importosimporttensorflow as tffrom tensorflow.examples.tutorials.mnist importinput_data

INPUT_NODE= 784OUTPUT_NODE= 10LAYER1_NODE= 500

defget_weight_variable(shape, regularizer):

weights= tf.get_variable("weights", shape, initializer=tf.truncated_normal_initializer(stddev=0.1))if(regularizer !=None):

tf.add_to_collection('losses', regularizer(weights))returnweightsdefinference(input_tensor, regularizer):

with tf.variable_scope('layer1'):

weights=get_weight_variable([INPUT_NODE, LAYER1_NODE], regularizer)

biases= tf.get_variable("biases", [LAYER1_NODE], initializer=tf.constant_initializer(0.0))

layer1= tf.nn.relu(tf.matmul(input_tensor, weights) +biases)

with tf.variable_scope('layer2'):

weights=get_weight_variable([LAYER1_NODE, OUTPUT_NODE], regularizer)

biases= tf.get_variable("biases", [OUTPUT_NODE], initializer=tf.constant_initializer(0.0))

layer2= tf.matmul(layer1, weights) +biasesreturnlayer2

BATCH_SIZE= 100LEARNING_RATE_BASE= 0.8LEARNING_RATE_DECAY= 0.99REGULARIZATION_RATE= 0.0001TRAINING_STEPS= 30000MOVING_AVERAGE_DECAY= 0.99MODEL_SAVE_PATH= "F:\\TensorFlowGoogle\\201806-github\\datasets\\MNIST_data\\"MODEL_NAME= "mnist_model"

deftrain(mnist):#定义输入输出placeholder。

x = tf.placeholder(tf.float32, [None, INPUT_NODE], name='x-input')

y_= tf.placeholder(tf.float32, [None, OUTPUT_NODE], name='y-input')

regularizer=tf.contrib.layers.l2_regularizer(REGULARIZATION_RATE)

y=inference(x, 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)

variables_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, variables_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()for i inrange(TRAINING_STEPS):

xs, ys=mnist.train.next_batch(BATCH_SIZE)

_, loss_value, step= sess.run([train_op, loss, global_step], feed_dict={x: xs, y_: ys})if i % 1000 ==0:print("After %d training step(s), loss on training batch is %g." %(step, loss_value))

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("F:\\TensorFlowGoogle\\201806-github\\datasets\\MNIST_data\\", one_hot=True)

train(mnist)if __name__ == '__main__':

main()