Set out with out argument functions PyTorch – ██FR█████ █INTELL███████████

This content originally appeared on DEV Community and was authored by Super Kai (Kazuya Ito)

You can set out with the functions which have out argument as shown below:

*Memos:

I selected some popular keepdim argument functions such as arange(), rand() add(), mean(), median(), min(), max(), all(), any() and matmul().
out(Optional, tensor) can output a tensor. *Sometimes, out(Optional, tuple(tensor, tensor) or list(tensor, tensor)).
Basically, out= is needed.
Sometimes, out needs to be used with dim.

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))

This content originally appeared on DEV Community and was authored by Super Kai (Kazuya Ito)