You can set dtype
with the functions which have dtype
arguments and get it with dtype and type() as shown below:
*Memos:
tensor()
. *My post explains tensor()
:
import torch
my_tensor = torch.tensor([0, 1, 2])
my_tensor = torch.tensor([0, 1, 2], dtype=torch.int64)
my_tensor = torch.tensor([0, 1, 2], dtype=int)
my_tensor, my_tensor.dtype, my_tensor.type()
# (tensor([0, 1, 2]), torch.int64, 'torch.LongTensor')
my_tensor = torch.tensor([0., 1., 2.], dtype=torch.float64)
my_tensor = torch.tensor([0., 1., 2.], dtype=float)
my_tensor, my_tensor.dtype, my_tensor.type()
# (tensor([0., 1., 2.], dtype=torch.float64),
# torch.float64,
# 'torch.DoubleTensor')
my_tensor = torch.tensor([0.+7.j, 1.+4.j, 2.+5.j], dtype=torch.complex32)
my_tensor, my_tensor.dtype, my_tensor.type()
# (tensor([0.+7.j, 1.+4.j, 2.+5.j], dtype=torch.complex32),
# torch.complex32,
# 'torch.ComplexHalfTensor')
my_tensor = torch.tensor([True, False, True], dtype=torch.bool)
my_tensor = torch.tensor([True, False, True], dtype=bool)
my_tensor, my_tensor.dtype, my_tensor.type()
# (tensor([ True, False, True]), torch.bool, 'torch.BoolTensor')
arange()
. *My post explains arange()
:
import torch
my_tensor = torch.arange(start=5, end=15, step=3, dtype=torch.float64)
my_tensor, my_tensor.dtype, my_tensor.type()
# (tensor([ 5., 8., 11., 14.], dtype=torch.float64),
# torch.float64,
# 'torch.DoubleTensor')
rand()
. *My post explains rand()
:
import torch
my_tensor = torch.rand(size=(3,), dtype=torch.float64)
my_tensor, my_tensor.dtype, my_tensor.type()
# (tensor([0.4620, 0.6369, 0.5189], dtype=torch.float64),
# torch.float64,
# 'torch.DoubleTensor')
rand_like()
. *My post explains rand_like()
:
import torch
my_tensor = torch.rand_like(input=torch.tensor([7., 4., 5.]),
dtype=torch.float64)
my_tensor, my_tensor.dtype, my_tensor.type()
# (tensor([0.7677, 0.2914, 0.3266], dtype=torch.float64),
# torch.float64,
# 'torch.DoubleTensor')
sum()
. *My post explains sum()
:
import torch
my_tensor = torch.sum(input=torch.tensor([0., 1., 2., 3.]),
dtype=torch.float64)
my_tensor, my_tensor.dtype, my_tensor.type()
# (tensor(6., dtype=torch.float64), torch.float64, 'torch.DoubleTensor')
view()
. *My post explains view()
:
import torch
my_tensor1 = torch.tensor([0., 1., 2.]).view(size=(3, 1))
my_tensor2 = my_tensor.view(dtype=torch.bool)
my_tensor1, my_tensor2, my_tensor.dtype, my_tensor.type()
# (tensor([[0.],
# [1.],
# [2.]]),
# tensor([[False, False, False, False],
# [False, False, True, True],
# [False, False, False, True]]),
# torch.bool,
# 'torch.BoolTensor')