Multilayer Perceptron in TensorFlow

Credits: Forked from TensorFlow-Examples by Aymeric Damien

Setup

Refer to the setup instructions

In [2]:
# Import MINST data
import input_data
mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)
Extracting /tmp/data/train-images-idx3-ubyte.gz
Extracting /tmp/data/train-labels-idx1-ubyte.gz
Extracting /tmp/data/t10k-images-idx3-ubyte.gz
Extracting /tmp/data/t10k-labels-idx1-ubyte.gz
In [3]:
import tensorflow as tf
In [4]:
# Parameters
learning_rate = 0.001
training_epochs = 15
batch_size = 100
display_step = 1
In [5]:
# Network Parameters
n_hidden_1 = 256 # 1st layer num features
n_hidden_2 = 256 # 2nd layer num features
n_input = 784 # MNIST data input (img shape: 28*28)
n_classes = 10 # MNIST total classes (0-9 digits)
In [6]:
# tf Graph input
x = tf.placeholder("float", [None, n_input])
y = tf.placeholder("float", [None, n_classes])
In [7]:
# Create model
def multilayer_perceptron(_X, _weights, _biases):
    #Hidden layer with RELU activation
    layer_1 = tf.nn.relu(tf.add(tf.matmul(_X, _weights['h1']), _biases['b1'])) 
    #Hidden layer with RELU activation
    layer_2 = tf.nn.relu(tf.add(tf.matmul(layer_1, _weights['h2']), _biases['b2'])) 
    return tf.matmul(layer_2, weights['out']) + biases['out']
In [8]:
# Store layers weight & 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]))
}
In [9]:
# Construct model
pred = multilayer_perceptron(x, weights, biases)
In [10]:
# Define loss and optimizer
# Softmax loss
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y)) 
# Adam Optimizer
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) 
In [11]:
# Initializing the variables
init = tf.global_variables_initializer()
In [12]:
# 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_xs, batch_ys = mnist.train.next_batch(batch_size)
            # Fit training using batch data
            sess.run(optimizer, feed_dict={x: batch_xs, y: batch_ys})
            # Compute average loss
            avg_cost += sess.run(cost, feed_dict={x: batch_xs, y: batch_ys})/total_batch
        # Display logs per epoch step
        if epoch % 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})
Epoch: 0001 cost= 160.113980416
Epoch: 0002 cost= 38.665780694
Epoch: 0003 cost= 24.118004577
Epoch: 0004 cost= 16.440921303
Epoch: 0005 cost= 11.689460141
Epoch: 0006 cost= 8.469423468
Epoch: 0007 cost= 6.223237230
Epoch: 0008 cost= 4.560174118
Epoch: 0009 cost= 3.250516910
Epoch: 0010 cost= 2.359658795
Epoch: 0011 cost= 1.694081847
Epoch: 0012 cost= 1.167997509
Epoch: 0013 cost= 0.872986831
Epoch: 0014 cost= 0.630616366
Epoch: 0015 cost= 0.487381571
Optimization Finished!
Accuracy: 0.9462