【tensorflow】VGG16-vgg.py

#!/usr/bin/python3
# -*- coding: utf-8 -*-
# @Time: 2018/6/29
# @Author: xfLi
# 实现VGG16网络

import tensorflow as tf
from VGG import tools


def VGG16(x, n_class, is_pretrain=True):
    with tf.name_scope('vgg16'):
        x = tools.conv('conv1_1', x, 64, kernel_size=[3, 3], stride=[1, 1, 1, 1], is_pretrain=is_pretrain)
        x = tools.conv('conv1_2', x, 64, kernel_size=[3, 3], stride=[1, 1, 1, 1], is_pretrain=is_pretrain)
        with tf.name_scope('pool1'):
            x = tools.pool('pool1', x, ksize=[1, 2, 2, 1], stride=[1, 2, 2, 1], is_max_pool=True)

        x = tools.conv('conv2_1', x, 128, kernel_size=[3, 3], stride=[1, 1, 1, 1], is_pretrain=is_pretrain)
        x = tools.conv('conv2_2', x, 128, kernel_size=[3, 3], stride=[1, 1, 1, 1], is_pretrain=is_pretrain)
        with tf.name_scope('pool2'):
            x = tools.pool('pool2', x, ksize=[1, 2, 2, 1], stride=[1, 2, 2, 1], is_max_pool=True)

        x = tools.conv('conv3_1', x, 256, kernel_size=[3, 3], stride=[1, 1, 1, 1], is_pretrain=is_pretrain)
        x = tools.conv('conv3_2', x, 256, kernel_size=[3, 3], stride=[1, 1, 1, 1], is_pretrain=is_pretrain)
        x = tools.conv('conv3_3', x, 256, kernel_size=[3, 3], stride=[1, 1, 1, 1], is_pretrain=is_pretrain)
        with tf.name_scope('pool3'):
            x = tools.pool('pool3', x, ksize=[1, 2, 2, 1], stride=[1, 2, 2, 1], is_max_pool=True)

        x = tools.conv('conv4_1', x, 512, kernel_size=[3, 3], stride=[1, 2, 2, 1], is_pretrain=is_pretrain)
        x = tools.conv('conv4_2', x, 512, kernel_size=[3, 3], stride=[1, 2, 2, 1], is_pretrain=is_pretrain)
        x = tools.conv('conv4_3', x, 512, kernel_size=[3, 3], stride=[1, 2, 2, 1], is_pretrain=is_pretrain)
        with tf.name_scope('pool4'):
            x = tools.pool('pool4', x, ksize=[1, 2, 2, 1], stride=[1, 2, 2, 1], is_max_pool=True)

        x = tools.conv('conv5_1', x, 512, kernel_size=[3, 3], stride=[1, 2, 2, 1], is_pretrain=is_pretrain)
        x = tools.conv('conv5_2', x, 512, kernel_size=[3, 3], stride=[1, 2, 2, 1], is_pretrain=is_pretrain)
        x = tools.conv('conv5_3', x, 512, kernel_size=[3, 3], stride=[1, 2, 2, 1], is_pretrain=is_pretrain)
        with tf.name_scope('pool5'):
            x = tools.pool('pool5', x, ksize=[1, 2, 2, 1], stride=[1, 2, 2, 1], is_max_pool=True)

        x = tools.fc_layer('fc6', x, out_nodes=4096)
        with tf.name_scope('batch_norm1'):
            x = tools.batch_norm(x)
        x = tools.fc_layer('fc7', x, out_nodes=4096)
        with tf.name_scope('batch_norm2'):
            x = tools.batch_norm(x)
        x = tools.fc_layer('fc8', x, out_nodes=n_class)

        return x