Credits: Forked from TensorFlow-Examples by Aymeric Damien
Refer to the setup instructions
In [2]:
# Import MINST data
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
In [3]:
'''
To classify images using a reccurent 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.
'''
# Parameters
learning_rate = 0.001
training_iters = 100000
batch_size = 128
display_step = 10
# Network Parameters
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)
In [4]:
# tf Graph input
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])
# Define weights
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]))
}
In [5]:
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']
In [6]:
pred = RNN(x, istate, weights, biases)
# Define loss and optimizer
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
# Evaluate model
correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
In [7]:
# Initializing the variables
init = tf.global_variables_initializer()
# Launch the graph
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))})