torch.Tensor¶
A torch.Tensor
is a multidimensional matrix containing elements of
a single data type.
Torch defines eight CPU tensor types and eight GPU tensor types:
Data type  dtype  CPU tensor  GPU tensor 

32bit floating point  torch.float32 or torch.float 
torch.FloatTensor 
torch.cuda.FloatTensor 
64bit floating point  torch.float64 or torch.double 
torch.DoubleTensor 
torch.cuda.DoubleTensor 
16bit floating point  torch.float16 or torch.half 
torch.HalfTensor 
torch.cuda.HalfTensor 
8bit integer (unsigned)  torch.uint8 
torch.ByteTensor 
torch.cuda.ByteTensor 
8bit integer (signed)  torch.int8 
torch.CharTensor 
torch.cuda.CharTensor 
16bit integer (signed)  torch.int16 or torch.short 
torch.ShortTensor 
torch.cuda.ShortTensor 
32bit integer (signed)  torch.int32 or torch.int 
torch.IntTensor 
torch.cuda.IntTensor 
64bit integer (signed)  torch.int64 or torch.long 
torch.LongTensor 
torch.cuda.LongTensor 
torch.Tensor
is an alias for the default tensor type (torch.FloatTensor
).
A tensor can be constructed from a Python list
or sequence using the
torch.tensor()
constructor:
>>> torch.tensor([[1., 1.], [1., 1.]])
tensor([[ 1.0000, 1.0000],
[ 1.0000, 1.0000]])
>>> torch.tensor(np.array([[1, 2, 3], [4, 5, 6]]))
tensor([[ 1, 2, 3],
[ 4, 5, 6]])
Warning
torch.tensor()
always copies data
. If you have a Tensor
data
and just want to change its requires_grad
flag, use
requires_grad_()
or
detach()
to avoid a copy.
If you have a numpy array and want to avoid a copy, use
torch.as_tensor()
.
A tensor of specific data type can be constructed by passing a
torch.dtype
and/or a torch.device
to a
constructor or tensor creation op:
>>> torch.zeros([2, 4], dtype=torch.int32)
tensor([[ 0, 0, 0, 0],
[ 0, 0, 0, 0]], dtype=torch.int32)
>>> cuda0 = torch.device('cuda:0')
>>> torch.ones([2, 4], dtype=torch.float64, device=cuda0)
tensor([[ 1.0000, 1.0000, 1.0000, 1.0000],
[ 1.0000, 1.0000, 1.0000, 1.0000]], dtype=torch.float64, device='cuda:0')
The contents of a tensor can be accessed and modified using Python’s indexing and slicing notation:
>>> x = torch.tensor([[1, 2, 3], [4, 5, 6]])
>>> print(x[1][2])
tensor(6)
>>> x[0][1] = 8
>>> print(x)
tensor([[ 1, 8, 3],
[ 4, 5, 6]])
Use torch.Tensor.item()
to get a Python number from a tensor containing a
single value:
>>> x = torch.tensor([[1]])
>>> x
tensor([[ 1]])
>>> x.item()
1
>>> x = torch.tensor(2.5)
>>> x
tensor(2.5000)
>>> x.item()
2.5
A tensor can be created with requires_grad=True
so that
torch.autograd
records operations on them for automatic differentiation.
>>> x = torch.tensor([[1., 1.], [1., 1.]], requires_grad=True)
>>> out = x.pow(2).sum()
>>> out.backward()
>>> x.grad
tensor([[ 2.0000, 2.0000],
[ 2.0000, 2.0000]])
Each tensor has an associated torch.Storage
, which holds its data.
The tensor class provides multidimensional, strided
view of a storage and defines numeric operations on it.
Note
For more information on the torch.dtype
, torch.device
, and
torch.layout
attributes of a torch.Tensor
, see
Tensor Attributes.
Note
Methods which mutate a tensor are marked with an underscore suffix.
For example, torch.FloatTensor.abs_()
computes the absolute value
inplace and returns the modified tensor, while torch.FloatTensor.abs()
computes the result in a new tensor.
Note
To change an existing tensor’s torch.device
and/or torch.dtype
, consider using
to()
method on the tensor.

class
torch.
Tensor
¶ There are a few main ways to create a tensor, depending on your use case.
 To create a tensor with preexisting data, use
torch.tensor()
.  To create a tensor with specific size, use
torch.*
tensor creation ops (see Creation Ops).  To create a tensor with the same size (and similar types) as another tensor,
use
torch.*_like
tensor creation ops (see Creation Ops).  To create a tensor with similar type but different size as another tensor,
use
tensor.new_*
creation ops.

new_tensor
(data, dtype=None, device=None, requires_grad=False) → Tensor¶ Returns a new Tensor with
data
as the tensor data. By default, the returned Tensor has the sametorch.dtype
andtorch.device
as this tensor.Warning
new_tensor()
always copiesdata
. If you have a Tensordata
and want to avoid a copy, usetorch.Tensor.requires_grad_()
ortorch.Tensor.detach()
. If you have a numpy array and want to avoid a copy, usetorch.from_numpy()
.Warning
When data is a tensor x,
new_tensor()
reads out ‘the data’ from whatever it is passed, and constructs a leaf variable. Thereforetensor.new_tensor(x)
is equivalent tox.clone().detach()
andtensor.new_tensor(x, requires_grad=True)
is equivalent tox.clone().detach().requires_grad_(True)
. The equivalents usingclone()
anddetach()
are recommended.Parameters:  data (array_like) – The returned Tensor copies
data
.  dtype (
torch.dtype
, optional) – the desired type of returned tensor. Default: if None, sametorch.dtype
as this tensor.  device (
torch.device
, optional) – the desired device of returned tensor. Default: if None, sametorch.device
as this tensor.  requires_grad (bool, optional) – If autograd should record operations on the
returned tensor. Default:
False
.
Example:
>>> tensor = torch.ones((2,), dtype=torch.int8) >>> data = [[0, 1], [2, 3]] >>> tensor.new_tensor(data) tensor([[ 0, 1], [ 2, 3]], dtype=torch.int8)
 data (array_like) – The returned Tensor copies

new_full
(size, fill_value, dtype=None, device=None, requires_grad=False) → Tensor¶ Returns a Tensor of size
size
filled withfill_value
. By default, the returned Tensor has the sametorch.dtype
andtorch.device
as this tensor.Parameters:  fill_value (scalar) – the number to fill the output tensor with.
 dtype (
torch.dtype
, optional) – the desired type of returned tensor. Default: if None, sametorch.dtype
as this tensor.  device (
torch.device
, optional) – the desired device of returned tensor. Default: if None, sametorch.device
as this tensor.  requires_grad (bool, optional) – If autograd should record operations on the
returned tensor. Default:
False
.
Example:
>>> tensor = torch.ones((2,), dtype=torch.float64) >>> tensor.new_full((3, 4), 3.141592) tensor([[ 3.1416, 3.1416, 3.1416, 3.1416], [ 3.1416, 3.1416, 3.1416, 3.1416], [ 3.1416, 3.1416, 3.1416, 3.1416]], dtype=torch.float64)

new_empty
(size, dtype=None, device=None, requires_grad=False) → Tensor¶ Returns a Tensor of size
size
filled with uninitialized data. By default, the returned Tensor has the sametorch.dtype
andtorch.device
as this tensor.Parameters:  dtype (
torch.dtype
, optional) – the desired type of returned tensor. Default: if None, sametorch.dtype
as this tensor.  device (
torch.device
, optional) – the desired device of returned tensor. Default: if None, sametorch.device
as this tensor.  requires_grad (bool, optional) – If autograd should record operations on the
returned tensor. Default:
False
.
Example:
>>> tensor = torch.ones(()) >>> tensor.new_empty((2, 3)) tensor([[ 5.8182e18, 4.5765e41, 1.0545e+30], [ 3.0949e41, 4.4842e44, 0.0000e+00]])
 dtype (

new_ones
(size, dtype=None, device=None, requires_grad=False) → Tensor¶ Returns a Tensor of size
size
filled with1
. By default, the returned Tensor has the sametorch.dtype
andtorch.device
as this tensor.Parameters:  size (int...) – a list, tuple, or
torch.Size
of integers defining the shape of the output tensor.  dtype (
torch.dtype
, optional) – the desired type of returned tensor. Default: if None, sametorch.dtype
as this tensor.  device (
torch.device
, optional) – the desired device of returned tensor. Default: if None, sametorch.device
as this tensor.  requires_grad (bool, optional) – If autograd should record operations on the
returned tensor. Default:
False
.
Example:
>>> tensor = torch.tensor((), dtype=torch.int32) >>> tensor.new_ones((2, 3)) tensor([[ 1, 1, 1], [ 1, 1, 1]], dtype=torch.int32)
 size (int...) – a list, tuple, or

new_zeros
(size, dtype=None, device=None, requires_grad=False) → Tensor¶ Returns a Tensor of size
size
filled with0
. By default, the returned Tensor has the sametorch.dtype
andtorch.device
as this tensor.Parameters:  size (int...) – a list, tuple, or
torch.Size
of integers defining the shape of the output tensor.  dtype (
torch.dtype
, optional) – the desired type of returned tensor. Default: if None, sametorch.dtype
as this tensor.  device (
torch.device
, optional) – the desired device of returned tensor. Default: if None, sametorch.device
as this tensor.  requires_grad (bool, optional) – If autograd should record operations on the
returned tensor. Default:
False
.
Example:
>>> tensor = torch.tensor((), dtype=torch.float64) >>> tensor.new_zeros((2, 3)) tensor([[ 0., 0., 0.], [ 0., 0., 0.]], dtype=torch.float64)
 size (int...) – a list, tuple, or

is_cuda
¶ Is
True
if the Tensor is stored on the GPU,False
otherwise.

device
¶ Is the
torch.device
where this Tensor is.

abs
() → Tensor¶ See
torch.abs()

acos
() → Tensor¶ See
torch.acos()

add
(value) → Tensor¶ add(value=1, other) > Tensor
See
torch.add()

addbmm
(beta=1, alpha=1, batch1, batch2) → Tensor¶ See
torch.addbmm()

addcdiv
(value=1, tensor1, tensor2) → Tensor¶ See
torch.addcdiv()

addcmul
(value=1, tensor1, tensor2) → Tensor¶ See
torch.addcmul()

addmm
(beta=1, alpha=1, mat1, mat2) → Tensor¶ See
torch.addmm()

addmv
(beta=1, alpha=1, mat, vec) → Tensor¶ See
torch.addmv()

addr
(beta=1, alpha=1, vec1, vec2) → Tensor¶ See
torch.addr()

allclose
(other, rtol=1e05, atol=1e08, equal_nan=False) → Tensor¶ See
torch.allclose()

apply_
(callable) → Tensor¶ Applies the function
callable
to each element in the tensor, replacing each element with the value returned bycallable
.Note
This function only works with CPU tensors and should not be used in code sections that require high performance.

argmax
(dim=None, keepdim=False) → LongTensor¶ See
torch.argmax()

argmin
(dim=None, keepdim=False) → LongTensor¶ See
torch.argmin()

asin
() → Tensor¶ See
torch.asin()

atan
() → Tensor¶ See
torch.atan()

atan2
(other) → Tensor¶ See
torch.atan2()

baddbmm
(beta=1, alpha=1, batch1, batch2) → Tensor¶ See
torch.baddbmm()

bernoulli
(*, generator=None) → Tensor¶ Returns a result tensor where each \(\texttt{result[i]}\) is independently sampled from \(\text{Bernoulli}(\texttt{self[i]})\).
self
must have floating pointdtype
, and the result will have the samedtype
.

bernoulli_
()¶ 
bernoulli_
(p=0.5, *, generator=None) → Tensor Fills each location of
self
with an independent sample from \(\text{Bernoulli}(\texttt{p})\).self
can have integraldtype
.

bernoulli_
(p_tensor, *, generator=None) → Tensor p_tensor
should be a tensor containing probabilities to be used for drawing the binary random number.The \(\text{i}^{th}\) element of
self
tensor will be set to a value sampled from \(\text{Bernoulli}(\texttt{p\_tensor[i]})\).self
can have integraldtype
, butp_tensor
must have floating pointdtype
.
See also
bernoulli()
andtorch.bernoulli()


bmm
(batch2) → Tensor¶ See
torch.bmm()

btrifact
(pivot=True) > (Tensor, Tensor)¶ See
torch.btrifact()

btrifact_with_info
(pivot=True) > (Tensor, Tensor, Tensor)¶

btrisolve
(LU_data, LU_pivots) → Tensor¶

cauchy_
(median=0, sigma=1, *, generator=None) → Tensor¶ Fills the tensor with numbers drawn from the Cauchy distribution:
\[f(x) = \dfrac{1}{\pi} \dfrac{\sigma}{(x  \text{median})^2 + \sigma^2}\]

ceil
() → Tensor¶ See
torch.ceil()

cholesky
(upper=False) → Tensor¶ See
torch.cholesky()

cholesky_solve
(input2, upper=False) → Tensor¶

chunk
(chunks, dim=0) → List of Tensors¶ See
torch.chunk()

clamp
(min, max) → Tensor¶ See
torch.clamp()

clone
() → Tensor¶ Returns a copy of the
self
tensor. The copy has the same size and data type asself
.Note
Unlike copy_(), this function is recorded in the computation graph. Gradients propagating to the cloned tensor will propagate to the original tensor.

contiguous
() → Tensor¶ Returns a contiguous tensor containing the same data as
self
tensor. Ifself
tensor is contiguous, this function returns theself
tensor.

copy_
(src, non_blocking=False) → Tensor¶ Copies the elements from
src
intoself
tensor and returnsself
.The
src
tensor must be broadcastable with theself
tensor. It may be of a different data type or reside on a different device.Parameters:

cos
() → Tensor¶ See
torch.cos()

cosh
() → Tensor¶ See
torch.cosh()

cpu
() → Tensor¶ Returns a copy of this object in CPU memory.
If this object is already in CPU memory and on the correct device, then no copy is performed and the original object is returned.

cross
(other, dim=1) → Tensor¶ See
torch.cross()

cuda
(device=None, non_blocking=False) → Tensor¶ Returns a copy of this object in CUDA memory.
If this object is already in CUDA memory and on the correct device, then no copy is performed and the original object is returned.
Parameters:  device (
torch.device
) – The destination GPU device. Defaults to the current CUDA device.  non_blocking (bool) – If
True
and the source is in pinned memory, the copy will be asynchronous with respect to the host. Otherwise, the argument has no effect. Default:False
.
 device (

cumprod
(dim, dtype=None) → Tensor¶ See
torch.cumprod()

cumsum
(dim, dtype=None) → Tensor¶ See
torch.cumsum()

data_ptr
() → int¶ Returns the address of the first element of
self
tensor.

det
() → Tensor¶ See
torch.det()

diag
(diagonal=0) → Tensor¶ See
torch.diag()

diag_embed
(offset=0, dim1=2, dim2=1) → Tensor¶

dim
() → int¶ Returns the number of dimensions of
self
tensor.

dist
(other, p=2) → Tensor¶ See
torch.dist()

div
(value) → Tensor¶ See
torch.div()

dot
(tensor2) → Tensor¶ See
torch.dot()

eig
(eigenvectors=False) > (Tensor, Tensor)¶ See
torch.eig()

element_size
() → int¶ Returns the size in bytes of an individual element.
Example:
>>> torch.tensor([]).element_size() 4 >>> torch.tensor([], dtype=torch.uint8).element_size() 1

eq
(other) → Tensor¶ See
torch.eq()

equal
(other) → bool¶ See
torch.equal()

erf
() → Tensor¶ See
torch.erf()

erfc
() → Tensor¶ See
torch.erfc()

erfinv
() → Tensor¶ See
torch.erfinv()

exp
() → Tensor¶ See
torch.exp()

expm1
() → Tensor¶ See
torch.expm1()

expand
(*sizes) → Tensor¶ Returns a new view of the
self
tensor with singleton dimensions expanded to a larger size.Passing 1 as the size for a dimension means not changing the size of that dimension.
Tensor can be also expanded to a larger number of dimensions, and the new ones will be appended at the front. For the new dimensions, the size cannot be set to 1.
Expanding a tensor does not allocate new memory, but only creates a new view on the existing tensor where a dimension of size one is expanded to a larger size by setting the
stride
to 0. Any dimension of size 1 can be expanded to an arbitrary value without allocating new memory.Parameters: *sizes (torch.Size or int...) – the desired expanded size Warning
More than one element of an expanded tensor may refer to a single memory location. As a result, inplace operations (especially ones that are vectorized) may result in incorrect behavior. If you need to write to the tensors, please clone them first.
Example:
>>> x = torch.tensor([[1], [2], [3]]) >>> x.size() torch.Size([3, 1]) >>> x.expand(3, 4) tensor([[ 1, 1, 1, 1], [ 2, 2, 2, 2], [ 3, 3, 3, 3]]) >>> x.expand(1, 4) # 1 means not changing the size of that dimension tensor([[ 1, 1, 1, 1], [ 2, 2, 2, 2], [ 3, 3, 3, 3]])

expand_as
(other) → Tensor¶ Expand this tensor to the same size as
other
.self.expand_as(other)
is equivalent toself.expand(other.size())
.Please see
expand()
for more information aboutexpand
.Parameters: other ( torch.Tensor
) – The result tensor has the same size asother
.

exponential_
(lambd=1, *, generator=None) → Tensor¶ Fills
self
tensor with elements drawn from the exponential distribution:\[f(x) = \lambda e^{\lambda x}\]

fill_
(value) → Tensor¶ Fills
self
tensor with the specified value.

flatten
(input, start_dim=0, end_dim=1) → Tensor¶ see
torch.flatten()

flip
(dims) → Tensor¶ See
torch.flip()

floor
() → Tensor¶ See
torch.floor()

fmod
(divisor) → Tensor¶ See
torch.fmod()

frac
() → Tensor¶ See
torch.frac()

gather
(dim, index) → Tensor¶ See
torch.gather()

ge
(other) → Tensor¶ See
torch.ge()

gels
(A) → Tensor¶ See
torch.gels()

geometric_
(p, *, generator=None) → Tensor¶ Fills
self
tensor with elements drawn from the geometric distribution:\[f(X=k) = (1  p)^{k  1} p\]

geqrf
() > (Tensor, Tensor)¶ See
torch.geqrf()

ger
(vec2) → Tensor¶ See
torch.ger()

gesv
(A)[source]¶ See
torch.solve()

get_device
() > Device ordinal (Integer)¶ For CUDA tensors, this function returns the device ordinal of the GPU on which the tensor resides. For CPU tensors, an error is thrown.
Example:
>>> x = torch.randn(3, 4, 5, device='cuda:0') >>> x.get_device() 0 >>> x.cpu().get_device() # RuntimeError: get_device is not implemented for type torch.FloatTensor

gt
(other) → Tensor¶ See
torch.gt()

histc
(bins=100, min=0, max=0) → Tensor¶ See
torch.histc()

index_add_
(dim, index, tensor) → Tensor¶ Accumulate the elements of
tensor
into theself
tensor by adding to the indices in the order given inindex
. For example, ifdim == 0
andindex[i] == j
, then thei
th row oftensor
is added to thej
th row ofself
.The
dim
th dimension oftensor
must have the same size as the length ofindex
(which must be a vector), and all other dimensions must matchself
, or an error will be raised.Note
When using the CUDA backend, this operation may induce nondeterministic behaviour that is not easily switched off. Please see the notes on Reproducibility for background.
Parameters: Example:
>>> x = torch.ones(5, 3) >>> t = torch.tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=torch.float) >>> index = torch.tensor([0, 4, 2]) >>> x.index_add_(0, index, t) tensor([[ 2., 3., 4.], [ 1., 1., 1.], [ 8., 9., 10.], [ 1., 1., 1.], [ 5., 6., 7.]])

index_add
(dim, index, tensor) → Tensor¶ Outofplace version of
torch.Tensor.index_add_()

index_copy_
(dim, index, tensor) → Tensor¶ Copies the elements of
tensor
into theself
tensor by selecting the indices in the order given inindex
. For example, ifdim == 0
andindex[i] == j
, then thei
th row oftensor
is copied to thej
th row ofself
.The
dim
th dimension oftensor
must have the same size as the length ofindex
(which must be a vector), and all other dimensions must matchself
, or an error will be raised.Parameters: Example:
>>> x = torch.zeros(5, 3) >>> t = torch.tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=torch.float) >>> index = torch.tensor([0, 4, 2]) >>> x.index_copy_(0, index, t) tensor([[ 1., 2., 3.], [ 0., 0., 0.], [ 7., 8., 9.], [ 0., 0., 0.], [ 4., 5., 6.]])

index_copy
(dim, index, tensor) → Tensor¶ Outofplace version of
torch.Tensor.index_copy_()

index_fill_
(dim, index, val) → Tensor¶ Fills the elements of the
self
tensor with valueval
by selecting the indices in the order given inindex
.Parameters:  Example::
>>> x = torch.tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=torch.float) >>> index = torch.tensor([0, 2]) >>> x.index_fill_(1, index, 1) tensor([[1., 2., 1.], [1., 5., 1.], [1., 8., 1.]])

index_fill
(dim, index, value) → Tensor¶ Outofplace version of
torch.Tensor.index_fill_()

index_put_
(indices, value, accumulate=False) → Tensor¶ Puts values from the tensor
value
into the tensorself
using the indices specified inindices
(which is a tuple of Tensors). The expressiontensor.index_put_(indices, value)
is equivalent totensor[indices] = value
. Returnsself
.If
accumulate
isTrue
, the elements intensor
are added toself
. If accumulate isFalse
, the behavior is undefined if indices contain duplicate elements.Parameters:

index_put
()¶

index_select
(dim, index) → Tensor¶

inverse
() → Tensor¶ See
torch.inverse()

is_contiguous
() → bool¶ Returns True if
self
tensor is contiguous in memory in C order.

is_floating_point
() → bool¶ Returns True if the data type of
self
is a floating point data type.

is_set_to
(tensor) → bool¶ Returns True if this object refers to the same
THTensor
object from the Torch C API as the given tensor.

is_signed
()¶

item
() → number¶ Returns the value of this tensor as a standard Python number. This only works for tensors with one element. For other cases, see
tolist()
.This operation is not differentiable.
Example:
>>> x = torch.tensor([1.0]) >>> x.item() 1.0

kthvalue
(k, dim=None, keepdim=False) > (Tensor, LongTensor)¶ See
torch.kthvalue()

le
(other) → Tensor¶ See
torch.le()

lerp
(end, weight) → Tensor¶ See
torch.lerp()

log
() → Tensor¶ See
torch.log()

logdet
() → Tensor¶ See
torch.logdet()

log10
() → Tensor¶ See
torch.log10()

log1p
() → Tensor¶ See
torch.log1p()

log2
() → Tensor¶ See
torch.log2()

log_normal_
(mean=1, std=2, *, generator=None)¶ Fills
self
tensor with numbers samples from the lognormal distribution parameterized by the given mean \(\mu\) and standard deviation \(\sigma\). Note thatmean
andstd
are the mean and standard deviation of the underlying normal distribution, and not of the returned distribution:\[f(x) = \dfrac{1}{x \sigma \sqrt{2\pi}}\ e^{\frac{(\ln x  \mu)^2}{2\sigma^2}}\]

logsumexp
(dim, keepdim=False) → Tensor¶

lt
(other) → Tensor¶ See
torch.lt()

map_
(tensor, callable)¶ Applies
callable
for each element inself
tensor and the giventensor
and stores the results inself
tensor.self
tensor and the giventensor
must be broadcastable.The
callable
should have the signature:def callable(a, b) > number

masked_scatter_
(mask, source)¶ Copies elements from
source
intoself
tensor at positions where themask
is one. The shape ofmask
must be broadcastable with the shape of the underlying tensor. Thesource
should have at least as many elements as the number of ones inmask
Parameters:  mask (ByteTensor) – the binary mask
 source (Tensor) – the tensor to copy from
Note
The
mask
operates on theself
tensor, not on the givensource
tensor.

masked_scatter
(mask, tensor) → Tensor¶ Outofplace version of
torch.Tensor.masked_scatter_()

masked_fill_
(mask, value)¶ Fills elements of
self
tensor withvalue
wheremask
is one. The shape ofmask
must be broadcastable with the shape of the underlying tensor.Parameters:  mask (ByteTensor) – the binary mask
 value (float) – the value to fill in with

masked_fill
(mask, value) → Tensor¶ Outofplace version of
torch.Tensor.masked_fill_()

masked_select
(mask) → Tensor¶

matmul
(tensor2) → Tensor¶ See
torch.matmul()

matrix_power
(n) → Tensor¶

max
(dim=None, keepdim=False) > Tensor or (Tensor, Tensor)¶ See
torch.max()

mean
(dim=None, keepdim=False) > Tensor or (Tensor, Tensor)¶ See
torch.mean()

median
(dim=None, keepdim=False) > (Tensor, LongTensor)¶ See
torch.median()

min
(dim=None, keepdim=False) > Tensor or (Tensor, Tensor)¶ See
torch.min()

mm
(mat2) → Tensor¶ See
torch.mm()

mode
(dim=None, keepdim=False) > (Tensor, LongTensor)¶ See
torch.mode()

mul
(value) → Tensor¶ See
torch.mul()

multinomial
(num_samples, replacement=False, *, generator=None) → Tensor¶

mv
(vec) → Tensor¶ See
torch.mv()

mvlgamma
(p) → Tensor¶ See
torch.mvlgamma()

mvlgamma_
(p) → Tensor¶ Inplace version of
mvlgamma()

narrow
(dimension, start, length) → Tensor¶ See
torch.narrow()
Example:
>>> x = torch.tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) >>> x.narrow(0, 0, 2) tensor([[ 1, 2, 3], [ 4, 5, 6]]) >>> x.narrow(1, 1, 2) tensor([[ 2, 3], [ 5, 6], [ 8, 9]])

ne
(other) → Tensor¶ See
torch.ne()

neg
() → Tensor¶ See
torch.neg()

nonzero
() → LongTensor¶ See
torch.nonzero()

norm
(p='fro', dim=None, keepdim=False, dtype=None)[source]¶ See
torch.norm()

normal_
(mean=0, std=1, *, generator=None) → Tensor¶ Fills
self
tensor with elements samples from the normal distribution parameterized bymean
andstd
.

numel
() → int¶ See
torch.numel()

numpy
() → numpy.ndarray¶ Returns
self
tensor as a NumPyndarray
. This tensor and the returnedndarray
share the same underlying storage. Changes toself
tensor will be reflected in thendarray
and vice versa.

orgqr
(input2) → Tensor¶ See
torch.orgqr()

ormqr
(input2, input3, left=True, transpose=False) → Tensor¶ See
torch.ormqr()

permute
(*dims) → Tensor¶ Permute the dimensions of this tensor.
Parameters: *dims (int...) – The desired ordering of dimensions Example
>>> x = torch.randn(2, 3, 5) >>> x.size() torch.Size([2, 3, 5]) >>> x.permute(2, 0, 1).size() torch.Size([5, 2, 3])

pin_memory
()¶

pinverse
() → Tensor¶ See
torch.pinverse()

potrf
(upper=True)[source]¶ See
torch.cholesky()

potri
(upper=True) → Tensor¶ See
torch.potri()

pow
(exponent) → Tensor¶ See
torch.pow()

prod
(dim=None, keepdim=False, dtype=None) → Tensor¶ See
torch.prod()

pstrf
(upper=True)[source]¶ See
torch.pstrf()

put_
(indices, tensor, accumulate=False) → Tensor¶ Copies the elements from
tensor
into the positions specified by indices. For the purpose of indexing, theself
tensor is treated as if it were a 1D tensor.If
accumulate
isTrue
, the elements intensor
are added toself
. If accumulate isFalse
, the behavior is undefined if indices contain duplicate elements.Parameters: Example:
>>> src = torch.tensor([[4, 3, 5], [6, 7, 8]]) >>> src.put_(torch.tensor([1, 3]), torch.tensor([9, 10])) tensor([[ 4, 9, 5], [ 10, 7, 8]])

qr
() > (Tensor, Tensor)¶ See
torch.qr()

random_
(from=0, to=None, *, generator=None) → Tensor¶ Fills
self
tensor with numbers sampled from the discrete uniform distribution over[from, to  1]
. If not specified, the values are usually only bounded byself
tensor’s data type. However, for floating point types, if unspecified, range will be[0, 2^mantissa]
to ensure that every value is representable. For example, torch.tensor(1, dtype=torch.double).random_() will be uniform in[0, 2^53]
.

reciprocal
() → Tensor¶

reciprocal_
() → Tensor¶ Inplace version of
reciprocal()

remainder
(divisor) → Tensor¶

remainder_
(divisor) → Tensor¶ Inplace version of
remainder()

renorm
(p, dim, maxnorm) → Tensor¶ See
torch.renorm()

repeat
(*sizes) → Tensor¶ Repeats this tensor along the specified dimensions.
Unlike
expand()
, this function copies the tensor’s data.Warning
torch.repeat()
behaves differently from numpy.repeat, but is more similar to numpy.tile.Parameters: sizes (torch.Size or int...) – The number of times to repeat this tensor along each dimension Example:
>>> x = torch.tensor([1, 2, 3]) >>> x.repeat(4, 2) tensor([[ 1, 2, 3, 1, 2, 3], [ 1, 2, 3, 1, 2, 3], [ 1, 2, 3, 1, 2, 3], [ 1, 2, 3, 1, 2, 3]]) >>> x.repeat(4, 2, 1).size() torch.Size([4, 2, 3])

requires_grad_
(requires_grad=True) → Tensor¶ Change if autograd should record operations on this tensor: sets this tensor’s
requires_grad
attribute inplace. Returns this tensor.require_grad_()
’s main use case is to tell autograd to begin recording operations on a Tensortensor
. Iftensor
hasrequires_grad=False
(because it was obtained through a DataLoader, or required preprocessing or initialization),tensor.requires_grad_()
makes it so that autograd will begin to record operations ontensor
.Parameters: requires_grad (bool) – If autograd should record operations on this tensor. Default: True
.Example:
>>> # Let's say we want to preprocess some saved weights and use >>> # the result as new weights. >>> saved_weights = [0.1, 0.2, 0.3, 0.25] >>> loaded_weights = torch.tensor(saved_weights) >>> weights = preprocess(loaded_weights) # some function >>> weights tensor([0.5503, 0.4926, 2.1158, 0.8303]) >>> # Now, start to record operations done to weights >>> weights.requires_grad_() >>> out = weights.pow(2).sum() >>> out.backward() >>> weights.grad tensor([1.1007, 0.9853, 4.2316, 1.6606])

reshape
(*shape) → Tensor¶ Returns a tensor with the same data and number of elements as
self
but with the specified shape. This method returns a view ifshape
is compatible with the current shape. Seetorch.Tensor.view()
on when it is possible to return a view.See
torch.reshape()
Parameters: shape (tuple of python:ints or int...) – the desired shape

reshape_as
(other) → Tensor¶ Returns this tensor as the same shape as
other
.self.reshape_as(other)
is equivalent toself.reshape(other.sizes())
. This method returns a view ifother.sizes()
is compatible with the current shape. Seetorch.Tensor.view()
on when it is possible to return a view.Please see
reshape()
for more information aboutreshape
.Parameters: other ( torch.Tensor
) – The result tensor has the same shape asother
.

resize_
(*sizes) → Tensor¶ Resizes
self
tensor to the specified size. If the number of elements is larger than the current storage size, then the underlying storage is resized to fit the new number of elements. If the number of elements is smaller, the underlying storage is not changed. Existing elements are preserved but any new memory is uninitialized.Warning
This is a lowlevel method. The storage is reinterpreted as Ccontiguous, ignoring the current strides (unless the target size equals the current size, in which case the tensor is left unchanged). For most purposes, you will instead want to use
view()
, which checks for contiguity, orreshape()
, which copies data if needed. To change the size inplace with custom strides, seeset_()
.Parameters: sizes (torch.Size or int...) – the desired size Example:
>>> x = torch.tensor([[1, 2], [3, 4], [5, 6]]) >>> x.resize_(2, 2) tensor([[ 1, 2], [ 3, 4]])

resize_as_
(tensor) → Tensor¶ Resizes the
self
tensor to be the same size as the specifiedtensor
. This is equivalent toself.resize_(tensor.size())
.

roll
(shifts, dims) → Tensor¶ See
torch.roll()

round
() → Tensor¶ See
torch.round()

rsqrt
() → Tensor¶ See
torch.rsqrt()

scatter_
(dim, index, src) → Tensor¶ Writes all values from the tensor
src
intoself
at the indices specified in theindex
tensor. For each value insrc
, its output index is specified by its index insrc
fordimension != dim
and by the corresponding value inindex
fordimension = dim
.For a 3D tensor,
self
is updated as:self[index[i][j][k]][j][k] = src[i][j][k] # if dim == 0 self[i][index[i][j][k]][k] = src[i][j][k] # if dim == 1 self[i][j][index[i][j][k]] = src[i][j][k] # if dim == 2
This is the reverse operation of the manner described in
gather()
.self
,index
andsrc
(if it is a Tensor) should have same number of dimensions. It is also required thatindex.size(d) <= src.size(d)
for all dimensionsd
, and thatindex.size(d) <= self.size(d)
for all dimensionsd != dim
.Moreover, as for
gather()
, the values ofindex
must be between0
andself.size(dim)  1
inclusive, and all values in a row along the specified dimensiondim
must be unique.Parameters:  dim (int) – the axis along which to index
 index (LongTensor) – the indices of elements to scatter, can be either empty or the same size of src. When empty, the operation returns identity
 src (Tensor) – the source element(s) to scatter, incase value is not specified
 value (float) – the source element(s) to scatter, incase src is not specified
Example:
>>> x = torch.rand(2, 5) >>> x tensor([[ 0.3992, 0.2908, 0.9044, 0.4850, 0.6004], [ 0.5735, 0.9006, 0.6797, 0.4152, 0.1732]]) >>> torch.zeros(3, 5).scatter_(0, torch.tensor([[0, 1, 2, 0, 0], [2, 0, 0, 1, 2]]), x) tensor([[ 0.3992, 0.9006, 0.6797, 0.4850, 0.6004], [ 0.0000, 0.2908, 0.0000, 0.4152, 0.0000], [ 0.5735, 0.0000, 0.9044, 0.0000, 0.1732]]) >>> z = torch.zeros(2, 4).scatter_(1, torch.tensor([[2], [3]]), 1.23) >>> z tensor([[ 0.0000, 0.0000, 1.2300, 0.0000], [ 0.0000, 0.0000, 0.0000, 1.2300]])

scatter
(dim, index, source) → Tensor¶ Outofplace version of
torch.Tensor.scatter_()

scatter_add_
(dim, index, other) → Tensor¶ Adds all values from the tensor
other
intoself
at the indices specified in theindex
tensor in a similar fashion asscatter_()
. For each value inother
, it is added to an index inself
which is specified by its index inother
fordimension != dim
and by the corresponding value inindex
fordimension = dim
.For a 3D tensor,
self
is updated as:self[index[i][j][k]][j][k] += other[i][j][k] # if dim == 0 self[i][index[i][j][k]][k] += other[i][j][k] # if dim == 1 self[i][j][index[i][j][k]] += other[i][j][k] # if dim == 2
self
,index
andother
should have same number of dimensions. It is also required thatindex.size(d) <= other.size(d)
for all dimensionsd
, and thatindex.size(d) <= self.size(d)
for all dimensionsd != dim
.Moreover, as for
gather()
, the values ofindex
must be between0
andself.size(dim)  1
inclusive, and all values in a row along the specified dimensiondim
must be unique.Note
When using the CUDA backend, this operation may induce nondeterministic behaviour that is not easily switched off. Please see the notes on Reproducibility for background.
Parameters: Example:
>>> x = torch.rand(2, 5) >>> x tensor([[0.7404, 0.0427, 0.6480, 0.3806, 0.8328], [0.7953, 0.2009, 0.9154, 0.6782, 0.9620]]) >>> torch.ones(3, 5).scatter_add_(0, torch.tensor([[0, 1, 2, 0, 0], [2, 0, 0, 1, 2]]), x) tensor([[1.7404, 1.2009, 1.9154, 1.3806, 1.8328], [1.0000, 1.0427, 1.0000, 1.6782, 1.0000], [1.7953, 1.0000, 1.6480, 1.0000, 1.9620]])

scatter_add
(dim, index, source) → Tensor¶ Outofplace version of
torch.Tensor.scatter_add_()

select
(dim, index) → Tensor¶ Slices the
self
tensor along the selected dimension at the given index. This function returns a tensor with the given dimension removed.Parameters: Note
select()
is equivalent to slicing. For example,tensor.select(0, index)
is equivalent totensor[index]
andtensor.select(2, index)
is equivalent totensor[:,:,index]
.

set_
(source=None, storage_offset=0, size=None, stride=None) → Tensor¶ Sets the underlying storage, size, and strides. If
source
is a tensor,self
tensor will share the same storage and have the same size and strides assource
. Changes to elements in one tensor will be reflected in the other.If
source
is aStorage
, the method sets the underlying storage, offset, size, and stride.Parameters:
Moves the underlying storage to shared memory.
This is a noop if the underlying storage is already in shared memory and for CUDA tensors. Tensors in shared memory cannot be resized.

sigmoid
() → Tensor¶ See
torch.sigmoid()

sign
() → Tensor¶ See
torch.sign()

sin
() → Tensor¶ See
torch.sin()

sinh
() → Tensor¶ See
torch.sinh()

size
() → torch.Size¶ Returns the size of the
self
tensor. The returned value is a subclass oftuple
.Example:
>>> torch.empty(3, 4, 5).size() torch.Size([3, 4, 5])

slogdet
() > (Tensor, Tensor)¶ See
torch.slogdet()

solve
(A) → Tensor, Tensor¶ See
torch.solve()

sort
(dim=1, descending=False) > (Tensor, LongTensor)¶ See
torch.sort()

split
(split_size, dim=0)[source]¶ See
torch.split()

sparse_mask
(input, mask) → Tensor¶ Returns a new SparseTensor with values from Tensor
input
filtered by indices ofmask
and values are ignored.input
andmask
must have the same shape.Parameters:  input (Tensor) – an input Tensor
 mask (SparseTensor) – a SparseTensor which we filter
input
based on its indices
Example:
>>> nnz = 5 >>> dims = [5, 5, 2, 2] >>> I = torch.cat([torch.randint(0, dims[0], size=(nnz,)), torch.randint(0, dims[1], size=(nnz,))], 0).reshape(2, nnz) >>> V = torch.randn(nnz, dims[2], dims[3]) >>> size = torch.Size(dims) >>> S = torch.sparse_coo_tensor(I, V, size).coalesce() >>> D = torch.randn(dims) >>> D.sparse_mask(S) tensor(indices=tensor([[0, 0, 0, 2], [0, 1, 4, 3]]), values=tensor([[[ 1.6550, 0.2397], [0.1611, 0.0779]], [[ 0.2326, 1.0558], [ 1.4711, 1.9678]], [[0.5138, 0.0411], [ 1.9417, 0.5158]], [[ 0.0793, 0.0036], [0.2569, 0.1055]]]), size=(5, 5, 2, 2), nnz=4, layout=torch.sparse_coo)

sqrt
() → Tensor¶ See
torch.sqrt()

squeeze
(dim=None) → Tensor¶ See
torch.squeeze()

std
(dim=None, unbiased=True, keepdim=False) → Tensor¶ See
torch.std()

storage
() → torch.Storage¶ Returns the underlying storage

storage_offset
() → int¶ Returns
self
tensor’s offset in the underlying storage in terms of number of storage elements (not bytes).Example:
>>> x = torch.tensor([1, 2, 3, 4, 5]) >>> x.storage_offset() 0 >>> x[3:].storage_offset() 3

storage_type
()¶

stride
(dim) → tuple or int¶ Returns the stride of
self
tensor.Stride is the jump necessary to go from one element to the next one in the specified dimension
dim
. A tuple of all strides is returned when no argument is passed in. Otherwise, an integer value is returned as the stride in the particular dimensiondim
.Parameters: dim (int, optional) – the desired dimension in which stride is required Example:
>>> x = torch.tensor([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]]) >>> x.stride() (5, 1) >>>x.stride(0) 5 >>> x.stride(1) 1

sub
(value, other) → Tensor¶ Subtracts a scalar or tensor from
self
tensor. If bothvalue
andother
are specified, each element ofother
is scaled byvalue
before being used.When
other
is a tensor, the shape ofother
must be broadcastable with the shape of the underlying tensor.

sum
(dim=None, keepdim=False, dtype=None) → Tensor¶ See
torch.sum()

svd
(some=True, compute_uv=True) > (Tensor, Tensor, Tensor)¶ See
torch.svd()

symeig
(eigenvectors=False, upper=True) > (Tensor, Tensor)¶ See
torch.symeig()

to
(*args, **kwargs) → Tensor¶ Performs Tensor dtype and/or device conversion. A
torch.dtype
andtorch.device
are inferred from the arguments ofself.to(*args, **kwargs)
.Note
If the
self
Tensor already has the correcttorch.dtype
andtorch.device
, thenself
is returned. Otherwise, the returned tensor is a copy ofself
with the desiredtorch.dtype
andtorch.device
.Here are the ways to call
to
:
to
(dtype, non_blocking=False, copy=False) → Tensor Returns a Tensor with the specified
dtype

to
(device=None, dtype=None, non_blocking=False, copy=False) → Tensor Returns a Tensor with the specified
device
and (optional)dtype
. Ifdtype
isNone
it is inferred to beself.dtype
. Whennon_blocking
, tries to convert asynchronously with respect to the host if possible, e.g., converting a CPU Tensor with pinned memory to a CUDA Tensor. Whencopy
is set, a new Tensor is created even when the Tensor already matches the desired conversion.

to
(other, non_blocking=False, copy=False) → Tensor Returns a Tensor with same
torch.dtype
andtorch.device
as the Tensorother
. Whennon_blocking
, tries to convert asynchronously with respect to the host if possible, e.g., converting a CPU Tensor with pinned memory to a CUDA Tensor. Whencopy
is set, a new Tensor is created even when the Tensor already matches the desired conversion.
Example:
>>> tensor = torch.randn(2, 2) # Initially dtype=float32, device=cpu >>> tensor.to(torch.float64) tensor([[0.5044, 0.0005], [ 0.3310, 0.0584]], dtype=torch.float64) >>> cuda0 = torch.device('cuda:0') >>> tensor.to(cuda0) tensor([[0.5044, 0.0005], [ 0.3310, 0.0584]], device='cuda:0') >>> tensor.to(cuda0, dtype=torch.float64) tensor([[0.5044, 0.0005], [ 0.3310, 0.0584]], dtype=torch.float64, device='cuda:0') >>> other = torch.randn((), dtype=torch.float64, device=cuda0) >>> tensor.to(other, non_blocking=True) tensor([[0.5044, 0.0005], [ 0.3310, 0.0584]], dtype=torch.float64, device='cuda:0')


take
(indices) → Tensor¶ See
torch.take()

tan
()¶

tanh
() → Tensor¶ See
torch.tanh()

tolist
()¶ ” tolist() > list or number
Returns the tensor as a (nested) list. For scalars, a standard Python number is returned, just like with
item()
. Tensors are automatically moved to the CPU first if necessary.This operation is not differentiable.
Examples:
>>> a = torch.randn(2, 2) >>> a.tolist() [[0.012766935862600803, 0.5415473580360413], [0.08909505605697632, 0.7729271650314331]] >>> a[0,0].tolist() 0.012766935862600803

topk
(k, dim=None, largest=True, sorted=True) > (Tensor, LongTensor)¶ See
torch.topk()

to_sparse
(sparseDims) → Tensor¶ Returns a sparse copy of the tensor. PyTorch supports sparse tensors in coordinate format.
Parameters: sparseDims (int, optional) – the number of sparse dimensions to include in the new sparse tensor Example:
>>> d = torch.tensor([[0, 0, 0], [9, 0, 10], [0, 0, 0]]) >>> d tensor([[ 0, 0, 0], [ 9, 0, 10], [ 0, 0, 0]]) >>> d.to_sparse() tensor(indices=tensor([[1, 1], [0, 2]]), values=tensor([ 9, 10]), size=(3, 3), nnz=2, layout=torch.sparse_coo) >>> d.to_sparse(1) tensor(indices=tensor([[1]]), values=tensor([[ 9, 0, 10]]), size=(3, 3), nnz=1, layout=torch.sparse_coo)

trace
() → Tensor¶ See
torch.trace()

transpose
(dim0, dim1) → Tensor¶

transpose_
(dim0, dim1) → Tensor¶ Inplace version of
transpose()

triangular_solve
(A, upper=True, transpose=False, unitriangular=False) > (Tensor, Tensor)¶

tril
(k=0) → Tensor¶ See
torch.tril()

triu
(k=0) → Tensor¶ See
torch.triu()

trunc
() → Tensor¶ See
torch.trunc()

type
(dtype=None, non_blocking=False, **kwargs) → str or Tensor¶ Returns the type if dtype is not provided, else casts this object to the specified type.
If this is already of the correct type, no copy is performed and the original object is returned.
Parameters:  dtype (type or string) – The desired type
 non_blocking (bool) – If
True
, and the source is in pinned memory and destination is on the GPU or vice versa, the copy is performed asynchronously with respect to the host. Otherwise, the argument has no effect.  **kwargs – For compatibility, may contain the key
async
in place of thenon_blocking
argument. Theasync
arg is deprecated.

type_as
(tensor) → Tensor¶ Returns this tensor cast to the type of the given tensor.
This is a noop if the tensor is already of the correct type. This is equivalent to
self.type(tensor.type())
Parameters: tensor (Tensor) – the tensor which has the desired type

unfold
(dim, size, step) → Tensor¶ Returns a tensor which contains all slices of size
size
fromself
tensor in the dimensiondim
.Step between two slices is given by
step
.If sizedim is the size of dimension
dim
forself
, the size of dimensiondim
in the returned tensor will be (sizedim  size) / step + 1.An additional dimension of size
size
is appended in the returned tensor.Parameters: Example:
>>> x = torch.arange(1., 8) >>> x tensor([ 1., 2., 3., 4., 5., 6., 7.]) >>> x.unfold(0, 2, 1) tensor([[ 1., 2.], [ 2., 3.], [ 3., 4.], [ 4., 5.], [ 5., 6.], [ 6., 7.]]) >>> x.unfold(0, 2, 2) tensor([[ 1., 2.], [ 3., 4.], [ 5., 6.]])

uniform_
(from=0, to=1) → Tensor¶ Fills
self
tensor with numbers sampled from the continuous uniform distribution:\[P(x) = \dfrac{1}{\text{to}  \text{from}} \]

unique
(sorted=True, return_inverse=False, dim=None)[source]¶ Returns the unique scalar elements of the tensor as a 1D tensor.
See
torch.unique()

unsqueeze
(dim) → Tensor¶

unsqueeze_
(dim) → Tensor¶ Inplace version of
unsqueeze()

var
(dim=None, unbiased=True, keepdim=False) → Tensor¶ See
torch.var()

view
(*shape) → Tensor¶ Returns a new tensor with the same data as the
self
tensor but of a differentshape
.The returned tensor shares the same data and must have the same number of elements, but may have a different size. For a tensor to be viewed, the new view size must be compatible with its original size and stride, i.e., each new view dimension must either be a subspace of an original dimension, or only span across original dimensions \(d, d+1, \dots, d+k\) that satisfy the following contiguitylike condition that \(\forall i = 0, \dots, k1\),
\[\text{stride}[i] = \text{stride}[i+1] \times \text{size}[i+1]\]Otherwise,
contiguous()
needs to be called before the tensor can be viewed. See also:reshape()
, which returns a view if the shapes are compatible, and copies (equivalent to callingcontiguous()
) otherwise.Parameters: shape (torch.Size or int...) – the desired size Example:
>>> x = torch.randn(4, 4) >>> x.size() torch.Size([4, 4]) >>> y = x.view(16) >>> y.size() torch.Size([16]) >>> z = x.view(1, 8) # the size 1 is inferred from other dimensions >>> z.size() torch.Size([2, 8]) >>> a = torch.randn(1, 2, 3, 4) >>> a.size() torch.Size([1, 2, 3, 4]) >>> b = a.transpose(1, 2) # Swaps 2nd and 3rd dimension >>> b.size() torch.Size([1, 3, 2, 4]) >>> c = a.view(1, 3, 2, 4) # Does not change tensor layout in memory >>> c.size() torch.Size([1, 3, 2, 4]) >>> torch.equal(b, c) False

view_as
(other) → Tensor¶ View this tensor as the same size as
other
.self.view_as(other)
is equivalent toself.view(other.size())
.Please see
view()
for more information aboutview
.Parameters: other ( torch.Tensor
) – The result tensor has the same size asother
.

zero_
() → Tensor¶ Fills
self
tensor with zeros.
 To create a tensor with preexisting data, use

class
torch.
ByteTensor
¶ The following methods are unique to
torch.ByteTensor
.
all
()¶ 
all
() → bool
Returns True if all elements in the tensor are nonzero, False otherwise.
Example:
>>> a = torch.randn(1, 3).byte() % 2 >>> a tensor([[1, 0, 0]], dtype=torch.uint8) >>> a.all() tensor(0, dtype=torch.uint8)

all
(dim, keepdim=False, out=None) → Tensor
Returns True if all elements in each row of the tensor in the given dimension
dim
are nonzero, False otherwise.If
keepdim
isTrue
, the output tensor is of the same size asinput
except in the dimensiondim
where it is of size 1. Otherwise,dim
is squeezed (seetorch.squeeze()
), resulting in the output tensor having 1 fewer dimension thaninput
.Parameters: Example:
>>> a = torch.randn(4, 2).byte() % 2 >>> a tensor([[0, 0], [0, 0], [0, 1], [1, 1]], dtype=torch.uint8) >>> a.all(dim=1) tensor([0, 0, 0, 1], dtype=torch.uint8)


any
()¶ 
any
() → bool
Returns True if any elements in the tensor are nonzero, False otherwise.
Example:
>>> a = torch.randn(1, 3).byte() % 2 >>> a tensor([[0, 0, 1]], dtype=torch.uint8) >>> a.any() tensor(1, dtype=torch.uint8)

any
(dim, keepdim=False, out=None) → Tensor
Returns True if any elements in each row of the tensor in the given dimension
dim
are nonzero, False otherwise.If
keepdim
isTrue
, the output tensor is of the same size asinput
except in the dimensiondim
where it is of size 1. Otherwise,dim
is squeezed (seetorch.squeeze()
), resulting in the output tensor having 1 fewer dimension thaninput
.Parameters: Example:
>>> a = torch.randn(4, 2).byte() % 2 >>> a tensor([[1, 0], [0, 0], [0, 1], [0, 0]], dtype=torch.uint8) >>> a.any(dim=1) tensor([1, 0, 1, 0], dtype=torch.uint8)

