tensorflow.contrib.slim实现vgg19和resnet101两种基础网络_vgg19模型是resnet101嘛-优快云博客

本文介绍了作者如何依据网上教程，利用tensorflow.contrib.slim库分别实现了vgg19和resnet101两种深度学习模型，强调了slim库在构建基础网络时的便捷性，并提供了相关博客链接以供参考。

最近自己按照网上的tensorflow中slim的编写流程，根据两篇博客和的模板自己将resnet101和vgg19实现了一遍，感觉受益匪浅，使用slim实现基础网络比使用tf.nn快捷和便利的多，以后还得向大佬学习。

https://blog.youkuaiyun.com/index20001/article/details/76623964 resnet

https://blog.youkuaiyun.com/ellin_young/article/details/79543290 vgg16

vgg19代码

# --*-- coding:utf8 --*--

import tensorflow as tf
import tensorflow.contrib.slim as slim
import random
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

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, 4, slim.conv2d, 256, [3, 3], scope='conv3')
        net = slim.max_pool2d(net, [2, 2], scope='pool3')
        net = slim.repeat(net, 4, slim.conv2d, 512, [3, 3], scope='conv4')
        net = slim.max_pool2d(net, [2, 2], scope='pool4')
        net = slim.repeat(net, 4, slim.conv2d, 512, [3, 3], scope='conv5')
        net = slim.max_pool2d(net, [2, 2], scope='pool5')
        net = slim.flatten(net, scope='flat')

    net = slim.fully_connected(net, 4096, scope='fc1')
    net = slim.dropout(net, keep_prob=0.5, scope='dropt1')
    net = slim.fully_connected(net, 4096, scope='fc2')
    net = slim.dropout(net, keep_prob=0.5, scop