Source Code to "Sentdex Deep Learning with Neural Networks and Tensorflow" part 4

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_net_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.matmul(l3, output_layer['weights']) + output_layer['biases']

	return output

def train_neural_network(x):
	prediction = neural_net_model(x)
	cost = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits(prediction,y))

	optimizer = tf.train.AdamOptimizer().minimize(cost)

	hm_epochs = 10

	with tf.Session() as sess:
		sess.run(tf.initialize_all_variables())

		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)
				_,epoch_c = sess.run([optimizer, cost], feed_dict = {x: epoch_x, y: epoch_y})
				epoch_loss += epoch_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)

softmax_cross_entropy_with_logits( _sentinel=None, labels=None, logits=None, dim=-1, name=None )
Try passing the labels and logits parameters
cost = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits(labels = y, logits = prediction))