import torch
import torchvision
from torchvision import datasets, transforms
from torch.autograd import Variable
import matplotlib.pyplot as plt
# 数据预处理
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.5], std=[0.5])
])
# 加载数据集
data_train = datasets.MNIST(root='./data/',
transform=transform,
train=True,
download=True)
data_test = datasets.MNIST(root='./data/',
transform=transform,
train=False)
# 创建数据加载器
data_loader_train = torch.utils.data.DataLoader(dataset=data_train,
batch_size=64,
shuffle=True)
data_loader_test = torch.utils.data.DataLoader(dataset=data_test,
batch_size=64,
shuffle=True)
# 定义模型
class Model(torch.nn.Module):
def __init__(self):
super(Model, self).__init__()
self.conv1 = torch.nn.Sequential(
torch.nn.Conv2d(1, 64, kernel_size=3, stride=1, padding=1),
torch.nn.ReLU(),
torch.nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1),
torch.nn.ReLU(),
torch.nn.MaxPool2d(stride=2, kernel_size=2)
)
self.dense = torch.nn.Sequential(
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