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VGG.py
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# !/usr/bin/env python
# -*- coding:utf-8 -*-
# Author:ZhengzhengLiu
import tensorflow as tf
# VGG_16全部使用3*3卷积核和2*2的池化核
# 创建卷积层函数
def conv_op(input_op, name, kh, kw, n_out, dh, dw, p):
"""
param :
input_op -- 输入tensor
name -- 该层的名称
kh -- 卷积核的高
kw -- 卷积核的宽
n_out -- 卷积核数目/输出通道数
dh -- 步长的高
dw -- 步长的宽
p -- 参数（字典类型）
return:
A -- 卷积层的输出
"""
n_in = input_op.get_shape()[-1].value # 输入的通道数
with tf.variable_scope(name) as scope:
weights = tf.get_variable(name=scope + "w", shape=[kh, kw, n_in, n_out], dtype=tf.float32,
initializer=tf.contrib.layers.xavier_initializer_con2d())
biases = tf.get_variable(name=scope + "b", shape=[n_out], dtype=tf.float32,
initializer=tf.constant_initializer(0.0), trainable=True)
conv = tf.nn.conv2d(input=input_op, filter=weights, strides=[1, dh, dw, 1], padding="SAME")
Z = tf.nn.bias_add(conv, biases)
A = tf.nn.relu(Z, name=scope)
p[name + "w"] = weights
p[name + "b"] = biases
return A
# 创建最大池化层的函数
def maxpool_op(input_op, name, kh, kw, dh, dw):
pool = tf.nn.max_pool(input_op, ksize=[1, kh, kw, 1], strides=[1, dh, dw, 1], padding="SAME", name=name)
return pool
# 创建全连接层的函数
def fc_op(input_op, name, n_out, p):
n_in = input_op.get_shape()[-1].value
with tf.variable_scope(name) as scope:
weights = tf.get_variable(name=scope + "w", shape=[n_in, n_out], dtype=tf.float32,
initializer=tf.contrib.layers.xavier_initializer())
# biases不再初始化为0，赋予一个较小的值，以避免dead neuron
biases = tf.get_variable(name=scope + "b", shape=[n_out], dtype=tf.float32,
initializer=tf.constant_initializer(0.1))
# tf.nn.relu_layer对输入变量input_op与weights做矩阵乘法加上biases，再做非线性relu变换
A = tf.nn.relu_layer(input_op, weights, biases, name=scope)
p[name + "w"] = weights
p[name + "b"] = biases
return A
def VGG_16(input_op,keep_prob):
p = {} # 初始化参数字典
# 第一段卷积网络——两个卷积层和一个最大池化层
# 两个卷积层的卷积核大小为3*3，卷积核数量均为64，步长s=1，输出均为：224*224*64
conv1_1 = conv_op(input_op, name="conv1_1", kh=3, kw=3, n_out=64, dh=1, dw=1, p=p)
conv1_2 = conv_op(conv1_1, name="conv1_2", kh=3, kw=3, n_out=64, dh=1, dw=1, p=p)
# 最大池化层采用的尺寸大小为：2*2，步长s=2，输出为：112*112*64
pool1 = maxpool_op(conv1_2, name="pool1", kh=2, kw=2, dh=2, dw=2)
# 第二段卷积网络——两个卷积层和一个最大池化层
# 两个卷积层的卷积核大小为3*3，卷积核数量均为128，步长s=1，输出均为：112*112*128
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)
# 最大池化层采用的尺寸大小为：2*2，步长s=2，输出为：56*56*128
pool2 = maxpool_op(conv2_2, name="pool2", kh=2, kw=2, dh=2, dw=2)
# 第三段卷积网络——三个卷积层和一个最大池化层
# 三个卷积层的卷积核大小为3*3，卷积核数量均为256，步长s=1，输出均为：56*56*256
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)
# 最大池化层采用的尺寸大小为：2*2，步长s=2，输出为：28*28*256
pool3 = maxpool_op(conv3_3, name="pool3", kh=2, kw=2, dh=2, dw=2)
# 第四段卷积网络——三个卷积层和一个最大池化层
# 三个卷积层的卷积核大小为3*3，卷积核数量均为512，步长s=1，输出均为：28*28*512
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)
# 最大池化层采用的尺寸大小为：2*2，步长s=2，输出为：14*14*512
pool4 = maxpool_op(conv4_3, name="pool4", kh=2, kw=2, dh=2, dw=2)
# 第五段卷积网络——三个卷积层和一个最大池化层
# 三个卷积层的卷积核大小为3*3，卷积核数量均为512，步长s=1，输出均为：14*14*512
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)
# 最大池化层采用的尺寸大小为：2*2，步长s=2，输出为：7*7*512
pool5 = maxpool_op(conv5_3, name="pool5", kh=2, kw=2, dh=2, dw=2)
# 第六、七段 —— 含4096个隐藏节点的全连接层及dropout
pool5_shape = pool5.get_shape().as_list()
flattened_shape = pool5_shape[1] * pool5_shape[2] * pool5_shape[3]
dense = tf.reshape(pool5, shape=[-1, flattened_shape], name="dense") # 向量化
fc6 = fc_op(dense, name="fc6", n_out=4096, p=p)
fc6_drop = tf.nn.dropout(fc6, keep_prob=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=keep_prob, name="fc7_drop")
# 最后一层输出层含1000个节点,进行softmax分类
fc8 = fc_op(fc7_drop, name="fc8", n_out=1000, p=p)
softmax = tf.nn.softmax(fc8)
prediction = tf.argmax(softmax, 1)
return prediction, softmax, fc8, psamsan
def vgg16(inputs):
with slim.arg_scope([slim.conv2d, slim.fully_connected],
activation_fn=tf.nn.relu,
weights_initializer=tf.truncated_normal_initializer(0.0, 0.01),
weights_regularizer=slim.l2_regularizer(0.0005)):
net = slim.repeat(inputs, 2, slim.conv2d, 64, [3, 3], scope='conv1')
net = slim.max_pool2d(net, [2, 2], scope='pool1')
net = slim.repeat(net, 2, slim.conv2d, 128, [3, 3], scope='conv2')
net = slim.max_pool2d(net, [2, 2], scope='pool2')
net = slim.repeat(net, 3, slim.conv2d, 256, [3, 3], scope='conv3')
net = slim.max_pool2d(net, [2, 2], scope='pool3')
net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], scope='conv4')
net = slim.max_pool2d(net, [2, 2], scope='pool4')
net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], scope='conv5')
net = slim.max_pool2d(net, [2, 2], scope='pool5')
net = slim.fully_connected(net, 4096, scope='fc6')
net = slim.dropout(net, 0.5, scope='dropout6')
net = slim.fully_connected(net, 4096, scope='fc7')
net = slim.dropout(net, 0.5, scope='dropout7')
net = slim.fully_connected(net, 1000, activation_fn=None, scope='fc8')
return net