我想用线性的模型训练一下MNIST数据集,在运行到
def train(epoch):
running_loss = 0.0
for batch_idx,data in enumerate(train_loader):
语句后显示
IndexError: too many indices for tensor of dimension 0
这是为什么呢??甚至都还没到将数据放到模型中训练,应该和模型没关系,我也看了其他人的代码,在加载数据这些代码中也没找到什么问题。所以在这请教一下大神,万分感谢Orz
以下是我的代码:
import torch
from torch.utils.data import DataLoader
from torchvision import transforms
from torchvision import datasets
import torch.nn.functional as F
import torch.optim as optim
batch_size = 64
transform = transforms.Compose([transforms.ToTensor(),transforms.Normalize((0.1307),(0.3081))])
train_dataset = datasets.MNIST(root='./data',
train=True,
download=True,
transform=transform)
train_loader = DataLoader(dataset=train_dataset,
shuffle=True,
batch_size=batch_size)
test_dataset = datasets.MNIST(root='./data',
train=False,
download=True,
transform=transform)
test_loader = DataLoader(dataset=test_dataset,
shuffle=False,
batch_size=batch_size)
class Net(torch.nn.Module):
def __init__(self):#构造函数
super(Net,self).__init__()
self.linear1 = torch.nn.Linear(784,512)
self.linear2 = torch.nn.Linear(512,256)
self.linear3 = torch.nn.Linear(256,128)
self.linear4 = torch.nn.Linear(128,64)
self.linear5 = torch.nn.Linear(64,10)
def forward(self, x):
x=x.view(-1,784)
x = F.relu(self.linear1(x))
x = F.relu(self.linear2(x))
x = F.relu(self.linear3(x))
x = F.relu(self.linear4(x))
return self.linear5(x)
model = Net()#实例化模型
criterion = torch.nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(),lr=0.1,momentum=0.5)#lr为学习率
def train(epoch):
running_loss = 0.0
for batch_idx,data in enumerate(train_loader):
inputs, target = data
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, target)
loss.backward()
optimizer.step()
running_loss +=loss.item()
if batch_idx%300 == 299:
print('[%d,%5d] loss: %.3f' %(epoch+1,batch_idx+1,running_loss/300))
running_loss = 0.0
if __name__=='__main__':
for epoch in range(10):
train(epoch)