RuntimeError: Expected object of type torch.cuda.FloatTensor but found type torch.FloatTensor for ar

运行如下程序：

import numpy as np
import torch
from torch import nn
from torch.autograd import Variable
import matplotlib.pyplot as plt

class LinearRegression(nn.Module):
def __init__(self):
super(LinearRegression,self).__init__()
self.linear = nn.Linear(1,1)
def forward(self, x):
out = self.linear(x)
return out
x_train = np.array([[3.3],[4.4],[5.5],[6.710],[6.93],[4.168],[9.779],[6.182],[7.59],[2.167],[7.042],[10.791],[5.313],[7.997],[3.1]],dtype=np.float32)
y_train = np.array([[1.7],[2.76],[2.09],[3.19],[1.694],[1.573],[3.366],[2.596],[2.53],[1.221],[2.827],[3.465],[1.65],[2.904],[1.3]],dtype=np.float32)
x_train = torch.from_numpy(x_train)
y_train = torch.from_numpy(y_train)
num_epochs = 1000
if torch.cuda.is_available():
print("GPU1")
model = LinearRegression().cuda()
else:
print("CPU1")
model = LinearRegression()

criterion = nn.MSELoss()
optimizer = torch.optim.SGD(model.parameters(),lr=1e-3)

for epoch in  range(num_epochs):
if torch.cuda.is_available():
print('GPU2')
inputs = Variable(x_train).cuda()
target = Variable(y_train).cuda()
else:
print("CPU2")
inputs = Variable(x_train)
target = Variable(y_train)
# forward
out = model(inputs)
loss = criterion(out,target)
# backward
optimizer.zero_grad()  # 梯度归零
loss.backward()   # 反向传播
optimizer.step()  # 更新参数
# if (epoch+1) % 20 ==0:
#     print('Epoch[{}/{}], loss:{:,6f}'.format(epoch+1,num_epochs,loss.data[0]))

model.eval()

predict = model(Variable(x_train))
predict = predict.data.cpu().numpy()
plt.plot(x_train.numpy(),y_train.numpy(),'ro',label='Original data')
plt.plot(x_train.numpy(),predict,label='Fitting Line')
plt.show()

这行报错

predict = model(Variable(x_train))

RuntimeError: Expected object of type torch.cuda.FloatTensor but found type torch.FloatTensor for argument #4 'mat1'

意思是要求的目标类型是torch.cuda.FloatTensor，但是找到的数据类型是torch.FloatTensor，所以需要在数据类型后面加上

.cuda()

predict = model(Variable(x_train))

predict = model(Variable(x_train.cuda()))