<jupyter_start><jupyter_text>p.116 다중계층신경망, Convolutional Neural Network<jupyter_code>from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data/', one_hot = True)
import tensorflow as tf

x = tf.placeholder("float", shape = [None, 784])
y_ = tf.placeholder("float", shape = [None, 10])
x_image = tf.reshape(x, [-1, 28, 28, 1])
def weight_variable(shape):
    initial = tf.truncated_normal(shape, stddev = 0.1)
    return tf.Variable(initial)

def bias_variable(shape):
    initial = tf.constant(0.1, shape = shape)
    return tf.Variable(initial)

# 합성곱 계층
def conv2d(x, W):
    return tf.nn.conv2d(x, W, strides = [1, 1, 1, 1], padding = 'SAME')

# 맥스풀링 계층
def max_pool_2x2(x):
    return tf.nn.max_pool(x, ksize = [1, 2, 2, 1], strides = [1, 2, 2, 1],
                         padding = 'SAME')

W_conv1 = weight_variable([5, 5, 1, 32])
b_conv1 = bias_variable([32])
# ReLU (Rectified Linear Unit) 활성화 함수
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)
W_conv2 = weight_variable([5, 5, 32, 64])
b_conv2 = bias_variable([64])

h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
h_pool2 = max_pool_2x2(h_conv2)
W_fc1 = weight_variable([7 * 7 * 64, 1024])
b_fc1 = bias_variable([1024])
h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 64])

h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
keep_prob = tf.placeholder("float")
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])

y_conv = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)
cross_entropy = -tf.reduce_sum(y_ * tf.log(y_conv))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))

sess = tf.Session()

sess.run(tf.initialize_all_variables())

for i in range(1000):
    batch = mnist.train.next_batch(100)
    if i % 100 == 0:
        train_accuracy = sess.run(accuracy,
                                 feed_dict = {x: batch[0], y_ : batch[1], keep_prob : 1.0})
        print("step: {i}, training accuracy: {tr}".format(i = i, tr = train_accuracy))
        
    sess.run(train_step,
            feed_dict = {x: batch[0], y_: batch[1], keep_prob: 0.5})

print("test accuracy: {te}".format(te = sess.run(accuracy, feed_dict = {x: mnist.test.images, y_: mnist.test.labels, keep_prob:1.0})))<jupyter_output>step: 0, training accuracy: 0.119999997318
step: 100, training accuracy: 0.920000016689
step: 200, training accuracy: 0.949999988079
step: 300, training accuracy: 0.949999988079
step: 400, training accuracy: 0.889999985695
step: 500, training accuracy: 0.97000002861
step: 600, training accuracy: 0.959999978542
step: 700, training accuracy: 0.980000019073
step: 800, training accuracy: 0.990000009537
step: 900, training accuracy: 0.959999978542
test accuracy: 0.97159999609
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