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CNN模型

import torchfrom torch import nnfrom torch.nn import functional as F'''step2. Net Creat   网络创建'''class Lenet5(nn.Module):    def __init__(self):        super(Lenet5, self).__init__()        self.conv_unit = nn.Sequential(                                                # Sequential不需要给每个层编号,同样也就没有赋值 ，因为总的来说它时一个整体；            # x: [b, 3, 32, 32]=> [b, 16, size]            nn.Conv2d(3, 16, kernel_size=5, stride=1, padding=0),       # in_channels=3代表 3个通道in 其实就是RGB这三个，out_channels =16            nn.MaxPool2d(kernel_size=2, stride=2, padding=0),          # kernel_size=2 kernel一次看一个长宽各2的窗口            nn.Conv2d(16, 32, kernel_size=5, stride=1, padding=0),            nn.MaxPool2d(kernel_size=2, stride=2, padding=0),        )        # flatten打平         从32*5*5 -> 10        # 全连接层        self.fc_unit = nn.Sequential(            nn.Linear(32*5*5, 120),            nn.ReLU(),            nn.Linear(120, 84),            nn.ReLU(),            nn.Linear(84, 10)        )        # [b, 3, 32, 32]                            测试1，看conv_unit后的out_channels数        # tmp =  torch.randn(2, 3, 32, 32)        # out = self.conv_unit(tmp)        # # [b, 16, 5, 5]        # print('conv out: ',out.shape)        # use Cross Entropy Loss        # self.criteon = nn.MSELoss()               # 处理逼近问题        # self.criteon = nn.CrossEntropyLoss()      # 处理分类问题    def forward(self, x):                    # 前向路径   #param(参数) x:[b, 3, 32, 32]        batchsz = x.size(0)                  #param(参数) x:[b, 3, 32, 32] 其中的 b        # [b, 3, 32, 32] => [b, 16, 5, 5]        x = self.conv_unit(x)        # flatten打平         从16*5*5 -> 10        x = x.view(batchsz, 32*5*5)        # [b, 16*5*5] => [b, 10]        logits = self.fc_unit(x)        # logits： 网络最后一般送入softmax 那么 softmax前的变量 统称 logits        return logits# def main():#     net = Lenet5()#     tmp = torch.randn(2, 3, 32, 32)#     out = net(tmp)#     # [b, 16, 5, 5]#     print('lenet5 out: ', out.shape)### if __name__ == '__main__':#     main()

查看经过CNN后的维度

[b, 3, 32, 32]

测试 : 为了看conv_unit后的out_channels数, 其中b为batch_size

结果展示

我自己从0又写了一遍

这次我写的是Minist手写体识别

方法1

仅通过__init__看结果

__ init __嘛～，你用那个类，它自动就会走 __ init __

import torchimport torch.nn as nnimport torch.nn.functional as Ffrom torchvision import datasets, transformsimport torch.utils.dataclass TeacherModel(nn.Module):    def __init__(self):        super(TeacherModel, self).__init__()        self.conv1 = nn.Conv2d(1, 32, 3, 1)        self.conv2 = nn.Conv2d(32, 64, 3, 1)        self.dropout1 = nn.Dropout2d(0.3)        self.dropout2 = nn.Dropout2d(0.5)        tmp = torch.randn(2, 1, 28, 28)        out = self.conv1(tmp)        out = self.conv2(out)        out = F.max_pool2d(out, 2)        out = torch.flatten(out, 1)        print('out->', out.shape)net = TeacherModel()

方法2

通过__init__ 与 def forward(self, x)看结果

import torchimport torch.nn as nnimport torch.nn.functional as Ffrom torchvision import datasets, transformsimport torch.utils.dataclass TeacherModel(nn.Module):    def __init__(self):        super(TeacherModel, self).__init__()        self.conv1 = nn.Conv2d(1, 32, 3, 1)        self.conv2 = nn.Conv2d(32, 64, 3, 1)        self.dropout1 = nn.Dropout2d(0.3)        self.dropout2 = nn.Dropout2d(0.5)    def forward(self, x):        batchsz = x.size(0)        x = self.conv1(x)        x = F.relu(x)        x = self.conv2(x)        x = F.relu(x)        x = F.max_pool2d(x, 2)        x = self.dropout1(x)        x = torch.flatten(x, 1)        return xtmp = torch.randn(2, 1, 28, 28)net = TeacherModel()out = net(tmp)print('out:', out.shape)

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