mlp.py

import numpy as np
import scipy.io
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
%matplotlib inline  
print ("PACKAGES LOADED")

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

# NETWORK TOPOLOGIES
n_hidden_1 = 256 
n_hidden_2 = 128 
n_input    = 784 
n_classes  = 10  

# INPUTS AND OUTPUTS
x = tf.placeholder("float", [None, n_input])
y = tf.placeholder("float", [None, n_classes])
    
# NETWORK PARAMETERS
stddev = 0.1
weights = {
    'h1': tf.Variable(tf.random_normal([n_input, n_hidden_1], stddev=stddev)),
    'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2], stddev=stddev)),
    'out': tf.Variable(tf.random_normal([n_hidden_2, n_classes], stddev=stddev))
}
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]))
}
print ("NETWORK READY")

def mlp(_X, _weights, _biases):
    layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(_X, _weights['h1']), _biases['b1'])) 
    layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, _weights['h2']), _biases['b2']))
    return (tf.matmul(layer_2, _weights['out']) + _biases['out'])
  
# PREDICTION
pred = mlp(x, weights, biases)

# LOSS AND OPTIMIZER
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y)) 
# optm = tf.train.GradientDescentOptimizer(learning_rate=0.001).minimize(cost) 
optm = tf.train.AdamOptimizer(learning_rate=0.001).minimize(cost) 
corr = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))    
accr = tf.reduce_mean(tf.cast(corr, "float"))

# INITIALIZER
init = tf.global_variables_initializer()
print ("FUNCTIONS READY")

# PARAMETERS
training_epochs = 20
batch_size      = 100
display_step    = 4
# LAUNCH THE GRAPH
sess = tf.Session()
sess.run(init)
# OPTIMIZE
for epoch in range(training_epochs):
    avg_cost = 0.
    total_batch = int(mnist.train.num_examples/batch_size)
    # ITERATION
    for i in range(total_batch):
        batch_xs, batch_ys = mnist.train.next_batch(batch_size)
        feeds = {x: batch_xs, y: batch_ys}
        sess.run(optm, feed_dict=feeds)
        avg_cost += sess.run(cost, feed_dict=feeds)
    avg_cost = avg_cost / total_batch
    # DISPLAY
    if (epoch+1) % display_step == 0:
        print ("Epoch: %03d/%03d cost: %.9f" % (epoch, training_epochs, avg_cost))
        feeds = {x: batch_xs, y: batch_ys}
        train_acc = sess.run(accr, feed_dict=feeds)
        print ("TRAIN ACCURACY: %.3f" % (train_acc))
        feeds = {x: mnist.test.images, y: mnist.test.labels}
        test_acc = sess.run(accr, feed_dict=feeds)
        print ("TEST ACCURACY: %.3f" % (test_acc))
print ("OPTIMIZATION FINISHED")

# SAVE
trainimg   = mnist.train.images
trainlabel = mnist.train.labels
testimg    = mnist.test.images
testlabel  = mnist.test.labels
w1   = sess.run(weights['h1'])
w2   = sess.run(weights['h2'])
wout = sess.run(weights['out'])
b1   = sess.run(biases['b1'])
b2   = sess.run(biases['b2'])
bout = sess.run(biases['out'])

# SAVE TO MAT FILE 
savepath = './data/mlp.mat'
scipy.io.savemat(savepath
    , mdict={'trainimg': trainimg, 'trainlabel': trainlabel
            , 'testimg': testimg, 'testlabel': testlabel
            , 'w1': w1, 'w2': w2, 'wout': wout, 'b1': b1, 'b2': b2, 'bout': bout})
print ("%s SAVED." % (savepath))