deep_net.py

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

mnist=input_data.read_data_sets("/tmp/data/", one_hot=True)
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):
	hidden_1_layer={
		'weights': tf.Variable(tf.random_normal([784, n_nodes_hl1])), 
		'biases': tf.Variable(tf.random_normal([n_nodes_hl1]))
	}
	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'])
	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.add(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(logits=prediction, labels=y))
	optimizer=tf.train.AdamOptimizer().minimize(cost)
	epochs=10
	with tf.Session() as sess:
		sess.run(tf.initialize_all_variables())
		for epoch in range(epochs):
			epoch_loss=0
			for _ in range(int(mnist.train.num_examples/batch_size)):
				e_x, e_y= mnist.train.next_batch(batch_size)
				_, c= sess.run([optimizer, cost], feed_dict={x:e_x, y:e_y})
				epoch_loss+=c
			print('Epoch ', epoch, ' completed out of ', 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)