CIFAR-10模型训练

利用上一篇文章搭建的卷积神经网络进行模型训练。

将搭建的卷积神经网络放在model.py中。

import torch
from torch import nn
from torch.nn import Conv2d, MaxPool2d, Flatten, Linear


class Gao(nn.Module):
    def __init__(self):
        super(Gao, self).__init__()
        self.model = nn.Sequential(
            Conv2d(3, 32, 5, stride=1, padding=2),
            MaxPool2d(2),
            Conv2d(32, 32, 5, stride=1, padding=2),
            MaxPool2d(2),
            Conv2d(32, 64, 5, stride=1, padding=2),
            MaxPool2d(2),
            Flatten(),
            Linear(1024, 64),
            Linear(64, 10)
        )
    def forward(self, x):
        x = self.model(x)
        return x

if __name__ == '__main__':
    gao=Gao()
    input = torch.ones((64, 3, 32, 32))
    output = gao(input)
    print(output.shape)

下面是训练模型的主要代码：

import torch
import torchvision
from torch.utils.tensorboard import SummaryWrit