tensorflow.contrib.slim实现vgg19和resnet101两种基础网络

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。

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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
TensorFlow 2.x 中,`tensorflow.contrib` 已经被移除了。为了使用Slim,可以按照以下步骤进行: 1. 安装 TensorFlow 2.x TensorFlow Addons(用于一些额外的功能): ```python !pip install tensorflow==2.5.0 !pip install tensorflow-addons ``` 2. 导入 `tensorflow_addons` `tensorflow.keras`: ```python import tensorflow_addons as tfa import tensorflow.keras as keras ``` 3. 通过 `keras` 导入 `slim`: ```python from tensorflow.keras import layers from tensorflow.keras import backend as K from tensorflow.keras.models import Model from tensorflow.keras.applications import imagenet_utils from tensorflow.keras.applications import ResNet50 from tensorflow.keras.preprocessing.image import img_to_array from tensorflow.keras.preprocessing.image import load_img from tensorflow.keras.applications.mobilenet_v2 import MobileNetV2 from tensorflow.keras.applications.mobilenet_v2 import preprocess_input as mobilenet_v2_preprocess_input from tensorflow.keras.applications.inception_v3 import InceptionV3 from tensorflow.keras.applications.inception_v3 import preprocess_input as inception_v3_preprocess_input from tensorflow.keras.applications.vgg16 import VGG16 from tensorflow.keras.applications.vgg16 import preprocess_input as vgg16_preprocess_input from tensorflow.keras.applications.resnet_v2 import ResNet50V2 from tensorflow.keras.applications.resnet_v2 import preprocess_input as resnet_v2_preprocess_input from tensorflow.keras.applications.efficientnet import EfficientNetB0 from tensorflow.keras.applications.efficientnet import preprocess_input as efficientnet_preprocess_input from tensorflow.keras.applications.nasnet import NASNetMobile from tensorflow.keras.applications.nasnet import preprocess_input as nasnet_preprocess_input from tensorflow.keras.applications.xception import Xception from tensorflow.keras.applications.xception import preprocess_input as xception_preprocess_input import tensorflow_hub as hub from typing import List, Tuple import numpy as np import cv2 ``` 这样就可以使用 Slim 的一些功能了。需要注意的是,SlimTensorFlow 2.x 中已经不是官方支持的模块,因此在使用时需要自行承担风险。
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