Tensorflow：实战Google深度学习框架_第5章（已经过修改）

  1 import tensorflow as tf
  2 from tensorflow.examples.tutorials.mnist import input_data
  3 
  4 INPUT_NODE = 784
  5 OUTPUT_NODE = 10
  6 
  7 # 1. 定义神经网络结构相关的参数
  8 LAYER1_NODE = 500
  9 
 10 BATCH_SIZE = 100
 11 
 12 LEARNING_RATE_BASE = 0.8
 13 LEARNING_RATE_DECAY = 0.99
 14 REGULARIZATION_RATE = 0.0001
 15 TRAINING_STEPS = 30000
 16 MOVING_AVERAGE_DECAY = 0.99
 17 
 18 def inference(input_tensor, avg_class, weights1, biases1, weights2, biases2):
 19     
 20     if avg_class == None:
 21         
 22         layer1 = tf.nn.relu(tf.matmul(input_tensor, weights1) + biases1)
 23         
 24         return tf.matmul(layer1, weights2) + biases2
 25     
 26     else:
 27         
 28         
 29         layer1 = tf.nn.relu(tf.matmul(input_tensor, avg_class.average(weights1)) + 
 30                             avg_class.average(biases1))
 31         return tf.matmul(layer1, avg_class.average(weights2)) + 
　　　　　　　　　　　　avg_class.average(biases2)
 32 
 33 # 2. 定义训练过程
 34 def train(mnist):
 35     # 定义输入输出placeholder。
 36     x = tf.placeholder(tf.float32, [None, INPUT_NODE], name=\'x-input\')
 37     y_ = tf.placeholder(tf.float32, [None, OUTPUT_NODE], name=\'y-input\')
 38     
 39     weights1 = tf.Variable(tf.truncated_normal([INPUT_NODE, LAYER1_NODE], stddev=0.1))
 40     biases1 = tf.Variable(tf.constant(0.1, shape=[LAYER1_NODE]))
 41     
 42     weights2 = tf.Variable(tf.truncated_normal([LAYER1_NODE, OUTPUT_NODE], stddev=0.1))
 43     biases2 = tf.Variable(tf.constant(0.1, shape=[OUTPUT_NODE]))
 44     
 45     y = inference(x, None, weights1, biases1, weights2, biases2)
 46     global_step = tf.Variable(0, trainable=False)
 47 
 48     # 定义损失函数、学习率、滑动平均操作以及训练过程。
 49     variable_averages = tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY, 
　　　　　　　　　　　　　　　　　　　　global_step)
 50     
 51     variables_averages_op = variable_averages.apply(tf.trainable_variables())
 52     
 53     average_y = inference(x, variable_averages, weights1, biases1, weights2, biases2)
 54     
 55     cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits = y, 
　　　　　　　　　　　　　　　　labels = tf.argmax(y_, 1))
 56     cross_entropy_mean = tf.reduce_mean(cross_entropy)
 57     
 58     regularizer = tf.contrib.layers.l2_regularizer(REGULARIZATION_RATE)
 59     regularization = regularizer(weights1) + regularizer(weights2)
 60     
 61     loss = cross_entropy_mean + regularization
 62     
 63     learning_rate = tf.train.exponential_decay(
 64         LEARNING_RATE_BASE,
 65         global_step,
 66         mnist.train.num_examples / BATCH_SIZE, LEARNING_RATE_DECAY)
 67     
 68     train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss, 
　　　　　　　　　　　　　　global_step=global_step)
 69     with tf.control_dependencies([train_step, variables_averages_op]):
 70         train_op = tf.no_op(name=\'train\')
 71 
 72     correct_prediction = tf.equal(tf.argmax(average_y, 1), tf.argmax(y_,1))
 73     accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
 74 
 75     # 初始化TensorFlow持久化类。
 76     with tf.Session() as sess:
 77         tf.global_variables_initializer().run()
 78         validate_feed = {x: mnist.validation.images,
 79                          y_: mnist.validation.labels}
 80         test_feed = {x: mnist.test.images, y_: mnist.test.labels}
 81         
 82         for i in range(TRAINING_STEPS):
 83             if i % 1000 == 0:
 84                 validate_acc = sess.run(accuracy, feed_dict = validate_feed)
 85                 print("After %d training step(s), validation accuracy"
 86                       "using average model is %g " % (i, validate_acc))
 87                 
 88             xs, ys = mnist.train.next_batch(BATCH_SIZE)
 89             sess.run(train_op, feed_dict={x: xs, y_: ys})
 90         
 91         test_acc = sess.run(accuracy, feed_dict=test_feed)
 92         print("After %d training step(s), test accuracy using average"
 93               "model is %g" % (TRAINING_STEPS, test_acc))
 94 
 95 # 3. 主程序入口
 96 def main(argv=None):
 97     mnist = input_data.read_data_sets("/tmp/data", one_hot=True)
 98     train(mnist)
 99 
100 if __name__ == \'__main__\':
101     tf.app.run()