import torch
from torchvision import transforms
from torchvision import datasets
from torch.utils.data import DataLoader
import torch.nn.functional as F
import torch.optim as optim
#准备数据集
batch_size=64
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307, ),(0.3081, ))
])
train_dataset=datasets.MNIST(root=r'D:\Python-Code\深度学习\data\MNIST',
train=True,
download=True,
transform=transform)
train_loader=DataLoader(train_dataset,
shuffle=True,
batch_size=batch_size)
test_dataset=datasets.MNIST(root=r'D:\Python-Code\深度学习\data\MNIST',
train=False,
download=True,
transform=transform)
test_loader=DataLoader(test_dataset,
shuffle=False,
batch_size=batch_size)
#设计模型
class Net(torch.nn.Module):
def __init__(self):
super(Net,self).__init__()
self.l1=torch.nn.Linear(784,512)
self.l2=torch.nn.Linear(512,256)
self.l3=torch.nn.Linear(256,128)
self.l4=torch.nn.Linear(128,64)
self.l5=torch.nn.Linear(64,10)
def forward(self,x):
x=x.view(-1,784)
x = F.relu(self.l1(x))
x = F.relu(self.l2(x))
x = F.relu(self.l3(x))
x = F.relu(self.l4(x))
return self.l5(x)
model=Net()
# 损失函数和优化器
criterion=torch.nn.Cr
【基于PyTorch的简单多层感知机(MLP)神经网络(深度学习经典代码实现)】
于 2024-11-13 15:40:11 首次发布