Drop Out을 사용해 MNIST 정확도 높이기

import tensorflow as tf
import matplotlib.pyplot as plt
import random

from tensorflow.examples.tutorials.mnist import input_data

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

learning_rate = 0.001
training_epochs = 15
batch_size = 100
total_batch = int(mnist.train.num_examples / batch_size)

X = tf.placeholder(tf.float32, [None, 784])
Y = tf.placeholder(tf.float32, [None, 10])

keep_prob = tf.placeholder(tf.float32)

W1 = tf.get_variable("W1", shape=[784, 512],
                     initializer=tf.contrib.layers.xavier_initializer())
b1 = tf.Variable(tf.random_normal([512]))
L1 = tf.nn.relu(tf.matmul(X, W1) + b1)
L1 = tf.nn.dropout(L1, keep_prob=keep_prob)

W2 = tf.get_variable("W2", shape=[512, 512],
                     initializer=tf.contrib.layers.xavier_initializer())
b2 = tf.Variable(tf.random_normal([512]))
L2 = tf.nn.relu(tf.matmul(L1, W2) + b2)
L2 = tf.nn.dropout(L2, keep_prob=keep_prob)

W3 = tf.get_variable("W3", shape=[512, 512],
                     initializer=tf.contrib.layers.xavier_initializer())
b3 = tf.Variable(tf.random_normal([512]))
L3 = tf.nn.relu(tf.matmul(L2, W3) + b3)
L3 = tf.nn.dropout(L3, keep_prob=keep_prob)

W4 = tf.get_variable("W4", shape=[512, 512],
                     initializer=tf.contrib.layers.xavier_initializer())
b4 = tf.Variable(tf.random_normal([512]))
L4 = tf.nn.relu(tf.matmul(L3, W4) + b4)
L4 = tf.nn.dropout(L4, keep_prob=keep_prob)

W5 = tf.get_variable("W5", shape=[512, 10],
                     initializer=tf.contrib.layers.xavier_initializer())
b5 = tf.Variable(tf.random_normal([10]))
hypothesis = tf.matmul(L4, W5) + b5

cost = tf.reduce_mean(
    tf.nn.softmax_cross_entropy_with_logits_v2(logits=hypothesis, labels=Y)
)
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)

sess = tf.Session()
sess.run(tf.global_variables_initializer())

for epoch in range(training_epochs):
    avg_cost = 0

    for i in range(total_batch):
        batch_xs, batch_ys = mnist.train.next_batch(batch_size)
        feed_dict = {X: batch_xs, Y: batch_ys, keep_prob: 0.7}
        c, _ = sess.run([cost, optimizer], feed_dict=feed_dict)
        avg_cost += c / total_batch

    print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.9f}'.format(avg_cost))

print('Learning Finished!')

Epoch: 0001 cost = 0.468633001
Epoch: 0002 cost = 0.170478383
Epoch: 0003 cost = 0.131077586
Epoch: 0004 cost = 0.108677959
Epoch: 0005 cost = 0.096312569
Epoch: 0006 cost = 0.082181592
Epoch: 0007 cost = 0.078254419
Epoch: 0008 cost = 0.069326370
Epoch: 0009 cost = 0.062080975
Epoch: 0010 cost = 0.055655396
Epoch: 0011 cost = 0.057310239
Epoch: 0012 cost = 0.055785087
Epoch: 0013 cost = 0.052270090
Epoch: 0014 cost = 0.048582647
Epoch: 0015 cost = 0.044075073
Learning Finished!

correct_prediction = tf.equal(tf.argmax(hypothesis, 1), tf.argmax(Y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print(
    'Accuracy:',
    sess.run(
        accuracy,
        feed_dict={
            X: mnist.test.images,
            Y: mnist.test.labels,
            keep_prob: 1
        }
    )
)

Accuracy: 0.9813

r = random.randint(0, mnist.test.num_examples - 1)
print("Label: ", sess.run(tf.argmax(mnist.test.labels[r:r+1], 1)))
print(
    "Prediction: ",
    sess.run(
        tf.argmax(hypothesis, 1),
        feed_dict={X: mnist.test.images[r:r+1], keep_prob:1}
    )
)

Label:  [6]
Prediction:  [6]

plt.imshow(
    mnist.test.images[r:r + 1].reshape(28, 28),
    cmap='Greys', interpolation='nearest'
)
plt.show()