deeplearning.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_nodes_hl1 = 500
n_nodes_hl2 = 500
n_nodes_hl3 = 500

n_classes = 10
batch_size = 100

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

def neural_network_model(data):
	print(data)
	hidden_1_layer = {'weights':tf.Variable(tf.random_normal([784,n_nodes_hl1])),
	'biases':tf.Variable(tf.random_normal([n_nodes_hl1]))}
	print(hidden_1_layer)

	hidden_2_layer = {'weights':tf.Variable(tf.random_normal([n_nodes_hl1,n_nodes_hl2])),
	'biases':tf.Variable(tf.random_normal([n_nodes_hl2]))}

	hidden_3_layer = {'weights':tf.Variable(tf.random_normal([n_nodes_hl2,n_nodes_hl3],)),
	'biases':tf.Variable(tf.random_normal([n_nodes_hl3]))}

	output_layer = {'weights':tf.Variable(tf.random_normal([n_nodes_hl3,n_classes],)),
	'biases':tf.Variable(tf.random_normal([n_classes]))}

	l1 = tf.add(tf.matmul(data,hidden_1_layer['weights']),hidden_1_layer['biases'])
	print(l1)
	l1 = tf.nn.relu(l1)

	l2 = tf.add(tf.matmul(l1,hidden_2_layer['weights']),hidden_2_layer['biases'])
	l2 = tf.nn.relu(l2)

	l3 = tf.add(tf.matmul(l2,hidden_3_layer['weights']),hidden_3_layer['biases'])
	l3 = tf.nn.relu(l3)

	output = tf.matmul(l3,output_layer['weights'])+output_layer['biases']
	

	return output

def train_neural_network(x):
	prediction = neural_network_model(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)