使用pytorch搭建GoogLeNet网络_pytorch googlenet network

 1.网络结构

 

 

 

 

 

github代码 pytorch搭建经典网络模型 

数据集 http://download.tensorflow.org/example_images/flower_photos.tgz

将数据集执行split_data.py脚本自动将数据集划分成训练集train和验证集val

|—— flower_data
|———— flower_photos（解压的数据集文件夹，3670个样本）
|———— train（生成的训练集，3306个样本）
|———— val（生成的验证集，364个样本）

代码相应部分：https://blog.csdn.net/com_fang_bean/article/details/108479808

1.model.py  

import torch.nn as nn
import torch
import torch.nn.functional as F
 
# 4.定义googlenet网络
# aux_logits=True：使用辅助分类器。
# init_weights=False：初始化权重。
# self.aux_logits = aux_logits->把是否使用辅助分类器传入到类变量当中。
# ceil_mode=True->代表卷积后参数向上取整
class GoogLeNet(nn.Module):
    def __init__(self, num_classes=1000, aux_logits=True, init_weights=False):
        super(GoogLeNet, self).__init__()
        self.aux_logits = aux_logits
 
        self.conv1 = BasicConv2d(3, 64, kernel_size=7, stride=2, padding=3)
        self.maxpool1 = nn.MaxPool2d(3, stride=2, ceil_mode=True)
 
        self.conv2 = BasicConv2d(64, 64, kernel_size=1)
        self.conv3 = BasicConv2d(64, 192, kernel_size=3, padding=1)
        self.maxpool2 = nn.MaxPool2d(3, stride=2, ceil_mode=True)
        # 按照inception结构（该层输入大小[上层输出大小],1x1,3x3reduce,3x3,5x5reduce,5x5）来写
        self.inception3a = Inception(192, 64, 96, 128, 16, 32, 32)
        self.inception3b = Inception(256, 128, 128, 192, 32, 96, 64)
        self.maxpool3 = nn.MaxPool2d(3, stride=2, ceil_mode=True)
 
        self.inception4a = Inception(480, 192, 96, 208, 16, 48, 64)
        self.inception4b = Inception(512, 160, 112, 224, 24, 64, 64)
        self.inception4c = Inception(512, 128, 128, 256, 24, 64, 64)
        self.inception4d = Inception(512, 112, 144, 288, 32, 64, 64)
        self.inception4e = Inception(528, 256, 160, 320, 32, 128, 128)
        self.maxpool4 = nn.MaxPoo