pytorch框架 手写CIFAR10 model

# 手写CIFAR10 model 结构

# stride = 1 padding = 2
import torch

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


class Kert(nn.Module):
    def __init__(self):
        super(Kert, self).__init__()
        # self.conv1 = Conv2d(3, 32, 5, padding=2)
        # self.pool1 = MaxPool2d(2)
        # self.conv2 = Conv2d(32, 32, 5, padding=2)
        # self.pool2 = MaxPool2d(2)
        # self.conv3 = Conv2d(32, 64, 5, padding=2)
        # self.pool3 = MaxPool2d(2)
        # self.flatten = Flatten()
        # self.linear1 = Linear(1024, 64)
        # self.linear2 = Linear(64, 10)

        # 利用sequential整合层
        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.pool1(x)
        # x = self.conv2(x)
        # x = self.pool2(x)
        # x = self.conv3(x)
        # x = self.pool3(x)
        # x = self.flatten(x)
        # x = self.linear1(x)
        # x = self.linear2(x)

        x = self.model1(x)
        return x


kert = Kert()
print(kert)

# 检验网络
input = torch.ones((64, 3, 32, 32))
output = kert(input)
print(output.shape)

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

参考资料

PyTorch深度学习快速入门教程（绝对通俗易懂！）【小土堆】_哔哩哔哩_bilibili