PyTorch搭建小实践

import torch
from torch import nn
from torch.nn import Conv2d, MaxPool2d, Flatten, Linear, Sequential
from torch.utils.tensorboard import SummaryWriter


class Model(nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        # self.conv1 = Conv2d(3, 32, 5, padding = 2)
        # self.maxpool1 = MaxPool2d(2)
        # self.conv2 = Conv2d(32, 32, 5, padding = 2)
        # self.maxpool2 = MaxPool2d(2)
        # self.conv3 = Conv2d(32, 64, 5, padding = 2)
        # self.maxpool3 = MaxPool2d(2)
        # self.flatten = Flatten()
        # self.linear1 = Linear(1024, 64)
        # self.linear2 = Linear(64, 10)
        self.model1 = Sequential(
            Conv2d(3, 32, 5, padding = 2),
            MaxPool2d(2),
            Conv2d(32, 32, 5, padding = 2),
            MaxPool2d(2),
            Conv2d(32, 64, 5, padding = 2),
            MaxPool2d(2),
            Flatten(),
            Linear(1024, 64),
            Linear(64, 10)
        )

    def forward(self, x):
        # x = self.conv1(x)
        # x = self.maxpool1(x)
        # x = self.conv2(x)
        # x = self.maxpool2(x)
        # x = self.conv3(x)
        # x = self.maxpool3(x)
        # x = self.flatten(x)
        # x = self.linear1(x)
        # x = self.linear2(x)
        x = self.model1(x)
        return x


model = Model()
print(model)
input = torch.ones((64, 3, 32, 32))
output = model(input)
print(output.shape)

writer = SummaryWriter("logs_seq")
writer.add_graph(model, input)
writer.close()

标签：32,self,实践,MaxPool2d,padding,PyTorch,64,Conv2d,搭建
来源： https://blog.csdn.net/weixin_51296032/article/details/122738207