cnn.py

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

mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
# print(mnist)


n_classes = 10
batch_size = 128

x = tf.placeholder('float',[None,784])
y = tf.placeholder('float')

def conv2d(x, W):
	return tf.nn.conv2d(x, W, strides=[1,1,1,1], padding='SAME')

def maxpool2d(x):
	return tf.nn.max_pool(x, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME')	

def convolutional_neural_network(x):
	
	weights = {'W_con1':tf.Variable(tf.random_normal([5,5,1,32])),
				'W_con2':tf.Variable(tf.random_normal([5,5,32,64])),
				'W_fc':tf.Variable(tf.random_normal([7*7*64,1024])),
				'out':tf.Variable(tf.random_normal([1024,n_classes]))}

	biases = {'b_con1':tf.Variable(tf.random_normal([32])),
				'b_con2':tf.Variable(tf.random_normal([64])),
				'b_fc':tf.Variable(tf.random_normal([1024])),
				'out':tf.Variable(tf.random_normal([n_classes]))}			


	x = tf.reshape(x, shape = [-1,28,28,1])
	conv1 = conv2d(x,weights['W_con1'])
	conv1 = maxpool2d(conv1)

	conv2 = conv2d(conv1,weights['W_con2'])
	conv2 = maxpool2d(conv2)

	fc = tf.reshape(conv2,[-1,7*7*64])
	fc = tf.nn.relu(tf.matmul(fc,weights['W_fc'])+biases['b_fc'])

	output = tf.matmul(fc,weights['out'])+biases['out']


				

	

	return output

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

	hm_epochs = 10

	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)
				_,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, y:mnist.test.labels}))	


train_neural_network(x)