PyTorch入门——实现MNIST分类

import torch
from matplotlib import pyplot as plt
from torch import nn, optim
# from torch.autograd import Variable
from torch.utils.data import DataLoader
from torchvision import datasets, transforms
from tqdm import tqdm
from matplotlib.ticker import MaxNLocator

# 超参数
batch_size = 256  # 批大小
learning_rate = 0.0001  # 学习率
epochs = 20  # 迭代次数
channels = 1  # 图像通道大小

# 数据集下载和预处理
transform = transforms.Compose([transforms.ToTensor(),  # 将图片转换成PyTorch中处理的对象Tensor,并且进行标准化0-1
                                transforms.Normalize([0.5], [0.5])])  # 归一化处理
path = './data/'  # 数据集下载后保存的目录
# 下载训练集和测试集
trainData = datasets.MNIST(path, train=True, transform=transform, download=True)
testData = datasets.MNIST(path, train=False, transform=transform)
# 处理成data loader
trainDataLoader = torch.utils.data.DataLoader(dataset=trainData, batch_size=batch_size, shuffle=True)  # 批量读取并打乱
testDataLoader = torch.utils.data.DataLoader(dataset=testData, batch_size=batch_size)


# 开始构建cnn模型
class cnn(torch.nn.Module):
    def __init__(self):
        super(cnn, self).__init__()
        self.model = torch.nn.Sequential(
            # The size of the picture is 28*28
            torch.nn.Conv2d(in_channels=1, out_channels=32, kernel_size=3, stride=1, padding=1),
            torch.nn.ReLU(),
            torch.nn.Ma