运行程序如下:
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.cuda()))
predict = predict.data.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 = predict.data.numpy()
TypeError: can't convert CUDA tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first.
意思是:如果想把CUDA tensor格式的数据改成numpy时,需要先将其转换成cpu float-tensor随后再转到numpy格式。 numpy不能读取CUDA tensor 需要将它转化为 CPU tensor
将predict.data.numpy()
改为predict.data.cpu().numpy()
即可
今天的文章TypeError: can‘t convert CUDA tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory fi分享到此就结束了,感谢您的阅读。
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