<reponame>inity13/flowingTensor<filename>deep-net.py<gh_stars>0
import tensorflow as tf

from tensorflow.examples.tutorials.mnist import input_data

mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)

'''
Number of nodes,Hidden layers
'''
n_nodes_hl1 = 500
n_nodes_hl2 = 500
n_nodes_hl3 = 500

'''
Number of classes
'''
n_classes = 10
'''
Size of the batch that training uses
'''
batch_size = 100

#height x width
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]))}

	#(input_data * weights) + biases
	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_network_model(x)
	cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y,logits=prediction))

	# learning_rate = 0.001 by default (fine enough)
	optimizer = tf.train.AdamOptimizer().minimize(cost)

	#cycles feed forward + backpropogation
	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)




import tensorflow as tf

from tensorflow.examples.tutorials.mnist import input_data

mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)

'''
Number of nodes,Hidden layers
'''
n_nodes_hl1 = 500
n_nodes_hl2 = 500
n_nodes_hl3 = 500

'''
Number of classes
'''
n_classes = 10
'''
Size of the batch that training uses
'''
batch_size = 100

#height x width
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]))}

	#(input_data * weights) + biases
	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_network_model(x)
	cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y,logits=prediction))

	# learning_rate = 0.001 by default (fine enough)
	optimizer = tf.train.AdamOptimizer().minimize(cost)

	#cycles feed forward + backpropogation
	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)
Number of nodes,Hidden layers Number of classes Size of the batch that training uses #height x width #(input_data * weights) + biases # learning_rate = 0.001 by default (fine enough) #cycles feed forward + backpropogation