LSTM_model_1 (1).py

import tensorflow as tf
from tensorflow.contrib import rnn

from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)

learning_rate = 0.001
training_iters = 100000
batch_size = 128
display_step = 10

n_input = 28 
n_steps = 28 
n_hidden = 128 
n_classes = 10 

x = tf.placeholder("float", [None, n_steps, n_input])
y = tf.placeholder("float", [None, n_classes])

weights = {
    'out': tf.Variable(tf.random_normal([n_hidden, n_classes]))
}
biases = {
    'out': tf.Variable(tf.random_normal([n_classes]))
}

def RNN(x, weights, biases):
    x = tf.transpose(x, [1, 0, 2])
    x = tf.reshape(x, [-1, n_input])
    x = tf.split(axis=0, num_or_size_splits=n_steps, value=x)
    lstm_cell = rnn.BasicLSTMCell(n_hidden, forget_bias=1.0)
    outputs, states = rnn.static_rnn(lstm_cell, x, dtype=tf.float32)
    return tf.matmul(outputs[-1], weights['out']) + biases['out']

pred = RNN(x, weights, biases)
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)

correct_pred = tf. equal(tf.argmax(pred,1), tf.argmax(y,1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))

init = tf.global_variables_initializer()

with tf.Session() as sess:
    sess.run(init)
    step = 1
    while step * batch_size < training_iters:
        batch_x, batch_y = mnist.train.next_batch(batch_size)
        batch_x = batch_x.reshape((batch_size, n_steps, n_input))
        sess.run(optimizer, feed_dict={x: batch_x, y: batch_y})
        if step % display_step == 0:
            acc = sess.run(accuracy, feed_dict={x: batch_x, y: batch_y})
            loss = sess.run(cost, feed_dict={x: batch_x, y: batch_y})
            print("Iter " + str(step*batch_size) + ", Minibatch Loss= " + \
                  "{:.6f}".format(loss) + ", Training Accuracy= " + \
                  "{:.5f}".format(acc))
        step += 1
    print("Optimization Finished!")

    test_len = 128
    test_data = mnist.test.images[:test_len].reshape((-1, n_steps, n_input))
    test_label = mnist.test.labels[:test_len]
    print("Testing Accuracy:", \
sess.run(accuracy, feed_dict={x: test_data, y: test_label}))