PYTHON InceptionV3模型的复现详解


Posted in Python onMay 06, 2022

学习前言

Inception系列的结构和其它的前向神经网络的结构不太一样,每一层的内容不是直直向下的,而是分了很多的块。

什么是InceptionV3模型

InceptionV3模型是谷歌Inception系列里面的第三代模型,其模型结构与InceptionV2模型放在了同一篇论文里,其实二者模型结构差距不大,相比于其它神经网络模型,Inception网络最大的特点在于将神经网络层与层之间的卷积运算进行了拓展。
如VGG,AlexNet网络,它就是一直卷积下来的,一层接着一层;
ResNet则是创新性的引入了残差网络的概念,使得靠前若干层的某一层数据输出直接跳过多层引入到后面数据层的输入部分,后面的特征层的内容会有一部分由其前面的某一层线性贡献。
而Inception网络则是采用不同大小的卷积核,使得存在不同大小的感受野,最后实现拼接达到不同尺度特征的融合。
对于InceptionV3而言,其网络中存在着如下的结构。
这个结构使用不同大小的卷积核对输入进行卷积(这个结构主要在代码中的block1使用)。
PYTHON InceptionV3模型的复现详解
还存在着这样的结构,利用1x7的卷积和7x1的卷积代替7x7的卷积,这样可以只使用约(1x7 + 7x1) / (7x7) = 28.6%的计算开销;利用1x3的卷积和3x1的卷积代替3x3的卷积,这样可以只使用约(1x3 + 3x1) / (3x3) = 67%的计算开销。
下图利用1x7的卷积和7x1的卷积代替7x7的卷积(这个结构主要在代码中的block2使用)。
PYTHON InceptionV3模型的复现详解
下图利用1x3的卷积和3x1的卷积代替3x3的卷积(这个结构主要在代码中的block3使用)。
PYTHON InceptionV3模型的复现详解

InceptionV3网络部分实现代码

我一共将InceptionV3划分为3个block,对应着35x35、17x17,8x8维度大小的图像。每个block中间有许多的part,对应着不同的特征层深度,用于特征提取。

#-------------------------------------------------------------#
#   InceptionV3的网络部分
#-------------------------------------------------------------#
from __future__ import print_function
from __future__ import absolute_import

import warnings
import numpy as np

from keras.models import Model
from keras import layers
from keras.layers import Activation,Dense,Input,BatchNormalization,Conv2D,MaxPooling2D,AveragePooling2D
from keras.layers import GlobalAveragePooling2D,GlobalMaxPooling2D
from keras.engine.topology import get_source_inputs
from keras.utils.layer_utils import convert_all_kernels_in_model
from keras.utils.data_utils import get_file
from keras import backend as K
from keras.applications.imagenet_utils import decode_predictions
from keras.preprocessing import image


def conv2d_bn(x,
              filters,
              num_row,
              num_col,
              padding='same',
              strides=(1, 1),
              name=None):
    if name is not None:
        bn_name = name + '_bn'
        conv_name = name + '_conv'
    else:
        bn_name = None
        conv_name = None
    x = Conv2D(
        filters, (num_row, num_col),
        strides=strides,
        padding=padding,
        use_bias=False,
        name=conv_name)(x)
    x = BatchNormalization(scale=False, name=bn_name)(x)
    x = Activation('relu', name=name)(x)
    return x


def InceptionV3(input_shape=[299,299,3],
                classes=1000):


    img_input = Input(shape=input_shape)

    x = conv2d_bn(img_input, 32, 3, 3, strides=(2, 2), padding='valid')
    x = conv2d_bn(x, 32, 3, 3, padding='valid')
    x = conv2d_bn(x, 64, 3, 3)
    x = MaxPooling2D((3, 3), strides=(2, 2))(x)

    x = conv2d_bn(x, 80, 1, 1, padding='valid')
    x = conv2d_bn(x, 192, 3, 3, padding='valid')
    x = MaxPooling2D((3, 3), strides=(2, 2))(x)

    #--------------------------------#
    #   Block1 35x35
    #--------------------------------#
    # Block1 part1
    # 35 x 35 x 192 -> 35 x 35 x 256
    branch1x1 = conv2d_bn(x, 64, 1, 1)

    branch5x5 = conv2d_bn(x, 48, 1, 1)
    branch5x5 = conv2d_bn(branch5x5, 64, 5, 5)

    branch3x3dbl = conv2d_bn(x, 64, 1, 1)
    branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3)
    branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3)

    branch_pool = AveragePooling2D((3, 3), strides=(1, 1), padding='same')(x)
    branch_pool = conv2d_bn(branch_pool, 32, 1, 1)
    x = layers.concatenate(
        [branch1x1, branch5x5, branch3x3dbl, branch_pool],
        axis=3,
        name='mixed0')

    # Block1 part2
    # 35 x 35 x 256 -> 35 x 35 x 288
    branch1x1 = conv2d_bn(x, 64, 1, 1)

    branch5x5 = conv2d_bn(x, 48, 1, 1)
    branch5x5 = conv2d_bn(branch5x5, 64, 5, 5)

    branch3x3dbl = conv2d_bn(x, 64, 1, 1)
    branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3)
    branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3)

    branch_pool = AveragePooling2D((3, 3), strides=(1, 1), padding='same')(x)
    branch_pool = conv2d_bn(branch_pool, 64, 1, 1)
    x = layers.concatenate(
        [branch1x1, branch5x5, branch3x3dbl, branch_pool],
        axis=3,
        name='mixed1')

    # Block1 part3
    # 35 x 35 x 288 -> 35 x 35 x 288
    branch1x1 = conv2d_bn(x, 64, 1, 1)

    branch5x5 = conv2d_bn(x, 48, 1, 1)
    branch5x5 = conv2d_bn(branch5x5, 64, 5, 5)

    branch3x3dbl = conv2d_bn(x, 64, 1, 1)
    branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3)
    branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3)

    branch_pool = AveragePooling2D((3, 3), strides=(1, 1), padding='same')(x)
    branch_pool = conv2d_bn(branch_pool, 64, 1, 1)
    x = layers.concatenate(
        [branch1x1, branch5x5, branch3x3dbl, branch_pool],
        axis=3,
        name='mixed2')

    #--------------------------------#
    #   Block2 17x17
    #--------------------------------#
    # Block2 part1
    # 35 x 35 x 288 -> 17 x 17 x 768
    branch3x3 = conv2d_bn(x, 384, 3, 3, strides=(2, 2), padding='valid')

    branch3x3dbl = conv2d_bn(x, 64, 1, 1)
    branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3)
    branch3x3dbl = conv2d_bn(
        branch3x3dbl, 96, 3, 3, strides=(2, 2), padding='valid')

    branch_pool = MaxPooling2D((3, 3), strides=(2, 2))(x)
    x = layers.concatenate(
        [branch3x3, branch3x3dbl, branch_pool], axis=3, name='mixed3')

    # Block2 part2
    # 17 x 17 x 768 -> 17 x 17 x 768
    branch1x1 = conv2d_bn(x, 192, 1, 1)

    branch7x7 = conv2d_bn(x, 128, 1, 1)
    branch7x7 = conv2d_bn(branch7x7, 128, 1, 7)
    branch7x7 = conv2d_bn(branch7x7, 192, 7, 1)

    branch7x7dbl = conv2d_bn(x, 128, 1, 1)
    branch7x7dbl = conv2d_bn(branch7x7dbl, 128, 7, 1)
    branch7x7dbl = conv2d_bn(branch7x7dbl, 128, 1, 7)
    branch7x7dbl = conv2d_bn(branch7x7dbl, 128, 7, 1)
    branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 1, 7)

    branch_pool = AveragePooling2D((3, 3), strides=(1, 1), padding='same')(x)
    branch_pool = conv2d_bn(branch_pool, 192, 1, 1)
    x = layers.concatenate(
        [branch1x1, branch7x7, branch7x7dbl, branch_pool],
        axis=3,
        name='mixed4')

    # Block2 part3 and part4
    # 17 x 17 x 768 -> 17 x 17 x 768 -> 17 x 17 x 768
    for i in range(2):
        branch1x1 = conv2d_bn(x, 192, 1, 1)

        branch7x7 = conv2d_bn(x, 160, 1, 1)
        branch7x7 = conv2d_bn(branch7x7, 160, 1, 7)
        branch7x7 = conv2d_bn(branch7x7, 192, 7, 1)

        branch7x7dbl = conv2d_bn(x, 160, 1, 1)
        branch7x7dbl = conv2d_bn(branch7x7dbl, 160, 7, 1)
        branch7x7dbl = conv2d_bn(branch7x7dbl, 160, 1, 7)
        branch7x7dbl = conv2d_bn(branch7x7dbl, 160, 7, 1)
        branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 1, 7)

        branch_pool = AveragePooling2D(
            (3, 3), strides=(1, 1), padding='same')(x)
        branch_pool = conv2d_bn(branch_pool, 192, 1, 1)
        x = layers.concatenate(
            [branch1x1, branch7x7, branch7x7dbl, branch_pool],
            axis=3,
            name='mixed' + str(5 + i))

    # Block2 part5
    # 17 x 17 x 768 -> 17 x 17 x 768
    branch1x1 = conv2d_bn(x, 192, 1, 1)

    branch7x7 = conv2d_bn(x, 192, 1, 1)
    branch7x7 = conv2d_bn(branch7x7, 192, 1, 7)
    branch7x7 = conv2d_bn(branch7x7, 192, 7, 1)

    branch7x7dbl = conv2d_bn(x, 192, 1, 1)
    branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 7, 1)
    branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 1, 7)
    branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 7, 1)
    branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 1, 7)

    branch_pool = AveragePooling2D((3, 3), strides=(1, 1), padding='same')(x)
    branch_pool = conv2d_bn(branch_pool, 192, 1, 1)
    x = layers.concatenate(
        [branch1x1, branch7x7, branch7x7dbl, branch_pool],
        axis=3,
        name='mixed7')

    #--------------------------------#
    #   Block3 8x8
    #--------------------------------#
    # Block3 part1
    # 17 x 17 x 768 -> 8 x 8 x 1280
    branch3x3 = conv2d_bn(x, 192, 1, 1)
    branch3x3 = conv2d_bn(branch3x3, 320, 3, 3,
                          strides=(2, 2), padding='valid')

    branch7x7x3 = conv2d_bn(x, 192, 1, 1)
    branch7x7x3 = conv2d_bn(branch7x7x3, 192, 1, 7)
    branch7x7x3 = conv2d_bn(branch7x7x3, 192, 7, 1)
    branch7x7x3 = conv2d_bn(
        branch7x7x3, 192, 3, 3, strides=(2, 2), padding='valid')

    branch_pool = MaxPooling2D((3, 3), strides=(2, 2))(x)
    x = layers.concatenate(
        [branch3x3, branch7x7x3, branch_pool], axis=3, name='mixed8')

    # Block3 part2 part3
    # 8 x 8 x 1280 -> 8 x 8 x 2048 -> 8 x 8 x 2048
    for i in range(2):
        branch1x1 = conv2d_bn(x, 320, 1, 1)

        branch3x3 = conv2d_bn(x, 384, 1, 1)
        branch3x3_1 = conv2d_bn(branch3x3, 384, 1, 3)
        branch3x3_2 = conv2d_bn(branch3x3, 384, 3, 1)
        branch3x3 = layers.concatenate(
            [branch3x3_1, branch3x3_2], axis=3, name='mixed9_' + str(i))

        branch3x3dbl = conv2d_bn(x, 448, 1, 1)
        branch3x3dbl = conv2d_bn(branch3x3dbl, 384, 3, 3)
        branch3x3dbl_1 = conv2d_bn(branch3x3dbl, 384, 1, 3)
        branch3x3dbl_2 = conv2d_bn(branch3x3dbl, 384, 3, 1)
        branch3x3dbl = layers.concatenate(
            [branch3x3dbl_1, branch3x3dbl_2], axis=3)

        branch_pool = AveragePooling2D(
            (3, 3), strides=(1, 1), padding='same')(x)
        branch_pool = conv2d_bn(branch_pool, 192, 1, 1)
        x = layers.concatenate(
            [branch1x1, branch3x3, branch3x3dbl, branch_pool],
            axis=3,
            name='mixed' + str(9 + i))
    # 平均池化后全连接。
    x = GlobalAveragePooling2D(name='avg_pool')(x)
    x = Dense(classes, activation='softmax', name='predictions')(x)


    inputs = img_input

    model = Model(inputs, x, name='inception_v3')

    return model

图片预测

建立网络后,可以用以下的代码进行预测。

def preprocess_input(x):
    x /= 255.
    x -= 0.5
    x *= 2.
    return x


if __name__ == '__main__':
    model = InceptionV3()

    model.load_weights("inception_v3_weights_tf_dim_ordering_tf_kernels.h5")
    
    img_path = 'elephant.jpg'
    img = image.load_img(img_path, target_size=(299, 299))
    x = image.img_to_array(img)
    x = np.expand_dims(x, axis=0)

    x = preprocess_input(x)

    preds = model.predict(x)
    print('Predicted:', decode_predictions(preds))

预测所需的已经训练好的InceptionV3模型可以在https://github.com/fchollet/deep-learning-models/releases下载。非常方便。
预测结果为:

Predicted: [[('n02504458', 'African_elephant', 0.50874853), ('n01871265', 'tusker', 0.19524273), ('n02504013', 'Indian_elephant', 0.1566972), ('n01917289', 'brain_coral', 0.0008956835), ('n01695060', 'Komodo_dragon', 0.0008260256)]]

这里我推荐一个很不错的blog讲InceptionV3的结构的深度神经网络Google Inception Net-V3结构图里面有每一层的结构图,非常清晰。


Tags in this post...

Python 相关文章推荐
十条建议帮你提高Python编程效率
Feb 16 Python
基于数据归一化以及Python实现方式
Jul 11 Python
Python基于多线程实现ping扫描功能示例
Jul 23 Python
Python wxPython库Core组件BoxSizer用法示例
Sep 03 Python
简单谈谈python基本数据类型
Sep 26 Python
python3爬虫怎样构建请求header
Dec 23 Python
Ubuntu+python将nii图像保存成png格式
Jul 18 Python
python实现文件的分割与合并
Aug 29 Python
pymysql 开启调试模式的实现
Sep 24 Python
利用Python优雅的登录校园网
Oct 21 Python
python实现一个简单RPC框架的示例
Oct 28 Python
Python中异常处理用法
Nov 27 Python
代码复现python目标检测yolo3详解预测
讲解Python实例练习逆序输出字符串
May 06 #Python
python turtle绘图
May 04 #Python
python blinker 信号库
May 04 #Python
python三子棋游戏
May 04 #Python
python神经网络 使用Keras构建RNN训练
May 04 #Python
python神经网络学习 使用Keras进行回归运算
May 04 #Python
You might like
php输出xml格式字符串(用的这个)
2012/07/12 PHP
php的GD库imagettftext函数解决中文乱码问题
2015/01/24 PHP
php数组函数array_key_exists()小结
2015/12/10 PHP
extjs 学习笔记 四 带分页的grid
2009/10/20 Javascript
jQuery中需要注意的细节问题小结
2011/12/06 Javascript
关于js日期转化为毫秒数“节省20%的效率和和节省9个字符“问题
2012/03/01 Javascript
jquery动画2.元素坐标动画效果(创建一个图片走廊)
2012/08/24 Javascript
js实现浏览器的各种菜单命令比如打印、查看源文件等等
2013/10/24 Javascript
js/jquery去掉空格,回车,换行示例代码
2013/11/05 Javascript
浅析jquery某一元素重复绑定的问题
2014/01/03 Javascript
js中直接声明一个对象的方法
2014/08/10 Javascript
js判断文本框剩余可输入字数的方法
2015/02/04 Javascript
javascript实现延时显示提示框特效代码
2016/04/27 Javascript
超实用的JavaScript代码段 附使用方法
2016/05/22 Javascript
JS数组返回去重后数据的方法解析
2017/01/03 Javascript
vue-cli的webpack模板项目配置文件分析
2017/04/01 Javascript
React中使用collections时key的重要性详解
2017/08/07 Javascript
js实现canvas保存图片为png格式并下载到本地的方法
2017/08/31 Javascript
JS简单实现点击跳转登陆邮箱功能的方法
2017/10/31 Javascript
json数据传到前台并解析展示成列表的方法
2018/08/06 Javascript
jQuery事件多次绑定与解绑问题实例分析
2019/02/19 jQuery
Cython 三分钟入门教程
2009/09/17 Python
python3+PyQt5实现自定义流体混合窗口部件
2018/04/24 Python
Python使用sqlalchemy模块连接数据库操作示例
2019/03/13 Python
TensorFlow tf.nn.max_pool实现池化操作方式
2020/01/04 Python
沙特阿拉伯网上购物:Sayidaty Mall
2018/05/06 全球购物
英国经济型酒店品牌:Travelodge
2019/12/17 全球购物
对象的序列化(serialization)类是面向流的,应如何将对象写入到随机存取文件中
2015/06/22 面试题
纺织工程专业个人求职信范文
2014/01/27 职场文书
施工安全责任书范本
2014/07/24 职场文书
婚前协议书标准版
2014/10/19 职场文书
售后服务承诺函格式
2015/01/21 职场文书
鲁迅故里导游词
2015/02/05 职场文书
实习生个人总结范文
2015/02/28 职场文书
小王子读书笔记
2015/06/29 职场文书
JavaScript数组 几个常用方法总结
2021/11/11 Javascript