DigitRecognitionRNN.cs

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   Licensed under the Apache License, Version 2.0 (the "License");
   you may not use this file except in compliance with the License.
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   distributed under the License is distributed on an "AS IS" BASIS,
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using Tensorflow;
using Tensorflow.NumPy;
using static Tensorflow.Binding;
using static Tensorflow.KerasApi;

namespace TensorFlowNET.Examples;

/// <summary>
/// Recurrent Neural Network for handwritten digits MNIST.
/// https://medium.com/machine-learning-algorithms/mnist-using-recurrent-neural-network-2d070a5915a2
/// </summary>
public class DigitRecognitionRNN : SciSharpExample, IExample
{
    // Hyper-parameters
    int n_neurons = 128;
    float learning_rate = 0.001f;
    int batch_size = 128;
    int n_epochs = 5;

    int n_steps = 28;
    int n_inputs = 28;
    int n_outputs = 10;

    Datasets<MnistDataSet> mnist;

    Tensor X, y;
    Tensor loss, accuracy, 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;

    Session sess;

    public ExampleConfig InitConfig()
        => Config = new ExampleConfig
        {
            Name = "MNIST RNN (Graph)",
            Enabled = false,
            IsImportingGraph = false
        };

    public bool Run()
    {
        tf.compat.v1.disable_eager_execution();

        PrepareData();
        BuildGraph();

        sess = tf.Session();

        Train();
        Test();

        return accuracy_test > 0.95;
    }

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

        X = tf.placeholder(tf.float32, new[] { -1, n_steps, n_inputs });
        y = tf.placeholder(tf.int32, new[] { -1 });
        var cell = tf.nn.rnn_cell.BasicRNNCell(num_units: n_neurons);
        var (output, state) = tf.nn.dynamic_rnn(cell, X, dtype: tf.float32);
        var logits = tf.keras.layers.Dense(n_outputs).Apply(state);
        var cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels: y, logits: logits);
        loss = tf.reduce_mean(cross_entropy);
        var adam = tf.train.AdamOptimizer(learning_rate: learning_rate);
        optimizer = adam.minimize(loss);
        prediction = tf.nn.in_top_k(logits, y, 1);
        accuracy = tf.reduce_mean(tf.cast(prediction, tf.float32));

        return graph;
    }

    public override void Train()
    {
        float loss_val = 100.0f;
        float accuracy_val = 0f;

        // Number of training iterations in each epoch
        var n_batches = (int)y_train.shape[0] / batch_size;

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

        foreach (var epoch in range(n_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(n_batches))
            {
                var start = iteration * batch_size;
                var end = (iteration + 1) * batch_size;
                var (X_train, y_batch) = mnist.GetNextBatch(x_train, y_train, start, end);
                X_train = X_train.reshape((-1, n_steps, n_inputs));
                y_batch = np.argmax(y_batch, axis: 1);
                // Run optimization op (backprop)
                sess.run(optimizer, new FeedItem(X, X_train), new FeedItem(y, y_batch));

                if (iteration % display_freq == 0)
                {
                    // Calculate and display the batch loss and accuracy
                    (loss_val, accuracy_val) = sess.run((loss, accuracy), (X, X_train), (y, y_batch));
                    print($"iter {iteration.ToString("000")}: Loss={loss_val.ToString("0.0000")}, Training Accuracy={accuracy_val.ToString("P")}");
                }
            }

            // 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 override void Test()
    {
        var result = sess.run(new[] { loss, accuracy }, new FeedItem(X, x_test), new FeedItem(y, y_test));
        loss_test = result[0];
        accuracy_test = result[1];
        print("---------------------------------------------------------");
        print($"Test loss: {loss_test.ToString("0.0000")}, test accuracy: {accuracy_test.ToString("P")}");
        print("---------------------------------------------------------");
    }

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

        y_valid = np.argmax(y_valid, axis: 1);
        x_valid = np.reshape(x_valid, (- 1, n_steps, n_inputs));
        y_test = np.argmax(y_test, axis: 1);
        x_test = np.reshape(x_test, (-1, n_steps, n_inputs));

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