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vgg16.py
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vgg16.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Apr 5 20:09:43 2018
@author: wu
"""
import tensorflow as tf
#定义卷积操作
def conv_op(input_op, name, kh, kw, n_out, dh, dw):
input_op = tf.convert_to_tensor(input_op)
n_in = input_op.get_shape()[-1].value
with tf.name_scope(name) as scope:
kernel = tf.get_variable(scope+"w",
shape = [kh, kw, n_in, n_out],
dtype = tf.float32,
initializer = tf.contrib.layers.xavier_initializer_conv2d())
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 = tf.Variable(bias_init_val, trainable = True, name = 'b')
z = tf.nn.bias_add(conv, biases)
activation = tf.nn.relu(z, name = scope)
return activation
#定义全连接操作
def fc_op(input_op, name, n_out):
n_in = input_op.get_shape()[-1].value
with tf.name_scope(name) as scope:
kernel = tf.get_variable(scope+'w',
shape = [n_in, n_out],
dtype = tf.float32,
initializer = tf.contrib.layers.xavier_initializer())
biases = tf.Variable(tf.constant(0.1, shape = [n_out], dtype = tf.float32), name = 'b')
# tf.nn.relu_layer对输入变量input_op与kernel做矩阵乘法加上bias，再做RELU非线性变换得到activation
activation = tf.nn.relu_layer(input_op, kernel, biases, name = scope)
return activation
#定义池化层
def mpool_op(input_op, name, kh, kw, dh, dw):
return tf.nn.max_pool(input_op,
ksize = [1, kh, kw, 1],
strides = [1, dh, dw, 1],
padding = 'SAME',
name = name)
#定义网络结构
def vgg16_net(input_op, keep_prob=1):
# block 1 -- outputs 112x112x64
conv1_1 = conv_op(input_op, name="conv1_1", kh=3, kw=3, n_out=64, dh=1, dw=1)
conv1_2 = conv_op(conv1_1, name="conv1_2", kh=3, kw=3, n_out=64, dh=1, dw=1)
pool1 = mpool_op(conv1_2, name="pool1", kh=2, kw=2, dw=2, dh=2)
# block 2 -- outputs 56x56x128
conv2_1 = conv_op(pool1, name="conv2_1", kh=3, kw=3, n_out=128, dh=1, dw=1)
conv2_2 = conv_op(conv2_1, name="conv2_2", kh=3, kw=3, n_out=128, dh=1, dw=1)
pool2 = mpool_op(conv2_2, name="pool2", kh=2, kw=2, dh=2, dw=2)
# # block 3 -- outputs 28x28x256
conv3_1 = conv_op(pool2, name="conv3_1", kh=3, kw=3, n_out=256, dh=1, dw=1)
conv3_2 = conv_op(conv3_1, name="conv3_2", kh=3, kw=3, n_out=256, dh=1, dw=1)
conv3_3 = conv_op(conv3_2, name="conv3_3", kh=3, kw=3, n_out=256, dh=1, dw=1)
pool3 = mpool_op(conv3_3, name="pool3", kh=2, kw=2, dh=2, dw=2)
# block 4 -- outputs 14x14x512
conv4_1 = conv_op(pool3, name="conv4_1", kh=3, kw=3, n_out=512, dh=1, dw=1)
conv4_2 = conv_op(conv4_1, name="conv4_2", kh=3, kw=3, n_out=512, dh=1, dw=1)
conv4_3 = conv_op(conv4_2, name="conv4_3", kh=3, kw=3, n_out=512, dh=1, dw=1)
pool4 = mpool_op(conv4_3, name="pool4", kh=2, kw=2, dh=2, dw=2)
# block 5 -- outputs 7x7x512
conv5_1 = conv_op(pool4, name="conv5_1", kh=3, kw=3, n_out=512, dh=1, dw=1)
conv5_2 = conv_op(conv5_1, name="conv5_2", kh=3, kw=3, n_out=512, dh=1, dw=1)
conv5_3 = conv_op(conv5_2, name="conv5_3", kh=3, kw=3, n_out=512, dh=1, dw=1)
pool5 = mpool_op(conv5_3, name="pool5", kh=2, kw=2, dw=2, dh=2)
# flatten
shp = pool5.get_shape()
flattened_shape = shp[1].value * shp[2].value * shp[3].value
resh1 = tf.reshape(pool5, [-1, flattened_shape], name="resh1")
# fully connected
fc6 = fc_op(resh1, name="fc6", n_out=4096)
fc6_drop = tf.nn.dropout(fc6, keep_prob, name="fc6_drop")
fc7 = fc_op(fc6_drop, name="fc7", n_out=4096)
fc7_drop = tf.nn.dropout(fc7, keep_prob, name="fc7_drop")
logits = fc_op(fc7_drop, name="fc8", n_out=6)
return logits