[人工智能]AlexNet 模型

import torch 
import torch.nn as nn
import torchvision
import numpy as np
import random

import torch 
import torchvision 
m=torchvision.models.alexnet(pretrained=True)
print(m)

class AlexNet(nn.Module):
    def __init__(self):
        super(AlexNet,self).__init__()
        # 第一层 
        self.conv1=nn.Conv2d(3,96,7,2,2) # 卷积层 
        self.relu1=nn.ReLU(inplace=True) # 激活函数层 
        self.MaxPool2d1=nn.MaxPool2d(3,2,0)# 池化层
        # 第二层 
        self.conv2=nn.Conv2d(96,256,5,1,2)
        self.relu2=nn.ReLU(inplace=True)
        self.MaxPool2d2=nn.MaxPool2d(3,2,0)  
        # 第三层 
        self.conv3=nn.Conv2d(256,384,3,1,1)
        self.relu3=nn.ReLU(inplace=True)
        # 第四层 
        self.conv4=nn.Conv2d(384,384,3,1,1)
        self.relu4=nn.ReLU(inplace=True)
        # 第五层 
        self.conv5=nn.Conv2d(384,256,3,1,1)
        self.relu5=nn.ReLU(inplace=True)
        
        
        for m in self.modules():# 进行判断把层的参数初始化为0 
            print("---------------")
            print(m)
            if isinstance(m,nn.Conv2d):
                print(isinstance(m,nn.Conv2d))
                nn.init.constant_(m.weight, 0.)
    # 前向传播 
    def forward(self, x):
        x=self.conv1(x)
        x=self.relu1(x)
        x=self.MaxPool2d1(x)
        
        x=self.conv2(x)
        x=self.relu2(x)
        x=self.MaxPool2d2(x)  

        x=self.conv3(x)
        x=self.relu3(x)

        x=self.conv4(x)
        x=self.relu4(x)

        x=self.conv5(x)
        x=self.relu5(x)
        return x
net = AlexNet()

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