*Memos:
- My post explains how to set and get dtype.
- My post explains how to set and get device.
- My post explains how to set requires_grad and get grad.
- My post explains how to set
outargument.
You can set keepdim argument as shown below:
*Memos:
- I selected some popular
keepdimargument functions such as sum(), prod() mean(), median(), min(), max(), argmin(), argmax(), all() and any(). - Basically,
keepdim(Optional-Default:False-Type:bool) can keep the dimension ofinputtensor. - Sometimes,
keepdimneeds to be used withdim.
sum(). *My post explains sum():
import torch my_tensor = torch.tensor([1, 2, 3, 4]) torch.sum(input=my_tensor) torch.sum(input=my_tensor, dim=0) # tensor(10) torch.sum(input=my_tensor, dim=0, keepdim=True) # tensor([10]) prod(). *My post explains prod():
import torch my_tensor = torch.tensor([1, 2, 3, 4]) torch.prod(input=my_tensor) torch.prod(input=my_tensor, dim=0) # tensor(24) torch.prod(input=my_tensor, dim=0, keepdim=True) # tensor([24]) mean(). *My post explains mean():
import torch my_tensor = torch.tensor([5., 4., 7., 7.]) torch.mean(input=my_tensor) torch.mean(input=my_tensor, dim=0) # tensor(5.7500) torch.mean(input=my_tensor, dim=0, keepdim=True) tensor([5.7500]) median(). *My post explains median():
import torch my_tensor = torch.tensor([5, 4, 7, 7]) torch.median(input=my_tensor, dim=0) # torch.return_types.median( # values=tensor(5), # indices=tensor(0)) torch.median(input=my_tensor, dim=0, keepdim=True) # torch.return_types.median( # values=tensor([5]), # indices=tensor([0])) min(). *My post explains min():
import torch my_tensor = torch.tensor([5, 4, 7, 7]) torch.min(input=my_tensor, dim=0) # torch.return_types.min( # values=tensor(4), # indices=tensor(1)) torch.min(input=my_tensor, dim=0, keepdim=True) # torch.return_types.min( # values=tensor([4]), # indices=tensor([1])) max(). *My post explains max():
import torch my_tensor = torch.tensor([5, 4, 7, 7]) torch.max(input=my_tensor, dim=0) # torch.return_types.max( # values=tensor(7), # indices=tensor(2)) torch.max(input=my_tensor, dim=0, keepdim=True) # torch.return_types.max( # values=tensor([7]), # indices=tensor([2])) argmin(). *My post explains argmin():
import torch my_tensor = torch.tensor([5, 4, 7, 7]) torch.argmin(input=my_tensor) torch.argmin(input=my_tensor, dim=0) # tensor(1) torch.argmin(input=my_tensor, keepdim=True) torch.argmin(input=my_tensor, dim=0, keepdim=True) # tensor([1]) argmax(). *My post explains argmax():
import torch my_tensor = torch.tensor([5, 4, 7, 7]) torch.argmax(input=my_tensor) torch.argmax(input=my_tensor, dim=0) # tensor(2) torch.argmax(input=my_tensor, keepdim=True) torch.argmax(input=my_tensor, dim=0, keepdim=True) # tensor([2]) all(). *My post explains all():
import torch my_tensor = torch.tensor([True, False, True, False]) torch.all(input=my_tensor) torch.all(input=my_tensor, dim=0) # tensor(False) torch.all(input=my_tensor, keepdim=True) torch.all(input=my_tensor, dim=0, keepdim=True) # tensor([False]) any(). *My post explains any():
import torch my_tensor = torch.tensor([True, False, True, False]) torch.any(input=my_tensor) torch.any(input=my_tensor, dim=0) # tensor(True) torch.any(input=my_tensor, keepdim=True) torch.any(input=my_tensor, dim=0, keepdim=True) # tensor([True]) 
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