DigitRecognitionLSTM.cs

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using System.Diagnostics;
using System.Linq;
using Tensorflow;
using Tensorflow.Operations;
using static Tensorflow.Binding;

namespace TensorFlowNET.Examples;

/// <summary>
/// Bi-directional Recurrent Neural Network.
/// 
/// To classify images using a bidirectional recurrent neural network, we consider
/// every image row as a sequence of pixels.Because MNIST image shape is 28*28px,
/// we will then handle 28 sequences of 28 steps for every sample.
/// 
/// https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/bidirectional_rnn.py
/// </summary>
public class DigitRecognitionLSTM : SciSharpExample, IExample
{
    // Training Parameters
    float learning_rate = 0.001f;
    int training_steps = 1000;
    int batch_size = 128;
    int display_step = 100;

    // Network Parameters
    int num_input = 28;
    int timesteps = 28;
    int num_hidden = 128; // hidden layer num of features
    int num_classes = 10; // MNIST total classes (0-9 digits)

    Datasets<MnistDataSet> mnist;

    Tensor X, Y;
    Tensor loss_op, accuracy, prediction;
    Operation train_op;

    float accuracy_test = 0f;
    Session sess;

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

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

        PrepareData();
        BuildGraph();

        sess = tf.Session();

        Train();
        Test();

        return accuracy_test > 0.40;
    }

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

        X = tf.placeholder(tf.float32, (-1, timesteps, num_input));
        Y = tf.placeholder(tf.float32, (-1, num_classes));

        // Hidden layer weights => 2*n_hidden because of forward + backward cells
        var weights = tf.Variable(tf.random.normal((2 * num_hidden, num_classes)));
        var biases = tf.Variable(tf.random.normal(num_classes));

        // Unstack to get a list of 'timesteps' tensors of shape (batch_size, num_input)
        var x = tf.unstack(X, timesteps, 1);

        // Define lstm cells with tensorflow
        // Forward direction cell
        var lstm_fw_cell = new BasicLstmCell(num_hidden, forget_bias: 1.0f);
        // Backward direction cell
        var lstm_bw_cell = new BasicLstmCell(num_hidden, forget_bias: 1.0f);

        // Get lstm cell output
        var (outputs, _, _) = rnn.static_bidirectional_rnn(lstm_fw_cell, lstm_bw_cell, x, dtype: tf.float32);

        // Linear activation, using rnn inner loop last output
        var logits = tf.matmul(outputs.Last(), weights) + biases;
        prediction = tf.nn.softmax(logits);

        // Define loss and optimizer
        loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(
            logits: logits, labels: Y));
        var optimizer = tf.train.GradientDescentOptimizer(learning_rate: learning_rate);
        train_op = optimizer.minimize(loss_op);

        // Evaluate model (with test logits, for dropout to be disabled)
        var correct_pred = tf.equal(tf.math.argmax(prediction, 1), tf.math.argmax(Y, 1));
        accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32));

        return graph;
    }

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

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

        var sw = new Stopwatch();
        sw.Start();
        foreach (var step in range(1, training_steps + 1))
        {
            var (batch_x, batch_y) = mnist.Train.GetNextBatch(batch_size);

            // Reshape data to get 28 seq of 28 elements
            batch_x = batch_x.reshape((batch_size, timesteps, num_input));

            // Run optimization op (backprop)
            sess.run(train_op, (X, batch_x), (Y, batch_y));

            if (step % display_step == 0 || step == 1)
            {
                // Calculate batch loss and accuracy
                (loss_val, accuracy_val) = sess.run((loss_op, accuracy), (X, batch_x), (Y, batch_y));
                print($"Step {step}: Minibatch Loss={loss_val.ToString("0.0000")}, Training Accuracy={accuracy_val.ToString("0.000")} {sw.ElapsedMilliseconds}ms");
                sw.Restart();
            }
        }

        print("Optimization Finished!");
    }

    public override void Test()
    {
        // Calculate accuracy for 128 mnist test images
        var (x_test, y_test) = (mnist.Test.Data[":128"], mnist.Test.Labels[":128"]);
        x_test = x_test.reshape((-1, timesteps, num_input));
        accuracy_test = sess.run(accuracy, new FeedItem(X, x_test), new FeedItem(Y, y_test));
        print("---------------------------------------------------------");
        print($"Testing Accuracy: {accuracy_test.ToString("P")}");
        print("---------------------------------------------------------");
    }

    public override void PrepareData()
    {
        var loader = new MnistModelLoader();
        mnist = loader.LoadAsync(".resources/mnist", oneHot: true, showProgressInConsole: true).Result;

        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)}");
    }
}