*My post explains empty_strided().
empty() can create the 0D or more D tensor of the zero or more floating-point numbers(Default), integers, complex numbers or boolean values from uninitialized memory which are called uninitialized data as shown below:
*Memos:
-
empty()
can be used with torch but not with a tensor. - The 1st or more arguments with
torch
aresize
(Required-Type:int
,tuple
ofint
,list
ofint
or size()). - There is
dtype
argument withtorch
(Optional-Type:dtype): *Memos:- If
dtype
is not given, get_default_dtype() is used. *My post explainsget_default_dtype()
and set_default_dtype(). -
dtype=
must be used. -
My post explains
dtype
argument.
- If
- There is
device
argument withtorch
(Optional-Type:str
,int
or device()): *Memos:- If
device
is not given, get_default_device() is used. *My post explainsget_default_device()
and set_default_device(). -
device=
must be used. -
My post explains
device
argument.
- If
- There is
requires_grad
argument withtorch
(Optional-Type:bool
): *Memos:-
requires_grad=
must be used. -
My post explains
requires_grad
argument.
-
- There is
out
argument withtorch
(Optional-Type:tensor
): *Memos:-
out=
must be used. -
My post explains
out
argument.
-
- You can use torch.Tensor() or torch.FloatTensor() like
torch.Tensor(3, 2, 4)
ortorch.FloatTensor(3, 2, 4)
because they can do the same job asempty()
. *torch.Tensor()
is the alias oftorch.FloatTensor()
by default. - Uninitialized memory has data but the data is unknown.
import torch
torch.empty(size=())
torch.empty(size=torch.tensor(8).size())
# tensor(3.6404e-27)
torch.empty(size=(0,))
torch.empty(0)
torch.empty(size=torch.tensor([]).size())
# tensor([])
torch.empty(size=(3,))
torch.empty(3)
torch.empty(size=torch.tensor([8, 3, 6]).size())
# tensor([-1.3610e+13, 4.4916e-41, -1.3610e+13])
torch.empty(size=(3, 2))
torch.empty(3, 2)
torch.empty(size=torch.tensor([[8, 3], [6, 0], [2, 9]]).size())
# tensor([[-1.3610e+13, 4.4916e-41],
# [5.7850e-23, 3.1100e-41],
# [4.4842e-44, 0.0000e+00]])
torch.empty(size=(3, 2, 4))
torch.empty(3, 2, 4)
# tensor([[[3.8848e-23, 3.1100e-41, 0.0000e+00, 0.0000e+00],
# [3.3892e-23, 3.1100e-41, 3.0224e-26, 3.1100e-41]],
# [[-6.0464e-34, 4.4914e-41, 0.0000e+00, 0.0000e+00],
# [0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00]],
# [[0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00],
# [0.0000e+00, 0.0000e+00, 1.4013e-45, 0.0000e+00]]])
torch.empty(size=(3, 2, 4), dtype=torch.int64)
torch.empty(3, 2, 4, dtype=torch.int64)
# tensor([[[136263006428688, 96270204571280, 1, 96270203986320],
# [0, 0, 96270208839376, 96270118417696]],
# [[136257315028352, 0, 0, 0],
# [0, 0, 0, 1]],
# [[0, 0, 0, 0],
# [0, 1, 352951805673479, 2542620672001]]])
torch.empty(size=(3, 2, 4), dtype=torch.complex64)
torch.empty(3, 2, 4, dtype=torch.complex64)
# tensor([[[1.4167e-07+4.4458e-41j, 1.4167e-07+4.4458e-41j,
# 4.4842e-44+0.0000e+00j, 1.5695e-43+0.0000e+00j],
# [-1.4883e+19+3.1404e-41j, 0.0000e+00+0.0000e+00j,
# 1.4013e-45+0.0000e+00j, -4.9888e-15+3.1409e-41j]],
# [[-2.4481e+37+4.4456e-41j, -4.9888e-15+3.1409e-41j,
# 9.1477e-41+0.0000e+00j, 8.9683e-44+0.0000e+00j],
# [3.5873e-43+0.0000e+00j, -2.6273e+37+4.4456e-41j,
# 0.0000e+00+0.0000e+00j, 0.0000e+00+0.0000e+00j]],
# [[0.0000e+00+0.0000e+00j, 2.4803e-43+0.0000e+00j,
# -4.6535e-15+3.1409e-41j, -3.2145e-15+3.1409e-41j],
# [0.0000e+00+0.0000e+00j, 1.4013e-45+0.0000e+00j,
# -1.7014e+38+1.1515e-40j, 4.5919e-41+8.2957e-43j]]])
torch.empty(size=(3, 2, 4), dtype=torch.bool)
torch.empty(3, 2, 4, dtype=torch.bool)
# tensor([[[True, True, True, True],
# [True, False, False, False]],
# [[True, True, True, True],
# [True, True, False, False]],
# [[False, True, False, False],
# [False, False, False, False]]])
empty_like() can replace the zero or more numbers of a 0D or more D tensor with the zero or more floating-point numbers, integers, complex numbers or boolean values from uninitialized memory which are called uninitialized data as shown below:
*Memos:
-
empty_like()
can be used withtorch
but not with a tensor. - The 1st argument with
torch
isinput
(Required-Type:tensor
ofint
,float
,complex
orbool
). - There is
dtype
argument withtorch
(Optional-Type:dtype): *Memos:- If
dtype
is not given, it is inferred frominput
. -
dtype=
must be used. -
My post explains
dtype
argument.
- If
- There is
device
argument withtorch
(Optional-Type:str
,int
or device()): *Memos:- If
device
is not given, it is inferred frominput
. -
device=
must be used. -
My post explains
device
argument.
- If
- There is
requires_grad
argument withtorch
(Optional-Type:bool
): *Memos:-
requires_grad=
must be used. -
My post explains
requires_grad
argument.
-
- Uninitialized memory has data but the data is unknown.
import torch
my_tensor = torch.tensor(7.)
torch.empty_like(input=my_tensor)
# tensor(-1.3610e+13)
my_tensor = torch.tensor([7., 4., 5.])
torch.empty_like(input=my_tensor)
# tensor([2.8244e+23, 4.4787e-41, -5.7316e-07])
my_tensor = torch.tensor([[7., 4., 5.], [2., 8., 3.]])
torch.empty_like(input=my_tensor)
# tensor([[-4.7415e-07, 3.1221e-41, -6.4098e-07],
# [3.1221e-41, 1.1210e-43, 0.0000e+00]])
my_tensor = torch.tensor([[[7., 4., 5.], [2., 8., 3.]],
[[6., 0., 1.], [5., 9., 4.]]])
torch.empty_like(input=my_tensor)
# tensor([[[-6.6094e-07, 3.1221e-41, -3.9661e-07],
# [3.1221e-41, 8.9683e-44, 0.0000e+00]],
# [[1.1210e-43, 0.0000e+00, -8.9451e+02],
# [3.1228e-41, 1.7282e-04, 1.2471e+16]]])
my_tensor = torch.tensor([[[7, 4, 5], [2, 8, 3]],
[[6, 0, 1], [5, 9, 4]]])
torch.empty_like(input=my_tensor)
# tensor([[[137273168313840, 95694909291296, 1],
# [95694912519088, 95694842532640, 0]],
# [[95694862074384, 95694820258896, 137269160918960],
# [0, 0, 0]]])
my_tensor = torch.tensor([[[7.+4.j, 4.+2.j, 5.+3.j],
[2.+5.j, 8.+1.j, 3.+9.j]],
[[6.+9.j, 0.+3.j, 1.+8.j],
[5.+3.j, 9.+4.j, 4.+6.j]]])
torch.empty_like(input=my_tensor)
# tensor([[[6.7127e-07+1.7183e-04j,
# 1.6519e-04+1.0187e-11j,
# 2.0661e+20+6.8629e-07j],
# [1.8077e-43+0.0000e+00j,
# -4.3084e-07+3.1221e-41j,
# -3.8936e-07+3.1221e-41j]],
# [[4.4842e-44+0.0000e+00j,
# 4.4842e-44+0.0000e+00j,
# -8.7266e+02+3.1228e-41j],
# [2.8026e-45+0.0000e+00j,
# 4.2039e-45+0.0000e+00j,
# 9.1084e-44+0.0000e+00j]]])
my_tensor = torch.tensor([[[True, False, True], [False, True, False]],
[[True, False, True], [False, True, False]]])
torch.empty_like(input=my_tensor)
# tensor([[[True, True, True],
# [True, True, False]],
# [[False, False, True],
# [True, True, True]]])
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