Cifar10模型的手动实现

CIfar10模型如下

针对给出的模型，我们手动实现，代码如下

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


class Cifar10(nn.Module):
    def __init__(self):
        super(Cifar10, self).__init__()
        self.module=Sequential(
            Conv2d(in_channels=3,out_channels=32,kernel_size=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,input):
        output=self.module(input)
        return output

tudiu=Cifar10()
#构建一个bach_size=64,通道为3,长和宽为32，且里面数组都是1的tensor
input=torch.ones((64,3,32,32))
output=tudiu(input)
print(output.shape)
writer=SummaryWriter("logs")
writer.add_graph(tudiu,input)
writer.close()

在终端输入：tensorboard --logdir=logs,打开tensorboard

可以形象的看到该模型的每一个层，双击还可以看到权重参数和偏置值，很有意思。