报错代码：
if __name__ == '__main__':
model = Perception(2, 3, 2).cuda()
input = torch.randn(4, 2).cuda()
output = model(input)
# output = output.cuda()
label = torch.Tensor([0, 1, 1, 0]).long()
criterion = nn.CrossEntropyLoss()
loss_nn = criterion(output, label)
print(loss_nn)
loss_functional = F.cross_entropy(output, label)
print(loss_functional)
报错截图如下：
RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu! (when checking argument for argument target in method wrapper_nll_loss_forward)
报这个错的原因在于，代码中的Tensor，一会在CPU中运行，一会在GPU中运行，所以最好是都放在同一个device中执行
核心代码：
device = torch.device('cuda:0')
并且将用到的Tensor都改为同一个device： Tensor.to(device)
上述代码修改后：
if __name__ == '__main__':
device = torch.device('cuda:0')
model = Perception(2, 3, 2).to(device)
input = torch.randn(4, 2).to(device)
output = model(input).to(device)
label = torch.Tensor([0, 1, 1, 0]).long().to(device)
criterion = nn.CrossEntropyLoss()
loss_nn = criterion(output, label).to(device)
print(loss_nn)
loss_functional = F.cross_entropy(output, label)
print(loss_functional)
这样就不会报错了
完整代码：
import torch
from torch import nn
import torch.nn.functional as F
from torch.nn import Linear
class linear(nn.Module): # 继承nn.Module
def __init__(self, in_dim, out_dim):
super(Linear, self).__init__() # 调用nn.Module的构造函数
# 使用nn.Parameter来构造需要学习的参数
self.w = nn.Parameter(torch.randn(in_dim, out_dim))
self.b = nn.Parameter(torch.randn(out_dim))
# 在forward中实现前向传播过程
def forward(self, x):
x = x.matmul(self.w)
y = x + self.b.expand_as(x) # expand_as保证矩阵形状一致
return y
class Perception(nn.Module):
def __init__(self, in_dim, hid_dim, out_dim):
super(Perception, self).__init__()
self.layer = nn.Sequential(
nn.Linear(in_dim, hid_dim),
nn.Sigmoid(),
nn.Linear(hid_dim, out_dim),
nn.Sigmoid()
)
# self.layer1 = Linear(in_dim, hid_dim)
# self.layer2 = Linear(hid_dim, out_dim)
def forward(self, x):
# x = self.layer1(x)
# y = torch.sigmoid(x)
# y = self.layer2(y)
# y = torch.sigmoid(y)
y = self.layer(x)
return y
if __name__ == '__main__':
device = torch.device('cuda:0')
model = Perception(2, 3, 2).to(device)
input = torch.randn(4, 2).to(device)
output = model(input).to(device)
# output = output.cuda()
label = torch.Tensor([0, 1, 1, 0]).long().to(device)
criterion = nn.CrossEntropyLoss()
loss_nn = criterion(output, label).to(device)
print(loss_nn)
loss_functional = F.cross_entropy(output, label)
print(loss_functional)
Original: https://www.cnblogs.com/seansheep/p/16020753.html
Author: 在青青草原上抓羊
Title: 【深度学习】RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu!
原创文章受到原创版权保护。转载请注明出处：https://www.johngo689.com/608997/
转载文章受原作者版权保护。转载请注明原作者出处！