Neural Network로 MNIST 학습하기

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

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

W1 = tf.Variable(tf.random_normal([784, 256]))
b1 = tf.Variable(tf.random_normal([256]))
L1 = tf.nn.relu(tf.matmul(X, W1) + b1)

W2 = tf.Variable(tf.random_normal([256, 256]))
b2 = tf.Variable(tf.random_normal([256]))
L2 = tf.nn.relu(tf.matmul(L1, W2) + b2)

W3 = tf.Variable(tf.random_normal([256, 10]))
b3 = tf.Variable(tf.random_normal([10]))
hypothesis = tf.matmul(L2, W3) + b3

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
    total_batch = int(mnist.train.num_examples / batch_size)

    for i in range(total_batch):
        batch_xs, batch_ys = mnist.train.next_batch(batch_size)
        feed_dict = {X: batch_xs, Y: batch_ys}
        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 = 183.513505096
Epoch: 0002 cost = 43.499959541
Epoch: 0003 cost = 27.054975612
Epoch: 0004 cost = 18.866209335
Epoch: 0005 cost = 13.745875303
Epoch: 0006 cost = 10.223983004
Epoch: 0007 cost = 7.581343187
Epoch: 0008 cost = 5.765891739
Epoch: 0009 cost = 4.320811899
Epoch: 0010 cost = 3.161147363
Epoch: 0011 cost = 2.411464093
Epoch: 0012 cost = 1.727428055
Epoch: 0013 cost = 1.445400364
Epoch: 0014 cost = 1.131284376
Epoch: 0015 cost = 0.882475840
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}
    )
)

Accuracy: 0.9459

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]}
    )
)

Label:  [9]
Prediction:  [9]

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