tensorflow入门：CNN for MNIST

import tensorflow as tf
import numpy as np

from tensorflow.examples.tutorials.mnist import input_data

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

# hyperparameter
learning_rate = 0.001
training_epoches = 20
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.training = tf.placeholder(tf.bool)
        # input placeholder for X & Y
        self.X = tf.placeholder(tf.float32, [None, 784])
        self.Y = tf.placeholder(tf.float32, [None, 10])
        # img 28x28x1 (black/white)
        X_img = tf.reshape(self.X, [-1, 28, 28, 1])
            
        # convolutional layer 1 & pooling layer 1
        conv1 = tf.layers.conv2d(inputs=X_img, filters=32, kernel_size=[3, 3], 
                                 padding="SAME", activation=tf.nn.relu)
        pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[2, 2],
                                        padding="SAME", strides=2)
        dropout1 = tf.layers.dropout(inputs=pool1, rate=0.3, training=self.training)

        # convolutional layer 2 & pooling layer 2
        conv2 = tf.layers.conv2d(inputs=dropout1, filters=64, kernel_size=[3, 3],
                                padding="SAME", activation=tf.nn.relu)
        pool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2],
                                       padding="SAME", strides=2)
        dropout2 = tf.layers.dropout(inputs=pool2, rate=0.3, training=self.training)

        # convolutional layer 3 & pooling layer 3
        conv3 = tf.layers.conv2d(inputs=dropout2, filters=128, kernel_size=[3, 3],
                                padding="SAME", activation=tf.nn.relu)
        pool3 = tf.layers.max_pooling2d(inputs=conv3, pool_size=[2, 2],
                                       padding="SAME", strides=2)
        dropout3 = tf.layers.dropout(inputs=pool3, rate=0.3, training=self.training)

        # dense layer with Relu
        flat = tf.reshape(dropout3, [-1, 128 * 4 * 4])
        dense4 = tf.layers.dense(inputs=flat, units=625, activation=tf.nn.relu)
        dropout4 = tf.layers.dropout(inputs=dense4, rate=0.5, training=self.training)

        # FC layer 625 input -> 10 output, no activation function
        self.logits = tf.layers.dense(inputs=dropout4, units=10)

        # define loss & optimizer
        self.cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(
                                   logits = self.logits, labels=self.Y))
        self.optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(self.cost)

        # accuracy
        correct_prediction = tf.equal(tf.argmax(self.logits, 1), tf.argmax(self.Y, 1))
        self.accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
        
    def train(self, x_data, y_data, training=True):
        return self.sess.run([self.cost, self.optimizer], 
                             feed_dict={self.X: x_data, self.Y: y_data, self.training: training})
    
    def predict(self, x_test, training=False):
        return self.sess.run(self.logits, 
                             feed_dict={self.X :x_test, self.training: training})

    def get_accuracy(self, x_test, y_test, training=False):
        return self.sess.run(self.accuracy, 
                             feed_dict={self.X: x_test,self.Y: y_test, self.training: training})
    

# train the models
with tf.Session() as sess:
    models = []
    num_models = 2
    
    for m in range(num_models):
        models.append(Model(sess, "modal"+str(m)))
        
    sess.run(tf.global_variables_initializer())
    
    print('Learning Start!')
    
    for epoch in range(training_epoches):
        avg_cost_list = np.zeros(len(models))
        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)
            
            # train each modal
            for m_id, m in enumerate(models):
                
                c, _ = m.train(batch_xs, batch_ys)
                avg_cost_list[m_id] += c / total_batch
        
        print('Epoch: ', '%04d' %(epoch+1), 'cost=', avg_cost_list)
        
    print('Learning finished!')

    # test & accuracy                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               
    test_size = len(mnist.test.labels)
    predictions = np.zeros([test_size, 10])

    for m_id,m in enumerate(models):
        print(m_id, "Accuracy:", m.get_accuracy(mnist.test.images, mnist.test.labels))
        p = m.predict(mnist.test.images)
        predictions += p

    ensemble_correct_prediction = tf.equal(tf.argmax(predictions, 1), tf.argmax(mnist.test.labels, 1))
    ensemble_accuracy = tf.reduce_mean(tf.cast(ensemble_correct_prediction, tf.float32))
    print("Ensemble_accuracy:", sess.run(ensemble_accuracy))

Learning Start!
Epoch:  0001 cost= [0.29211415 0.28355632]
Epoch:  0002 cost= [0.08716567 0.0870499 ]
Epoch:  0003 cost= [0.06902521 0.06623169]
Epoch:  0004 cost= [0.05563359 0.05452387]
Epoch:  0005 cost= [0.04963774 0.04871382]
Epoch:  0006 cost= [0.04462749 0.04449957]
Epoch:  0007 cost= [0.04132144 0.03907955]
Epoch:  0008 cost= [0.03792324 0.03861412]
Epoch:  0009 cost= [0.0354344  0.03323769]
Epoch:  0010 cost= [0.03516847 0.03405525]
Epoch:  0011 cost= [0.03143759 0.03219781]
Epoch:  0012 cost= [0.03051504 0.02993162]
Epoch:  0013 cost= [0.02906878 0.02711077]
Epoch:  0014 cost= [0.02729127 0.02754832]
Epoch:  0015 cost= [0.02729633 0.02632647]
Epoch:  0016 cost= [0.02438517 0.02701174]
Epoch:  0017 cost= [0.02482958 0.0244114 ]
Epoch:  0018 cost= [0.02455271 0.02649499]
Epoch:  0019 cost= [0.02371975 0.02178147]
Epoch:  0020 cost= [0.02260135 0.0213784 ]
Learning finished!
0 Accuracy: 0.995
1 Accuracy: 0.9949
Ensemble_accuracy: 0.9954