mlp.py

from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf
import matplotlib.pyplot as plt

# Load data

mnist = input_data.read_data_sets('MNIST_data', one_hot=True)

# Neural Network Initialization

x = tf.placeholder(tf.float32, shape=[None, 784])

W_1 = tf.Variable(tf.random_normal([784, 100], stddev=.05))
b_1 = tf.Variable(tf.random_normal([100], stddev=.05))
h_1 = tf.nn.sigmoid(tf.matmul(x, W_1) + b_1)

W_2 = tf.Variable(tf.random_normal([100, 10], stddev=.05))
b_2 = tf.Variable(tf.random_normal([10], stddev=.05))
y = tf.nn.softmax(tf.matmul(h_1, W_2) + b_2)

y_ = tf.placeholder(tf.float32, shape=[None, 10])

# Output Initialization

cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))
train_step = tf.train.GradientDescentOptimizer(0.05).minimize(cross_entropy)

correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

# Training

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

    train_accs, validation_accs, test_accs = [], [], []

    for i in range(100000):
        batch = mnist.train.next_batch(100)
        train_step.run(feed_dict={x: batch[0], y_: batch[1]})

        if i % 100 == 0:
            train_acc = accuracy.eval(feed_dict={x: mnist.train.images, y_: mnist.train.labels})
            validation_acc = accuracy.eval(feed_dict={x: mnist.validation.images, y_: mnist.validation.labels})
            test_acc = accuracy.eval(feed_dict={x: mnist.test.images, y_: mnist.test.labels})

            train_acc.append( train_acc )
            validation_acc.append( validation_acc )
            test_acc.append( test_acc )

            print ("Iteration %s: train accuracy %.3f" % (i, train_acc))

    # Save Model

    saver = tf.train.Saver()
    save_path = saver.save(sess, "/tmp/model.ckpt")
    print("Model saved in file: %s" % save_path)

# Plot Training Results

plt.figure(figsize=(18, 12))
plt.plot(1-np.array(train_acc), label="train")
plt.plot(1-np.array(validation_acc), label="validation")
plt.plot(1-np.array(test_acc), label="test")
plt.xlabel("Fraction Error")
plt.ylabel("Number of Batches in Hundreds")
plt.ylim([0, 0.15])
plt.legend()
plt.show()