RNNMnistTensorFlow.py

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
from tensorflow.contrib import rnn

mnist = input_data.read_data_sets("/data/", one_hot=True)

hm_epochs = 3
n_classes = 10
batch_size = 128

chunck_size = 28
n_chucks = 28
rnn_size = 128

x = tf.placeholder('float', [None, n_chucks, chunck_size])
y = tf.placeholder('float')


def recurrent_neural_network_model(x):
    layer = {'weights':tf.Variable(tf.random_normal([rnn_size, n_classes])),
                      'biases':tf.Variable(tf.random_normal([n_classes]))}

    x = tf.transpose(x, [1,0,2])
    x = tf.reshape(x, [-1, chunck_size])
    x = tf.split(x, n_chucks, 0)

    lstm_node = rnn.BasicLSTMCell(rnn_size)
    outputs, states = rnn.static_rnn(lstm_node, x, dtype=tf.float32)

    output = tf.matmul(outputs[-1], layer['weights']) + layer['biases']

    return output


def train_neural_network(x):
    prediction = recurrent_neural_network_model(x)
    cost = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits(logits=prediction, labels=y) )
    optimizer = tf.train.AdamOptimizer().minimize(cost)

    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())

        for epoch in range(hm_epochs):
            epoch_loss = 0
            for _ in range(int(mnist.train.num_examples/batch_size)):
                epoch_x, epoch_y = mnist.train.next_batch(batch_size)
                epoch_x = epoch_x.reshape((batch_size, n_chucks, chunck_size))
                _, c = sess.run([optimizer, cost], feed_dict={x: epoch_x, y: epoch_y})
                epoch_loss += c

            print ('Epoch ', epoch, ' completed out of ', hm_epochs, ' loss ', epoch_loss )

        correct = tf.equal(tf.argmax(prediction,1), tf.argmax(y,1))
        accuracy = tf.reduce_mean(tf.cast(correct, 'float'))
        print ('Accuracy: ', accuracy.eval({x: mnist.test.images.reshape((-1,n_chucks,chunck_size)), y: mnist.test.labels}))

train_neural_network(x)