深度学习-图像分类(mnist)

# Max pool


import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.features = nn.Sequential(
            nn.Conv2d(1, 64, kernel_size=3, stride=1, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),
        )
        self.classifier = nn.Sequential(
            nn.Dropout(),
            nn.Linear(128 * 7 * 7, 128),
            nn.ReLU(inplace=True),
            nn.Dropout(),
            nn.Linear(128, 128),
            nn.ReLU(inplace=True),
            nn.Linear(128, 10),
        )

    def forward(self, x):
        x = self.features(x)
        x = torch.flatten(x, 1)
        x = self.classifier(x)
        return F.log_softmax(x, dim=1)


def train(model, device, train_loader, optimizer, epoch):
    # 模型设置为训练模式, 等价于 model.train(True)
    # 主要涉及 Dropout和 BatchNorm层
    # 查到的一些博客中的解释(不一定准确)
    # train时, BN用每一批数据的均值方差; eval时, BN用所有训练数据的均值方差
    # train时, Dropout随机启用神经元; eval时, 用所有神经元
    model.train()
    for idx, (data, target) in enumerate(train_loader):
        data, target = data.to(device), target