mnist_inference.py：
# -*- coding:utf-8 -*- import tensorflow as tf # 配置神经网络参数 INPUT_NODE=784 OUTPUT_NODE=10 IMAGE_SIZE=28 NUM_CHANNELS=1 NUM_LABELS=10 # 第一层卷积层的尺寸和深度 CONV1_DEEP=32 CONV1_SIZE=5 # 第二层卷积层的尺寸和深度 CONV2_DEEP=64 CONV2_SIZE=5 # 全连接层的节点个数 FC_SIZE=512 def inference(input_tensor,train,regularizer): # 输入：28×28×1，输出：28×28×32 with tf.variable_scope(‘layer1-conv1‘): conv1_weights=tf.get_variable(‘weights‘,[CONV1_SIZE,CONV1_SIZE,NUM_CHANNELS,CONV1_DEEP], initializer=tf.truncated_normal_initializer(stddev=0.1)) conv1_biases=tf.get_variable(‘biases‘,[CONV1_DEEP],initializer=tf.constant_initializer(0.0)) # 使用尺寸为5，深度为32的过滤器，步长为1，使用全0填充 conv1=tf.nn.conv2d(input_tensor,conv1_weights,strides=[1,1,1,1],padding=‘SAME‘) relu1=tf.nn.relu(tf.nn.bias_add(conv1,conv1_biases)) # 输入：28×28×32，输出：14×14×32 with tf.name_scope(‘layer2-pool1‘): pool1=tf.nn.max_pool(relu1,ksize=[1,2,2,1],strides=[1,2,2,1],padding=‘SAME‘) # 输入：14×14×32，输出：14×14×64 with tf.variable_scope(‘layer3-conv2‘): conv2_weights=tf.get_variable(‘weights‘,[CONV2_SIZE,CONV2_SIZE,CONV1_DEEP,CONV2_DEEP], initializer=tf.truncated_normal_initializer(stddev=0.1)) conv2_biases=tf.get_variable(‘biases‘,[CONV2_DEEP],initializer=tf.constant_initializer(0.0)) # 使用尺寸为5，深度为64的过滤器，步长为1，使用全0填充 conv2=tf.nn.conv2d(pool1,conv2_weights,strides=[1,1,1,1],padding=‘SAME‘) relu2=tf.nn.relu(tf.nn.bias_add(conv2,conv2_biases)) # 输入：14×14×64，输出：7×7×64 with tf.name_scope(‘layer4-pool2‘): pool2=tf.nn.max_pool(relu2,ksize=[1,2,2,1],strides=[1,2,2,1],padding=‘SAME‘) # 将7×7×64的矩阵转换成一个向量，因为每一层神经网络的输入输出都为一个batch矩阵，所以这里得到的维度 # 也包含了一个batch中数据的个数（batch×7×7×64 --> batch×vector) pool_shape=pool2.get_shape().as_list() # pool_shape[0]为一个batch中数据的个数 nodes=pool_shape[1]*pool_shape[2]*pool_shape[3] # 通过tf.reshape函数将第四层的输出变成一个batch的向量 reshaped=tf.reshape(pool2,[pool_shape[0],nodes]) with tf.variable_scope(‘layer5-fc1‘): fc1_weights=tf.get_variable(‘weights‘,[nodes,FC_SIZE],initializer=tf.truncated_normal_initializer(stddev=0.1)) # 只有全连接层的权重需要加入正则化 if regularizer != None: tf.add_to_collection(‘losses‘,regularizer(fc1_weights)) fc1_biases=tf.get_variable(‘biases‘,[FC_SIZE],initializer=tf.constant_initializer(0.1)) fc1=tf.nn.relu(tf.matmul(reshaped,fc1_weights)+fc1_biases) if train: fc1=tf.nn.dropout(fc1,0.5) with tf.variable_scope(‘layer6-fc2‘): fc2_weights=tf.get_variable(‘weights‘,[FC_SIZE,NUM_LABELS],initializer=tf.truncated_normal_initializer(stddev=0.1)) if regularizer != None: tf.add_to_collection(‘losses‘,regularizer(fc2_weights)) fc2_biases=tf.get_variable(‘biases‘,[NUM_LABELS],initializer=tf.constant_initializer(0.1)) logit=tf.matmul(fc1,fc2_weights)+fc2_biases return logit
mnist_train.py：
# -*- coding:utf-8 -*- import os import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data import numpy as np import mnist_inference # 配置神经网络的参数 BATCH_SIZE=100 LEARNING_RATE_BASE=0.01 LEARNING_RATE_DECAY=0.99 REGULARAZTION_RATE=0.0001 TRAINING_STEPS=6000 MOVING_AVERAGE_DECAY=0.99 # 模型保存的路径和文件名 MODEL_SAVE_PATH=‘log/‘ MODEL_NAME=‘model.ckpt‘ def train(mnist): # 定义输入输出placeholder x=tf.placeholder(tf.float32,[BATCH_SIZE,mnist_inference.IMAGE_SIZE,mnist_inference.IMAGE_SIZE,mnist_inference.NUM_CHANNELS],name=‘x-input‘) y_=tf.placeholder(tf.float32,[None,mnist_inference.OUTPUT_NODE],name=‘y-input‘) regularizer=tf.contrib.layers.l2_regularizer(REGULARAZTION_RATE) # 直接使用mnist_inference.py 中定义的前向传播过程。 y=mnist_inference.inference(x,False,regularizer) global_step=tf.Variable(0,trainable=False) # 定义损失函数、学习率、滑动平均操作以及训练过程。 variable_averages=tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY,global_step) variable_averages_op=variable_averages.apply(tf.trainable_variables()) cross_entropy=tf.nn.sparse_softmax_cross_entropy_with_logits(logits=y,labels=tf.argmax(y_,1)) cross_entropy_mean=tf.reduce_mean(cross_entropy) loss=cross_entropy_mean+tf.add_n(tf.get_collection(‘losses‘)) learning_rate=tf.train.exponential_decay(LEARNING_RATE_BASE,global_step, mnist.train.num_examples/BATCH_SIZE,LEARNING_RATE_DECAY,staircase=True) 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(name=‘train‘) # 初始化Tensorflow持久化类 saver=tf.train.Saver() with tf.Session() as sess: tf.global_variables_initializer().run() # 在训练时不在测试模型的在验证数据上的表现，验证和测试的过程将会用一个独立的程序来完成 for i in range(TRAINING_STEPS): xs,ys=mnist.train.next_batch(BATCH_SIZE) reshaped_xs=np.reshape(xs,(BATCH_SIZE,mnist_inference.IMAGE_SIZE,mnist_inference.IMAGE_SIZE,mnist_inference.NUM_CHANNELS)) _,loss_value,step=sess.run([train_op,loss,global_step],feed_dict={x:reshaped_xs,y_:ys}) # 每1000轮保存一次模型 if i % 1000 == 0: print(‘After {} training step(s), loss on training batch is {}.‘.format(step,loss_value)) # 这里给出了global_step参数，可以在每个被保存模型的文件名末尾加上训练的轮数 saver.save(sess,os.path.join(MODEL_SAVE_PATH,MODEL_NAME),global_step=global_step) def main(argv=None): mnist=input_data.read_data_sets(‘.‘,one_hot=True) train(mnist) if __name__ == ‘__main__‘: tf.app.run()