import input_data

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

import tensorflow as tf

from tensorflow.models.rnn import rnn, rnn_cell

import numpy as np

learning_rate = 0.001

training_iters = 100000

batch_size = 128

display_step = 10

n_input = 28 # MNIST data input (img shape: 28*28)

n_steps = 28 # timesteps

n_hidden = 128 # hidden layer num of features

n_classes = 10 # MNIST total classes (0-9 digits)

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

istate = tf.placeholder("float", [None, 2*n_hidden]) #state & cell => 2x n_hidden

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

weights = {

'hidden': tf.Variable(tf.random_normal([n_input, n_hidden])), # Hidden layer weights

'out': tf.Variable(tf.random_normal([n_hidden, n_classes]))

}

biases = {

'hidden': tf.Variable(tf.random_normal([n_hidden])),

'out': tf.Variable(tf.random_normal([n_classes]))

}

def RNN(_X, _istate, _weights, _biases):

# input shape: (batch_size, n_steps, n_input)

_X = tf.transpose(_X, [1, 0, 2]) # permute n_steps and batch_size

# Reshape to prepare input to hidden activation

_X = tf.reshape(_X, [-1, n_input]) # (n_steps*batch_size, n_input)

# Linear activation

_X = tf.matmul(_X, _weights['hidden']) + _biases['hidden']

# Define a lstm cell with tensorflow

lstm_cell = rnn_cell.BasicLSTMCell(n_hidden, forget_bias=1.0)

# Split data because rnn cell needs a list of inputs for the RNN inner loop

_X = tf.split(0, n_steps, _X) # n_steps * (batch_size, n_hidden)

# Get lstm cell output

outputs, states = rnn.rnn(lstm_cell, _X, initial_state=_istate)

# Linear activation

# Get inner loop last output

return tf.matmul(outputs[-1], _weights['out']) + _biases['out']

pred = RNN(x, istate, weights, biases)

cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y)) # Softmax loss

optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) # Adam Optimizer

correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1))

accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.types.float32))

init = tf.initialize_all_variables()

with tf.Session() as sess:

sess.run(init)

step = 1

# Keep training until reach max iterations

while step * batch_size < training_iters:

batch_xs, batch_ys = mnist.train.next_batch(batch_size)

# Reshape data to get 28 seq of 28 elements

batch_xs = batch_xs.reshape((batch_size, n_steps, n_input))

# Fit training using batch data

sess.run(optimizer, feed_dict={x: batch_xs, y: batch_ys,

istate: np.zeros((batch_size, 2*n_hidden))})

if step % display_step == 0:

# Calculate batch accuracy

acc = sess.run(accuracy, feed_dict={x: batch_xs, y: batch_ys,

istate: np.zeros((batch_size, 2*n_hidden))})

# Calculate batch loss

loss = sess.run(cost, feed_dict={x: batch_xs, y: batch_ys,

istate: np.zeros((batch_size, 2*n_hidden))})

print "Iter " + str(step*batch_size) + ", Minibatch Loss= " + "{:.6f}".format(loss) + \

", Training Accuracy= " + "{:.5f}".format(acc)

step += 1

print "Optimization Finished!"

# Calculate accuracy for 256 mnist test images

test_len = 256

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,

istate: np.zeros((test_len, 2*n_hidden))})