RNN.py

import tensorflow as tf
from tensorflow.python.ops import rnn, rnn_cell
import numpy as np
from tensorflow.examples.tutorials.mnist import input_data

mnist = input_data.read_data_sets('MNIST_data', one_hot=True) #call mnist function

learningRate = 0.001
trainingIters = 22000
batchSize = 110
displayStep = 10

nInput = 28 #we want the input to take the 28 pixels
nSteps = 28 #every 28
nHidden = 256  #number of neurons for the RNN
nClasses = 10 #this is MNIST so you know

x = tf.placeholder('float', [None, nSteps, nInput])
y = tf.placeholder('float', [None, nClasses])

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

def RNN(x, weights, biases):
	x = tf.transpose(x, [1,0,2])
	x = tf.reshape(x, [-1, nInput])
	x = tf.split(0, nSteps, x) #configuring so you can get it as needed for the 28 pixels
	#lstmCell =  rnn_cell.BasicRNNCell(nHidden) #find which lstm to use in the documentation
	lstmCell = rnn_cell.GRUCell(nHidden)  # find which lstm to use in the documentation
	outputs, states = rnn.rnn(lstmCell, x, dtype=tf.float32) #for the rnn where to get the output and hidden state
	return tf.matmul(outputs[-1], weights['out'])+ biases['out']

pred = RNN(x, weights, biases)


cost = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(pred, y))
optimizer = tf.train.AdamOptimizer(learning_rate=learningRate).minimize(cost)

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

init = tf.initialize_all_variables()
testData = mnist.test.images.reshape((-1, nSteps, nInput))
testLabel = mnist.test.labels

with tf.Session() as sess:
	sess.run(init)
	step = 1
	while step* batchSize < trainingIters:
		batchX, batchY = mnist.train.next_batch(batchSize) #mnist has a way to get the next batch
		batchX = batchX.reshape((batchSize, nSteps, nInput))
		sess.run(optimizer, feed_dict={x: batchX, y: batchY})
		if step % displayStep == 0:
			acc = sess.run(accuracy, feed_dict={x: batchX, y: batchY})
			test_acc = sess.run(accuracy, feed_dict={x: testData, y: testLabel})
			loss = sess.run(cost, feed_dict={x: batchX, y: batchY})
			print("Iter " + str(step*batchSize) + ", Minibatch Loss= " + \
                  "{:.6f}".format(loss) + ", Training Accuracy= " + \
                  "{:.5f}".format(acc) + ", Test Accuracy= " + \
                  "{:.5f}".format(test_acc))
		step +=1
	print('Optimization finished')
	testData = mnist.test.images.reshape((-1, nSteps, nInput))
	testLabel = mnist.test.labels
	print("Testing Accuracy:", \
        sess.run(accuracy, feed_dict={x: testData, y: testLabel}))