TensorFlow学習ノート(5)--全接続ニューラルネットワークを訓練する

全接続ニューラルネットワークによるMNIST問題の解決
ここでのモデルは、スライド平均のモデルコードと私のコメントを使用します.

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
'''
mnist = input_data.read_data_sets("path/to/MNIST_data", one_hot=True)
print(mnist.train.num_examples)
print(mnist.validation.num_examples)
print(mnist.test.num_examples)
print(mnist.train.images[0])
print(mnist.train.labels[0])
'''

#MNIST       
INPUT_NODE=784
OUTPUT_NODE=10

#        
LAYER1_NODE=500
BATCH_SIZE=100

LEARNING_RATE_BASE=0.8
LEARNING_RATE_DECAY=0.99
REGULARIZATION_RATE=0.0001
TRAINING_STEPS=30000
MOVING_AVERAGE_DECAY=0.99

#      ，              ，             。     ReLU              。
def inference(input_tensor, avg_class, weights1, biases1, weights2, biases2):
    #           
    if avg_class==None:
        layer1 = tf.nn.relu(tf.matmul(input_tensor, weights1)+biases1)

        return tf.matmul(layer1, weights2)+biases2
    else:
        layer1 = tf.nn.relu(tf.matmul(input_tensor, avg_class.average(weights1))+
                            avg_class.average(biases1))
        return tf.matmul(layer1, avg_class.average(weights2))+avg_class.average(biases2)

#       
def train(mnist):
#       
    x=tf.placeholder(tf.float32, [None, INPUT_NODE], name='x-input')
    y_=tf.placeholder(tf.float32, [None, OUTPUT_NODE], name='y-input')
#       
    weights1=tf.Variable(tf.truncated_normal([INPUT_NODE, LAYER1_NODE], stddev=0.1))
    biases1=tf.Variable(tf.constant(0.1, shape=[LAYER1_NODE]))

    weights2 = tf.Variable(tf.truncated_normal([LAYER1_NODE, OUTPUT_NODE], stddev=0.1))
    biases2 = tf.Variable(tf.constant(0.1, shape=[OUTPUT_NODE]))

    y = inference(x, None, weights1, biases1, weights2, biases2)
    #   y         ，          
    global_step = tf.Variable(0, trainable=False)#                 

    #     ，         
    variable_averages=tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY, global_step)

    variable_averages_op = variable_averages.apply(tf.trainable_variables())
    average_y=inference(x, variable_averages, weights1, biases1, weights2, biases2)

    cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=y, labels=tf.argmax(y_, 1))
#      ，                  y
#         ，      logits labels，      ，logits     ，labels     
    cross_entropy_mean = tf.reduce_mean(cross_entropy)

    #L2   
    regularizer = tf.contrib.layers.l2_regularizer(REGULARIZATION_RATE)#    
    regularization = regularizer(weights1) + regularizer(weights2)
    loss = cross_entropy_mean + regularization

    #          
    learning_rate = tf.train.exponential_decay(
        LEARNING_RATE_BASE,
        global_step,
        mnist.train.num_examples / BATCH_SIZE,
        LEARNING_RATE_DECAY)

    #          
    train_step=tf.train.GradientDescentOptimizer(learning_rate).minimize(loss, global_step=global_step)

    with tf.control_dependencies([train_step, variable_averages_op]):
        train_op=tf.no_op("train")
#tf.control_dependencies([a,b]): c=tf.no_op() c=tf.group(a,b)  ，          ，     

    correct_prediction=tf.equal(tf.arg_max(average_y, 1), tf.argmax(y_,1))
    accuracy=tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

    #            ，     ，         
    with tf.Session() as sess:
        tf.initialize_all_variables().run()

        #    
        validated_feed={x: mnist.validation.images, y_: mnist.validation.labels}

        #    
        test_feed = {x: mnist.test.images, y_: mnist.test.labels}

        #    
        for i in range(TRAINING_STEPS):
            # 1000                 
            if i % 1000 ==0:
                validate_acc = sess.run(accuracy, feed_dict=validated_feed)
                print("After %d training step(s), validation accuracy using average model is %g" %(i, validate_acc))

            #         batch     ，       
            xs, ys = mnist.train.next_batch(BATCH_SIZE)
            sess.run(train_op, feed_dict={x:xs, y_:ys})

        #            
        test_acc = sess.run(accuracy, feed_dict=test_feed)
        print("After %d training step(s), test accuracy using average model is %g" %(TRAINING_STEPS, test_acc))

def main(argv=None):
        mnist = input_data.read_data_sets("path/to/MNIST_data", one_hot=True)#      
        train(mnist)#    

#tf        ，tf.app.run()     main  
if __name__ == '__main__':   
        tf.app.run()