*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
keepdimargument. -
My post explains
biasargument.
You can set out as shown below:
*Memos:
- I selected some popular
outargument functions such as arange(), rand() add(), mean(), median(), min(), max(), all(), any() and matmul(). - Basically,
out(Optional-Default-None-Type:tensor) can have a returned tensor. *Sometimes,out(Optional-Default-None-Type:tuple(tensor,tensor) orlist(tensor,tensor)). - Basically,
outcan be used with torch but not with a tensor. - Basically,
out=must be used. - Sometimes,
outneeds to be used withdim. - I recommend not to use
outargument because it is useless at all.
arange(). *My post explains arange():
import torch
torch.arange(start=5, end=15, step=4)
# tensor([5, 9, 13])
my_tensor = torch.tensor([0, 1, 2])
torch.arange(start=5, end=15, step=4, out=my_tensor)
# tensor([5, 9, 13])
tensor1 = torch.tensor([0, 1, 2])
tensor2 = torch.arange(start=5, end=15, step=4, out=tensor1)
tensor1, tensor2
# (tensor([5, 9, 13]), tensor([5, 9, 13]))
rand(). *My post explains rand():
import torch
tensor1 = torch.tensor([0., 1., 2.])
tensor2 = torch.rand(size=(3,), out=tensor1)
tensor1, tensor2
# (tensor([0.3379, 0.9394, 0.5509]), tensor([0.3379, 0.9394, 0.5509]))
add(). *My post explains add():
import torch
tensor1 = torch.tensor([1, 2, 3])
tensor2 = torch.tensor([4, 5, 6])
tensor3 = torch.tensor([7, 8, 9])
tensor4 = torch.add(input=tensor1, other=tensor2, out=tensor3)
tensor1, tensor2, tensor3, tensor4
# (tensor([1, 2, 3]), tensor([4, 5, 6]), tensor([5, 7, 9]), tensor([5, 7, 9]))
mean(). *My post explains mean():
import torch
tensor1 = torch.tensor([5., 4., 7., 7.])
tensor2 = torch.tensor(9.)
tensor3 = torch.mean(input=tensor1, dim=0, out=tensor2)
tensor1, tensor2, tensor3
# (tensor([5., 4., 7., 7.]), tensor(5.7500), tensor(5.7500))
median(). *My post explains median():
import torch
tensor1 = torch.tensor([5., 4., 7., 7.])
tensor2 = torch.tensor(9.)
tensor3 = torch.tensor(6)
tensor4 = torch.median(input=tensor1, dim=0, out=(tensor2, tensor3))
tensor1, tensor2, tensor3, tensor4
# (tensor([5., 4., 7., 7.]),
# tensor(5.),
# tensor(0),
# torch.return_types.median_out(
# values=tensor(5.),
# indices=tensor(0)))
min(). *My post explains min():
import torch
tensor1 = torch.tensor([5, 4, 7, 7])
tensor2 = torch.tensor(9)
tensor3 = torch.tensor(6)
tensor4 = torch.min(input=tensor1, dim=0, out=(tensor2, tensor3))
tensor1, tensor2, tensor3, tensor4
# (tensor([5, 4, 7, 7]),
# tensor(4),
# tensor(1),
# torch.return_types.min_out(
# values=tensor(4),
# indices=tensor(1)))
max(). *My post explains max():
import torch
tensor1 = torch.tensor([5, 4, 7, 7])
tensor2 = torch.tensor(9)
tensor3 = torch.tensor(6)
tensor4 = torch.max(input=tensor1, dim=0, out=(tensor2, tensor3))
tensor1, tensor2, tensor3, tensor4
# (tensor([5, 4, 7, 7]),
# tensor(7),
# tensor(2),
# torch.return_types.max_out(
# values=tensor(7),
# indices=tensor(2)))
all(). *My post explains all():
import torch
tensor1 = torch.tensor([True, False, True, False])
tensor2 = torch.tensor(True)
tensor3 = torch.all(input=tensor1, out=tensor2)
tensor3 = torch.all(input=tensor1, dim=0, out=tensor2)
tensor1, tensor2, tensor3
# (tensor([True, False, True, False]), tensor(False), tensor(False))
any(). *My post explains any():
import torch
tensor1 = torch.tensor([True, False, True, False])
tensor2 = torch.tensor(True)
tensor3 = torch.any(input=tensor1, out=tensor2)
tensor3 = torch.any(input=tensor1, dim=0, out=tensor2)
tensor1, tensor2, tensor3
# (tensor([True, False, True, False]), tensor(True), tensor(True))
matmul(). *My post explains matmul():
import torch
tensor1 = torch.tensor([2, -5, 4])
tensor2 = torch.tensor([3, 6, -1])
tensor3 = torch.tensor(7)
tensor4 = torch.matmul(input=tensor1, other=tensor2, out=tensor3)
tensor1, tensor2, tensor3, tensor4
# (tensor([2, -5, 4]), tensor([3, 6, -1]), tensor(-28), tensor(-28))
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