tensorflow实战Google深度学习框架 第292页的程序 完整版 以及计算图可视化

书上给的程序 省略了一些代码

import os
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import numpy as np
import mnist_inference

tf.reset_default_graph()

INPUT_NODE=784
OUTPUT_NODE=10
LAYER1_NODE=500

BATCH_SIZE=100
LEARNING_RATE_BASE=0.8
LEARNING_RATE_DECAY=0.99

REGULAZATION_RATE=0.0001
TRAINING_STEPS=30000
MOVING_AVERAGE_DECAY=0.99
MODEL_SAVE_PATH="model/"
MODEL_NAME="model.ckpt"

def train(mnist):
#处理输入数据放在input命名空间下
with tf.name_scope('input'):
x=tf.placeholder(
tf.float32,
[None,mnist_inference.INPUT_NODE],
name='x-input')
y_=tf.placeholder(
tf.float32,
[None,mnist_inference.OUTPUT_NODE],
name='y-input')
#正则化的作用：防止过拟合
regularizer=tf.contrib.layers.l2_regularizer(REGULAZATION_RATE)

y=mnist_inference.inference(x,regularizer)
global_step=tf.Variable(0,trainable=False)

with tf.name_scope("moving_average"):
#平均平滑：提高泛化能力
variable_averages=tf.train.ExponentialMovingAverage(
MOVING_AVERAGE_DECAY,global_step)
#平均平滑的操作
variable_averages_op=variable_averages.apply(tf.trainable_variables())

with tf.name_scope("loss_function"):
cross_entropy=tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=y,labels=tf.argmax(y_,1))

#交叉熵平均
cross_entropy_mean=tf.reduce_mean(cross_entropy)
loss=cross_entropy_mean +tf.add_n(tf.get_collection('losses'))

#将定义学习率、优化方法以及每一轮训练需要执行的操作都放在名字为train_step的命名空间下

with tf.name_scope('train_step'):
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')
saver=tf.train.Saver()
with tf.Session() as sess:
tf.global_variables_initializer().run()

for i in range(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 steps,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("MNIST_data/",one_hot=True)
train(mnist)
if __name__=='__main__':
tf.app.run()