# -*- coding: utf-8 -*-
"""
Created on Wed Nov 8 21:17:53 2017
@author: Jayce
"""
# --* coding: UTF-8 *--
# VGG通过反复堆叠3*3的小型卷积核和2*2的最大池化层，VGGNet成功的构建了16~19层深的卷积神经网络
# VGGNet评测forward（inference）耗时和backward（training）耗时
from datetime import datetime
import math
import time
import tensorflow as tf
########完成卷积层、全连接层和最大池化层的创建函数########
# conv_op函数用来创建卷积层并且把本层的参数存入参数列表
def conv_op(input_op, name, kh, kw, n_out, dh, dw, p):
'''
Args:
input_op：输入的tensor
name：这一层的名称
kh：kernel height即卷积核的高
kw：kernel weight即卷积核的宽
n_out：卷积核数量即输出通道数
dh：步长的高
dw：步长的宽
p：参数列表
'''
n_in = input_op.get_shape()[-1].value # 获取input_op的通道数
with tf.name_scope(name) as scope: # 设置scope，生成的Variable使用默认的命名
kernel = tf.get_variable(scope+"w", # kernel（即卷积核参数）使用tf.get_variable创建
shape=[kh, kw, n_in, n_out], # 【卷积核的高，卷积核的宽、输入通道数，输出通道数】
dtype=tf.float32,
initializer=tf.contrib.layers.xavier_initializer_conv2d()) # 参数初始化
# 使用tf.nn.conv2d对input_op进行卷积处理，卷积核kernel，步长dh*dw，padding模式为SAME
conv = tf.nn.conv2d(input_op, kernel, (1, dh, dw, 1), padding='SAME')
bias_init_val = tf.constant(0.0, shape=[n_out], dtype=tf.float32) # biases使用tf.constant赋值为0
biases = tf.Variable(bias_init_val, trainable=True, name='b') # 将bias_init_val转成可训练的参数
z = tf.nn.bias_add(conv, biases) # 将卷积结果conv和bias相加
activation = tf.nn.relu(z, name=scope) # 对z进行非线性处理得到activation
p += [kernel, biases] # 创建卷积层时用到的参数kernel和bias添加进参数列表
return activation # 将卷积层的输出activation作为函数结果返回
# 定义全连接层的创建函数
def fc_op(input_op, name, n_out, p):
n_in = input_op.get_shape()[-1].value # 获取tensor的通道数
with tf.name_scope(name) as scope:
kernel = tf.get_variable(scope+"w", # 使用tf.get_variable创建全连接层的参数
shape=[n_in, n_out], # 参数的维度有两个，输入通道数和输出通道数
dtype=tf.float32,
initializer=tf.contrib.layers.xavier_initializer())
# biases赋值0.1以避免dead neuron
biases = tf.Variable(tf.constant(0.1, shape=[n_out], dtype=tf.float32), name='b')
# 对输入变量input_op和kernel做矩阵乘法并加上biases。再做非线性变换activation
activation = tf.nn.relu_layer(input_op, kernel, biases, name=scope)
p += [kernel, biases]
return activation
# 定义最大池化层的创建函数
def mpool_op(input_op, name, kh, kw, dh, dw):
return tf.nn.max_pool(input_op,
ksize=[1, kh, kw, 1], # 池化层尺寸kh*kw
strides=[1, dh, dw, 1], # 步长dh*dw
padding='SAME',
name=name)
########开始创建VGGNet-16的网络结构########
def inference_op(input_op, keep_prob):
'''
VGGNet-16的网络结构主要分为6个部分：前五段为卷积网络，最后一段是全连接网络。
Args:
input_op：输入Tensor
keep_prob：控制Dropout的一个placeholder
'''
# 初始化参数列表p
p = []
# assume input_op shape is 224x224x3（第一个卷积层的输入input_op）
# 创建第一段卷积网络 -- outputs 112x112x64
# 两个卷积层的卷积核都是3*3，卷积核数量（输出通道数）均为64，步长1*1，全像素扫描。
conv1_1 = conv_op(input_op, name="conv1_1", kh=3, kw=3, n_out=64, dh=1, dw=1, p=p) # outputs 224x224x64
conv1_2 = conv_op(conv1_1, name="conv1_2", kh=3, kw=3, n_out=64, dh=1, dw=1, p=p) # outputs 224x224x64
pool1 = mpool_op(conv1_2, name="pool1", kh=2, kw=2, dw=2, dh=2) # 标准的2*2的最大池化-outputs 112x112x64
# 创建第二段卷积网络 -- outputs 56x56x128
conv2_1 = conv_op(pool1, name="conv2_1", kh=3, kw=3, n_out=128, dh=1, dw=1, p=p)
conv2_2 = conv_op(conv2_1, name="conv2_2", kh=3, kw=3, n_out=128, dh=1, dw=1, p=p)
pool2 = mpool_op(conv2_2, name="pool2", kh=2, kw=2, dh=2, dw=2)
# 创建第三段卷积网络 -- outputs 28x28x256
conv3_1 = conv_op(pool2, name="conv3_1", kh=3, kw=3, n_out=256, dh=1, dw=1, p=p)
conv3_2 = conv_op(conv3_1, name="conv3_2", kh=3, kw=3, n_out=256, dh=1, dw=1, p=p)
conv3_3 = conv_op(conv3_2, name="conv3_3", kh=3, kw=3, n_out=256, dh=1, dw=1, p=p)
pool3 = mpool_op(conv3_3, name="pool3", kh=2, kw=2, dh=2, dw=2)
# 创建第四段卷积网络 -- outputs 14x14x512
conv4_1 = conv_op(pool3, name="conv4_1", kh=3, kw=3, n_out=512, dh=1, dw=1, p=p)
conv4_2 = conv_op(conv4_1, name="conv4_2", kh=3, kw=3, n_out=512, dh=1, dw=1, p=p)
conv4_3 = conv_op(conv4_2, name="conv4_3", kh=3, kw=3, n_out=512, dh=1, dw=1, p=p)
pool4 = mpool_op(conv4_3, name="pool4", kh=2, kw=2, dh=2, dw=2)
# 创建第五段卷积网络 -- outputs 7x7x512
conv5_1 = conv_op(pool4, name="conv5_1", kh=3, kw=3, n_out=512, dh=1, dw=1, p=p)
conv5_2 = conv_op(conv5_1, name="conv5_2", kh=3, kw=3, n_out=512, dh=1, dw=1, p=p)
conv5_3 = conv_op(conv5_2, name="conv5_3", kh=3, kw=3, n_out=512, dh=1, dw=1, p=p)
pool5 = mpool_op(conv5_3, name="pool5", kh=2, kw=2, dw=2, dh=2)
# 备注：VGGNet-16的每一段卷积网络都会将图像的边长缩小一半，但是将卷积输出通道数翻倍。
# 第五段卷积输出的通道数不再增加。
# flatten 将第五段卷积网络的输出结果进行扁平化
shp = pool5.get_shape()
flattened_shape = shp[1].value * shp[2].value * shp[3].value
# tf.reshape函数将每个样本化为长度7*7*512 = 25088的向量
resh1 = tf.reshape(pool5, [-1, flattened_shape], name="resh1")
# fully connected 隐含节点4096的全连接层
fc6 = fc_op(resh1, name="fc6", n_out=4096, p=p)
fc6_drop = tf.nn.dropout(fc6, keep_prob, name="fc6_drop")
fc7 = fc_op(fc6_drop, name="fc7", n_out=4096, p=p)
fc7_drop = tf.nn.dropout(fc7, keep_prob, name="fc7_drop")
fc8 = fc_op(fc7_drop, name="fc8", n_out=1000, p=p)
softmax = tf.nn.softmax(fc8) # 得到分类输出概率
predictions = tf.argmax(softmax, 1) # tf.argmax求输出概率最大类别
return predictions, softmax, fc8, p
########评测函数########
def time_tensorflow_run(session, target, feed, info_string): # 与AlexNet非常相似，session参数一点点区别
num_steps_burn_in = 10
total_duration = 0.0
total_duration_squared = 0.0
for i in range(num_batches + num_steps_burn_in):
start_time = time.time()
_ = session.run(target, feed_dict=feed) # 引入feed_dict方便后面传入keep_prob来控制Dropout层的保留比率
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,
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