一,mnist_forward.py
#coding:utf-8
#0导入模块,生成模拟数据集
import tensorflow as tf
INPUT_NODE=784 #输入层节点
OUTPUT_NODE=10 #输出层节点
LAYER1_NODE=500 #隐藏层节点
#定义神经网络的输入、参数和输出,定义前向传播过程
def get_weight(shape,regularizer):
w=tf.Variable(tf.truncated_normal(shape,stddev=0.1))
if regularizer!=None:tf.add_to_collection('losses',tf.contrib.layers.l2_regularizer(regularizer)(w))
return w
def get_bias(shape):
b=tf.Variable(tf.zeros(shape))
return b
def forward(x,regularizer):
w1 = get_weight([INPUT_NODE, LAYER1_NODE], regularizer)
b1 = get_bias([LAYER1_NODE])
y1 = tf.nn.relu(tf.matmul(x, w1) + b1)
w2 = get_weight([LAYER1_NODE, OUTPUT_NODE], regularizer)
b2 = get_bias([OUTPUT_NODE])
y = tf.matmul(y1, w2) + b2 # 输出层不过激活
return y
二,mnist_backward.py
#coding:utf-8
#0导入模块,生成模拟数据集
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import mnist_forward
import os
BATCH_SIZE=200
LEARNING_RATE_BASE=0.1 #最初学习率
LEARNING_RATE_DECAY=0.99 #学习率衰减率
REGULARIZER=0.0001 #正则化系数
STEPS=50000 #训练多少轮
MOVING_AVERAGE_DECAY=0.99 #滑动平均衰减率
MODEL_SAVE_PATH="./model" #模型保存路径
MODEL_NAME="mnist_model" #模型名称
def backward(mnist):
x = tf.placeholder(tf.float32, [None,mnist_forward.INPUT_NODE])
y_ = tf.placeholder(tf.float32,[None,mnist_forward.OUTPUT_NODE])
y=mnist_forward.forward(x,REGULARIZER)
# 运行了几轮BATCH_SIZE的计数器,初值给0,设为不被训练
global_step = tf.Variable(0, trainable=False)
# 定义损失函数
ce=tf.nn.sparse_softmax_cross_entropy_with_logits(logits=y,labels=tf.argmax(y_,1))
cem=tf.reduce_mean(ce)
loss=cem+tf.add_n(tf.get_collection('losses'))
# 定义指数下降学习率
learning_rate = tf.train.exponential_decay(
LEARNING_RATE_BASE,
global_step,
mnist.train.num_examples/BATCH_SIZE,
LEARNING_RATE_DECAY,
staircase=True)
# 定义反向传播方法;不含正则化
train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss,global_step=global_step)
ema=tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY,global_step)
ema_op=ema.apply(tf.trainable_variables())
with tf.control_dependencies([train_step,ema_op]):
train_op=tf.no_op(name='train')
saver=tf.train.Saver()
with tf.Session() as sess:
init_op = tf.global_variables_initializer()
sess.run(init_op)
for i in range(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():
mnist=input_data.read_data_sets('./data/',one_hot=True)
backward(mnist)
if __name__=='__main__':
main()
三,
#coding:utf-8
import time
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import mnist_forward
import mnist_backward
TEST_INTERVAL_SECS=5 #程序循环的间隔时间 5s
def test(mnist): #读入mnist数据集
with tf.Graph().as_default() as g: #复现计算图
#初始化
x = tf.placeholder(tf.float32, [None, mnist_forward.INPUT_NODE])
y_ = tf.placeholder(tf.float32, [None, mnist_forward.OUTPUT_NODE])
#前向传播计算y的值
y = mnist_forward.forward(x, None)
#实例化带滑动平均的saver对象,这样所有参数被加载时,都会被赋值为各自的滑动平均值
ema=tf.train.ExponentialMovingAverage(mnist_backward.MOVING_AVERAGE_DECAY)
ema_restore=ema.variables_to_restore()
saver=tf.train.Saver(ema_restore)
#计算准确率
correct_prediction=tf.equal(tf.argmax(y,1),tf.argmax(y_,1))
accuracy=tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
while True:
with tf.Session() as sess:
#加载ckpt,即将滑动平均值赋值给各个参数
ckpt=tf.train.get_checkpoint_state(mnist_backward.MODEL_SAVE_PATH)
#判断有没有模型,如果有,先恢复模型到当前会话
if ckpt and ckpt.model_checkpoint_path:
#先恢复模型到当前会话
saver.restore(sess,ckpt.model_checkpoint_path)
#恢复global_step值
global_step=ckpt.model_checkpoint_path.split('/')[-1].split("-")[-1]
#执行准确率计算
accuracy_score=sess.run(accuracy,feed_dict={x:mnist.test.images,y_:mnist.test.labels})
print("After %s training step(s), test accuracy=%g"%(global_step,accuracy_score))
else:
print("No checkpoint file found") #未找到模型
return
time.sleep(TEST_INTERVAL_SECS)
def main():
mnist=input_data.read_data_sets('./data/',one_hot=True) #读入数据集
test(mnist) #执行test函数
if __name__=='__main__':
main()