multilayer_perceptron.py

'''
A Multilayer Perceptron implementation example using TensorFlow library.
'''

from __future__ import print_function

#import Mnist data
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/tmp/data/", one_hot = True)

import tensorflow as tf 

#Parameters
learning_rate = 0.001
training_epochs = 15
batch_size = 100
display_step = 1

#Network parameters
n_hidden_1 = 256 # 1st layer number of features
n_hidden_2 = 256 # 2nd layer number of features
n_input = 784 # Mnist data input
n_classes = 10 # the number of classes mnist

#tf graph input
x = tf.placeholder("float", [None, n_input])
y = tf.placeholder("float", [None, n_classes])

#Create model
def multilayer_perceptron(x, weights, biases):
	#Hidden layer with relu activation
	layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1'])
	layer_1 = tf.nn.relu(layer_1)
	#hidden layer with RELU activation
	layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2'])
	layer_2 = tf.nn.relu(layer_2)
	# output layer with linear activation
	out_layer = tf.matmul(layer_2, weights['out']) + biases['out']
	return out_layer

# store layers weights and bias
weights = {
	'h1' : tf.Variable(tf.random_normal([n_input, n_hidden_1])),
	'h2' : tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),
	'out' : tf.Variable(tf.random_normal([n_hidden_2, n_classes]))
}

biases = {
	'b1' : tf.Variable(tf.random_normal([n_hidden_1])),
	'b2' : tf.Variable(tf.random_normal([n_hidden_2])),
	'out': tf.Variable(tf.random_normal([n_classes]))
}


# construct model
pred = multilayer_perceptron(x, weights, biases)
# define loss and optimizer
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits = pred, labels = y))
optimizer = tf.train.AdamOptimizer(learning_rate = learning_rate).minimize(cost)

#initializing the variables
init = tf.global_variables_initializer()

#launch the graph
with tf.Session() as sess:
	sess.run(init)

	#training cycle
	for epoch in range(training_epochs):
		avg_cost = 0.

		total_batch = int(mnist.train.num_examples/batch_size)
		# Loop over all batches
		for i in range(total_batch):
			batch_x, batch_y = mnist.train.next_batch(batch_size)
			# run optimization op (backprop) and cost op (loss value)
			_, c = sess.run([optimizer,cost], feed_dict = {x : batch_x, y : batch_y})

			#compute average loss
			avg_cost += c/total_batch

		#display logs per epoch step
		if (epoch +1) % display_step ==0:
			print("epoch:", '%04d'% (epoch +1), "cost =", "{:.9f}".format(avg_cost))

	print("optimization finished")

	#test model
	correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y,1))

	#calculate accuracy
	accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
	print("Accuracy", accuracy.eval({x:mnist.test.images, y: mnist.test.labels}))