MLlab11-2.py

#클래스를 이용하여 CNN구현
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
tf.set_random_seed(777)
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)

learning_rate = 0.001
traning_epochs = 15
batch_size = 100

class Model:
    def __init__(self,sess, name):
        self.sess = sess
        self.name = name
        self._build_net()

    def _build_net(self):
        with tf.variable_scope(self.name):
            self.keep_prob = tf.placeholder(tf.float32)

            self.X = tf.placeholder(tf.float32, [None,784])
            X_img = tf.reshape(self.X, [-1, 28, 28, 1])
            self.Y = tf.placeholder(tf.float32, [None, 10])

            W1 = tf.Variable(tf.random_normal([3,3,1,32],stddev=0.01))
            L1 = tf.nn.conv2d(X_img,W1,strides=[1,1,1,1],padding="SAME")
            L1 = tf.nn.relu(L1)
            L1 = tf.nn.max_pool(L1, ksize=[1,2,2,1],strides=[1,2,2,1], padding="SAME")
            L1 = tf.nn.dropout(L1, keep_prob=self.keep_prob)

            W2 = tf.Variable(tf.random_normal([3,3,32,64],stddev=0.01))
            L2 = tf.nn.conv2d(L1, W2, strides=[1,1,1,1], padding="SAME")
            L2 = tf.nn.relu(L2)
            L2 = tf.nn.max_pool(L2, ksize=[1,2,2,1], strides=[1,2,2,1], padding="SAME")
            L2 = tf.nn.dropout(L2, keep_prob=self.keep_prob)

            W3 = tf.Variable(tf.random_normal([3,3,64,128],stddev=0.01))
            L3 = tf.nn.conv2d(L2,W3,strides=[1,1,1,1], padding="SAME")
            L3 = tf.nn.relu(L3)
            L3 = tf.nn.max_pool(L3, ksize=[1,2,2,1], strides=[1,2,2,1], padding="SAME")
            L3 = tf.nn.dropout(L3, keep_prob=self.keep_prob)
            L3 = tf.reshape(L3, [-1, 128*4*4])

            W4 = tf.get_variable('W4', shape=[128*4*4, 625], initializer=tf.contrib.layers.xavier_initializer())
            b4 = tf.Variable(tf.random_normal([625]))
            L4 = tf.nn.relu(tf.matmul(L3, W4)+b4)
            L4 = tf.nn.dropout(L4, keep_prob=self.keep_prob)

            W5 = tf.get_variable('W5', shape=[625,10], initializer=tf.contrib.layers.xavier_initializer())
            b5 = tf.Variable(tf.random_normal([10]))
            self.hypothesis = tf.matmul(L4, W5)+b5

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

            correct_prediction = tf.equal(tf.arg_max(self.hypothesis,1), tf.arg_max(self.Y,1))
            self.accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

    def train(self, x_data, y_data, keep_prob=0.7):
            return self.sess.run([self.cost, self.optimizer], feed_dict = {self.X:x_data, self.Y:y_data, self.keep_prob:keep_prob})
    def predict(self, x_test, keep_prob =1.0):
            return self.sess.run(self.hypothesis,feed_dict={self.X : x_test, self.keep_prob : keep_prob})
    def get_accuracy(self, x_test, y_test,keep_prob = 1.0):
            return self.sess.run(self.accuracy, feed_dict={self.X :x_test, self.Y:y_test, self.keep_prob:keep_prob})
sess = tf.Session()
m1 = Model(sess, 'm1')
sess.run(tf.global_variables_initializer())
print('Learning Started')

for epoch in range(traning_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)
        c, _ = m1.train(batch_xs,batch_ys)
        avg_cost = avg_cost + c / total_batch
    print('Epoch:', '%04d' %(epoch + 1), 'cost = ', '{:.9f}'.format(avg_cost))

print('Learning Finished!')

print('Accuracy: ',m1.get_accuracy(mnist.test.images, mnist.test.labels))