param name="layer">input array</param>
        /// <returns>flattened array</returns>
        private Tensor flatten_layer(Tensor layer)
        {
            return tf_with(tf.variable_scope("Flatten_layer"), delegate
            {
                var layer_shape = layer.TensorShape;
                var num_features = layer_shape[new Slice(1, 4)].size;
                var layer_flat = tf.reshape(layer, new[] { -1, num_features });

                return layer_flat;
            });
        }

        /// <summary>
        /// Create a weight variable with appropriate initialization
        /// </summary>
        /// <param name="name"></param>
        /// <param name="shape"></param>
        /// <returns></returns>
        private RefVariable weight_variable(string name, int[] shape)
        {
            var initer = tf.truncated_normal_initializer(stddev: 0.01f);
            return tf.get_variable(name,
                                   dtype: tf.float32,
                                   shape: shape,
                                   initializer: initer);
        }

        /// <summary>
        /// Create a bias variable with appropriate initialization
        /// </summary>
        /// <param name="name"></param>
        /// <param name="shape"></param>
        /// <returns></returns>
        private RefVariable bias_variable(string name, int[] shape)
        {
            var initial = tf.constant(0f, shape: shape, dtype: tf.float32);
            return tf.get_variable(name,
                           dtype: tf.float32,
                           initializer: initial);
        }

        /// <summary>
        /// Create a fully-connected layer
        /// </summary>
        /// <param name="x">input from previous layer</param>
        /// <param name="num_units">number of hidden units in the fully-connected layer</param>
        /// <param name="name">layer name</param>
        /// <param name="use_relu">boolean to add ReLU non-linearity (or not)</param>
        /// <returns>The output array</returns>
        private Tensor fc_layer(Tensor x, int num_units, string name, bool use_relu = true)
        {
            return tf_with(tf.variable_scope(name), delegate
            {
                var in_dim = x.shape[1];

                var W = weight_variable("W_" + name, shape: new[] { in_dim, num_units });
                var b = bias_variable("b_" + name, new[] { num_units });

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

                return layer;
            });
        } 
            
        public void PrepareData()
        {
            mnist = MnistModelLoader.LoadAsync(".resources/mnist", oneHot: true, showProgressInConsole: true).Result;
            (x_train, y_train) = Reformat(mnist.Train.Data, mnist.Train.Labels);
            (x_valid, y_valid) = Reformat(mnist.Validation.Data, mnist.Validation.Labels);
            (x_test, y_test) = Reformat(mnist.Test.Data, mnist.Test.Labels);

            print("Size of:");
            print($"- Training-set:\t\t{len(mnist.Train.Data)}");
            print($"- Validation-set:\t{len(mnist.Validation.Data)}");
        }

        /// <summary>
        /// Reformats the data to the format acceptable for convolutional layers
        /// </summary>
        /// <param name="x"></param>
        /// <param name="y"></param>
        /// <returns></returns>
        private (NDArray, NDArray) Reformat(NDArray x, NDArray y)
        {
            var (img_size, num_ch, num_class) = (np.sqrt(x.shape[1]).astype(np.int32), 1, len(np.unique(np.argmax(y, 1))));
            var dataset = x.reshape(x.shape[0], img_size, img_size, num_ch).astype(np.float32);
            //y[0] = np.arange(num_class) == y[0];
            //var labels = (np.arange(num_class) == y.reshape(y.shape[0], 1, y.shape[1])).astype(np.float32);
            return (dataset, y);
        }

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

        public void Predict(Session sess) => throw new NotImplementedException();
    }
}