Tensorflow实战（ 四）经典卷积神经网络之实现AlexNet

重点：

实验代码：

# -*- coding: utf-8 -*-
"""
Created on Fri Jan 19 21:04:52 2018

@author: hui
"""

from datetime import datetime
import math
import time
import tensorflow as tf
batch_size=32
num_batches=100

def print_avtivations(t):
print(t.op.name,' ',t.get_shape().as_list())
def interfence(images):
parameters=[]
with tf.name_scope('conv1') as scope:
kernel=tf.Variable(tf.truncated_normal([11,11,3,64],
dtype=tf.float32,stddev=1e-1),
name='weights')
conv=tf.nn.conv2d(images,kernel,[1,4,4,1],padding='SAME')
biases=tf.Variable(tf.constant(0.0,shape=[64],dtype=tf.float32),
trainable=True,name='biases')
bias=tf.nn.bias_add(conv,biases)
conv1=tf.nn.relu(bias,name=scope)
print_avtivations(conv1)
parameters+=[kernel,biases]
lrn1=tf.nn.lrn(conv1,4,bias=1.0,alpha=0.001/9,beta=0.75,name='lrn1')
pool1=tf.nn.max_pool(lrn1,ksize=[1,3,3,1],strides=[1,2,2,1],
padding='VALID',name='pool1')
print_avtivations(pool1)

with tf.name_scope('conv2') as scope:
kernel=tf.Variable(tf.truncated_normal([5,5,64,192],
dtype=tf.float32,
stddev=1e-1),
name='weights')
conv=tf.nn.conv2d(pool1,kernel,[1,1,1,1],padding='SAME')
biases=tf.Variable(tf.constant(0.0,shape=[192],dtype=tf.float32),
trainable=True,name='biases')
bias=tf.nn.bias_add(conv,biases)
conv2=tf.nn.relu(bias,name=scope)
parameters+=[kernel,biases]
print_avtivations(conv2)

lrn2=tf.nn.lrn(conv2,4,bias=1.0,alpha=0.001/9,beta=0.75,name='lrn2')
pool2=tf.nn.max_pool(lrn2,ksize=[1,3,3,1],strides=[1,2,2,1],
padding='VALID',name='pool2')
print_avtivations(pool2)

with tf.name_scope('conv3') as scope:
kernel=tf.Variable(tf.truncated_normal([3,3,192,384],
dtype=tf.float32,
stddev=1e-1),
name='weights')
conv=tf.nn.conv2d(pool2,kernel,[1,1,1,1],padding='SAME')
biases=tf.Variable(tf.constant(0.0,shape=[384],dtype=tf.float32),
trainable=True,name='biases')
bias=tf.nn.bias_add(conv,biases)
conv3=tf.nn.relu(bias,name=scope)
parameters+=[kernel,biases]
print_avtivations(conv3)

with tf.name_scope('conv4') as scope:
kernel=tf.Variable(tf.truncated_normal([3,3,384,256],
dtype=tf.float32,
stddev=1e-1),
name='weights')
conv=tf.nn.conv2d(conv3,kernel,[1,1,1,1],padding='SAME')
biases=tf.Variable(tf.constant(0.0,shape=[256],dtype=tf.float32),
trainable=True,name='biases')
bias=tf.nn.bias_add(conv,biases)
conv4=tf.nn.relu(bias,name=scope)
parameters+=[kernel,biases]
print_avtivations(conv4)

with tf.name_scope('conv5') as scope:
kernel=tf.Variable(tf.truncated_normal([3,3,256,256],
dtype=tf.float32,
stddev=1e-1),
name='weights')
conv=tf.nn.conv2d(conv4,kernel,[1,1,1,1],padding='SAME')
biases=tf.Variable(tf.constant(0.0,shape=[256],dtype=tf.float32),
trainable=True,name='biases')
bias=tf.nn.bias_add(conv,biases)
conv5=tf.nn.relu(bias,name=scope)
parameters+=[kernel,biases]
print_avtivations(conv5)
pool5=tf.nn.max_pool(conv5,ksize=[1,3,3,1],strides=[1,2,2,1],
padding='VALID',name='pool5')
print_avtivations(pool5)
return pool5,parameters

def time_tensorflow_run(session,target,info_string):
num_steps_brun_in=10
total_duration=0.0
total_duration_squared=0.0
for i in range(num_batches+num_steps_brun_in):
start_time=time.time()
_=session.run(target)
duration=time.time()-start_time
if i>=num_steps_brun_in:
if not i%10:
print('%s:step %d,duration=%3f'%(datetime.now(),
i-num_steps_brun_in,
duration))
total_duration+=duration
total_duration_squared+=duration*duration
mn=total_duration/num_batches
vr=total_duration_squared/num_batches-mn*mn
sd=math.sqrt(vr)
print('%s: %s across %d steps,%.3f+-%.3f sec/batch'%(datetime.now(),
info_string,
num_batches,
mn,
sd))
def run_benchmark():
with tf.Graph().as_default():
image_size=224
images=tf.Variable(tf.random_normal([batch_size,image_size,image_size,3],
dtype=tf.float32,stddev=1e-1))
pool5,parameters=interfence(images)
init=tf.global_variables_initializer()
sess=tf.Session()
sess.run(init)
time_tensorflow_run(sess,pool5,"forward")
objective=tf.nn.l2_loss(pool5)
grad=tf.gradients(objective,parameters)
time_tensorflow_run(sess,grad,"forward-backward")
run_benchmark()

运行结果：

调试错误：