一、引入
1.发展
2.特点
（1）全部使用3×3的卷积核，步长1；和2×2的池化核，步长2；通过不断加深网络结构提升性能。
（2）两个3×3卷积层串联优于一个7×7卷积层，使得CNN对特征学习能力更强。
（3）不用LRN层；越深层网络效果越好。
二、tensorflow实现
1.实现计算前向预测和反向测评时间
（1）导入
from datetime import datetime
import math
import time
import tensorflow as tf（2）设置参数
#设置参数
batch_size = 32 #每个batch数据的大小
num_batches = 50 #一共测试50个batch数据（3）封装VGG所用函数
def conv_op(input, name, wh, ww, n_out, dh, dw, p): #输入，范围名称，滤波器尺寸，滤波器输出通道数，步长，参数列表
n_in = input.get_shape()[-1].value #输入通道数
with tf.name_scope(name) as scope:
weight = tf.get_variable(scope+"w", shape=[wh, ww, n_in, n_out], dtype=tf.float32,
initializer=tf.contrib.layers.xavier_initializer_conv2d()) #滤波器
conv = tf.nn.conv2d(input, weight, (1, dw, dw, 1),padding='SAME') #步长 必须是边界填充SAME
bias_init_val = tf.constant(0.0, shape=[n_out],dtype=tf.float32)
biases = tf.Variable(bias_init_val, trainable=True, name='b') #偏置
result = tf.nn.bias_add(conv, biases) #前向
activation = tf.nn.relu(result, name=scope) #非线性激活
p += [weight, biases] #参数
return activation
def pool_op(input, name, wh, ww, dh, dw):
pool = tf.nn.max_pool(input, ksize=[1, wh, ww, 1], strides=[1, dw, dh, 1], padding='VALID', name=name) #池化 尺寸 步长 必须是不填充VALID
return pool
def fc_op(input, name, n_out, p): #全连接
n_in = input.get_shape()[-1].value #通道数
with tf.name_scope(name) as scope:
weight = tf.get_variable(scope+"w", shape=[n_in, n_out], dtype=tf.float32,
initializer=tf.contrib.layers.xavier_initializer())
bias_init_val = tf.constant(0.1, shape=[n_out], dtype=tf.float32)
biases = tf.Variable(bias_init_val, name='b')
activation = tf.nn.relu_layer(input, weight, biases, name=scope)
p += [weight, biases]
return activation
def inference(images, keep_prob):
parameters = [] #参数
#卷积1
conv1_1 = conv_op(images, name='conv1_1', wh=3, ww=3, n_out=64, dh=1, dw=1, p=parameters)
conv1_2 = conv_op(conv1_1, name='conv1_2', wh=3, ww=3, n_out=64, dh=1, dw=1, p=parameters)
pool1 = pool_op(conv1_2, name='pool1', wh=2, ww=2, dh=2, dw=2)
#卷积2
conv2_1 = conv_op(pool1, name='conv2_1', wh=3, ww=3, n_out=128, dh=1, dw=1, p=parameters)
conv2_2 = conv_op(conv2_1, name='conv2_1', wh=3, ww=3, n_out=128, dh=1, dw=1, p=parameters)
pool2 = pool_op(conv2_2, name='pool2', wh=2, ww=2, dh=2, dw=2)
# 卷积3
conv3_1 = conv_op(pool2, name='conv3_1', wh=3, ww=3, n_out=256, dh=1, dw=1, p=parameters)
conv3_2 = conv_op(conv3_1, name='conv3_1', wh=3, ww=3, n_out=256, dh=1, dw=1, p=parameters)
conv3_3 = conv_op(conv3_2, name='conv3_1', wh=3, ww=3, n_out=256, dh=1, dw=1, p=parameters)
pool3 = pool_op(conv3_3, name='pool3', wh=2, ww=2, dh=2, dw=2)
# 卷积4
conv4_1 = conv_op(pool3, name='conv4_1', wh=3, ww=3, n_out=512, dh=1, dw=1, p=parameters)
conv4_2 = conv_op(conv4_1, name='conv4_1', wh=3, ww=3, n_out=512, dh=1, dw=1, p=parameters)
conv4_3 = conv_op(conv4_2, name='conv4_1', wh=3, ww=3, n_out=512, dh=1, dw=1, p=parameters)
pool4 = pool_op(conv4_3, name='pool4', wh=2, ww=2, dh=2, dw=2)
# 卷积5
conv5_1 = conv_op(pool4, name='conv5_1', wh=3, ww=3, n_out=512, dh=1, dw=1, p=parameters)
conv5_2 = conv_op(conv5_1, name='conv5_1', wh=3, ww=3, n_out=512, dh=1, dw=1, p=parameters)
conv5_3 = conv_op(conv5_2, name='conv5_1', wh=3, ww=3, n_out=512, dh=1, dw=1, p=parameters)
pool5 = pool_op(conv5_3, name='pool5', wh=2, ww=2, dh=2, dw=2)
#全连接:扁平化处理成1维向量
shp = pool5.get_shape()
flattened_shape = shp[1].value * shp[2].value * shp[3].value
resh1 = tf.reshape(pool5, [-1,flattened_shape], name='resh1') #转换为一维
fc1 = fc_op(resh1, name='fc1', n_out=4096, p=parameters)
fc1_drop = tf.nn.dropout(fc1, keep_prob, name='fc1_drop')
fc2 = fc_op(fc1_drop, name='fc2', n_out=4096, p=parameters)
fc2_drop = tf.nn.dropout(fc2, keep_prob, name='fc2_drop')
fc3 = fc_op(fc2_drop, name='fc3', n_out=1000, p=parameters)
softmax = tf.nn.softmax(fc3) #预测值
predictions = tf.argmax(softmax, 1) #返回最大值的索引号
return predictions, parameters, fc3（4）时间间隔（同于alex）
def time_tensorflow_run(session, target, feed, info_string):
num_steps_burn_in = 10 # 设备热身,存在显存加载/cache命中等问题
total_duration = 0.0 # 总时间
total_duration_squared = 0.0 # 用于计算方差
for i in range(num_batches + num_steps_burn_in):
start_time = time.time()
_ = session.run(target, feed_dict=feed)
duration = time.time() - start_time
if i >= num_steps_burn_in:
if not i % 10:
print('%s: step %d, duration = %.3f' %
(datetime.now(), i - num_steps_burn_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))
（5）运行
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))
keep_prob = tf.placeholder(tf.float32) #必须占位
predictions, parameters, fc3 = inference(images, keep_prob)
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)
time_tensorflow_run(sess, predictions, {keep_prob: 1.0}, "Forward") #预测用keep_prob=1.0
objective = tf.nn.l2_loss(fc3) #l2正则化必须计算每层输出，不能越级计算prediction，否则报错，格式不符。
grad = tf.gradients(objective, parameters) # 计算梯度(objective与parameters有相关)
time_tensorflow_run(sess, grad, {keep_prob: 0.5}, "Forward-backward") #评测用keep_prob=0.5
if __name__ == '__main__':
run_benchmark()（6）结果
2018-09-20 10:47:33.207136: step 0, duration = 19.633
2018-09-20 10:50:55.973392: step 10, duration = 21.066
2018-09-20 10:54:25.718082: step 20, duration = 21.203
2018-09-20 10:57:47.927425: step 30, duration = 19.620
2018-09-20 11:01:21.385679: step 40, duration = 23.137
2018-09-20 11:04:22.032885: Forward across 50 steps, 20.569 +/- 0.932 sec / batch
2018-09-20 11:15:46.500329: step 0, duration = 59.778
2018-09-20 11:25:44.948142: step 10, duration = 59.143
2018-09-20 11:35:45.526153: step 20, duration = 59.795
2018-09-20 11:45:45.806747: step 30, duration = 60.079
2018-09-20 11:55:47.121064: step 40, duration = 60.367
2018-09-20 12:04:45.947511: Forward-backward across 50 steps, 59.984 +/- 0.530 sec / batch