我在GPU中使用Python3.x和pytors1.5.0。我试图用mnist数据编写一个简单的多项式logistic回归。
我的问题是loss()函数在遍历培训批时抛出一个TypeError: 'Tensor' object is not callable。让我困惑的是,错误并不出现在循环的第一次迭代中,但是对于第二批,我得到了下面的完整错误:。
Traceback (most recent call last):
File "/snap/pycharm-community/207/plugins/python-ce/helpers/pydev/pydevd.py", line 1448, in _exec
pydev_imports.execfile(file, globals, locals) # execute the script
File "/snap/pycharm-community/207/plugins/python-ce/helpers/pydev/_pydev_imps/_pydev_execfile.py", line 18, in execfile
exec(compile(contents+"\n", file, 'exec'), glob, loc)
File "/home/pytorch_tutorial/Pytorch_feed_fwd_310720.py", line 78, in <module>
loss = loss(preds,ys)
TypeError: 'Tensor' object is not callable这里的损耗()函数只是loss = nn.CrossEntropyLoss()函数。完整的代码如下。任何指点都是非常欢迎的。
for epoch in range(5):
running_loss = 0.0
for i, data in enumerate(trainloader, 0):
xs, ys = data
opt.zero_grad()
preds = net(xs)
loss = loss(preds,ys)
loss.backward()
opt.step()
# print statistics
running_loss += loss.item()
if i % 1000 == 999: # print every 1000 mini-batches
print('[%d, %5d] loss: %.3f' %
(epoch + 1, i + 1, running_loss / 2000))
running_loss = 0.0
print('epoch {}, loss {}'.format(epoch, loss.item()))
a=1发布于 2020-08-01 10:29:58
这是因为您要在循环中本地设置loss。
将loss = loss(preds, ys)更改为_loss = loss(preds, ys)
https://stackoverflow.com/questions/63204176
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