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igammac_(other) β Tensor
In-place version of igammac() | torch.tensors#torch.Tensor.igammac_ |
igamma_(other) β Tensor
In-place version of igamma() | torch.tensors#torch.Tensor.igamma_ |
imag
Returns a new tensor containing imaginary values of the self tensor. The returned tensor and self share the same underlying storage. Warning imag() is only supported for tensors with complex dtypes. Example::
>>> x=torch.randn(4, dtype=torch.cfloat)
>>> x
tensor([(0.3100+0.3553j), (-0.5445-0.7896j), (-1.6492-0.0633j), (-0.0638-0.8119j)])
>>> x.imag
tensor([ 0.3553, -0.7896, -0.0633, -0.8119]) | torch.tensors#torch.Tensor.imag |
index_add(tensor1, dim, index, tensor2) β Tensor
Out-of-place version of torch.Tensor.index_add_(). tensor1 corresponds to self in torch.Tensor.index_add_(). | torch.tensors#torch.Tensor.index_add |
index_add_(dim, index, tensor) β Tensor
Accumulate the elements of tensor into the self tensor by adding to the indices in the order given in index. For example, if dim == 0 and index[i] == j, then the ith row of tensor is added to the jth row of self. The dimth dimension of tensor must have the same size as the length of index (which must be a vector), and all other dimensions must match self, or an error will be raised. Note This operation may behave nondeterministically when given tensors on a CUDA device. See Reproducibility for more information. Parameters
dim (int) β dimension along which to index
index (IntTensor or LongTensor) β indices of tensor to select from
tensor (Tensor) β the tensor containing values to add 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.]]) | torch.tensors#torch.Tensor.index_add_ |
index_copy(tensor1, dim, index, tensor2) β Tensor
Out-of-place version of torch.Tensor.index_copy_(). tensor1 corresponds to self in torch.Tensor.index_copy_(). | torch.tensors#torch.Tensor.index_copy |
index_copy_(dim, index, tensor) β Tensor
Copies the elements of tensor into the self tensor by selecting the indices in the order given in index. For example, if dim == 0 and index[i] == j, then the ith row of tensor is copied to the jth row of self. The dimth dimension of tensor must have the same size as the length of index (which must be a vector), and all other dimensions must match self, or an error will be raised. Note If index contains duplicate entries, multiple elements from tensor will be copied to the same index of self. The result is nondeterministic since it depends on which copy occurs last. Parameters
dim (int) β dimension along which to index
index (LongTensor) β indices of tensor to select from
tensor (Tensor) β the tensor containing values to copy 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.]]) | torch.tensors#torch.Tensor.index_copy_ |
index_fill(tensor1, dim, index, value) β Tensor
Out-of-place version of torch.Tensor.index_fill_(). tensor1 corresponds to self in torch.Tensor.index_fill_(). | torch.tensors#torch.Tensor.index_fill |
index_fill_(dim, index, val) β Tensor
Fills the elements of the self tensor with value val by selecting the indices in the order given in index. Parameters
dim (int) β dimension along which to index
index (LongTensor) β indices of self tensor to fill in
val (float) β the value to fill with 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.]]) | torch.tensors#torch.Tensor.index_fill_ |
index_put(tensor1, indices, values, accumulate=False) β Tensor
Out-place version of index_put_(). tensor1 corresponds to self in torch.Tensor.index_put_(). | torch.tensors#torch.Tensor.index_put |
index_put_(indices, values, accumulate=False) β Tensor
Puts values from the tensor values into the tensor self using the indices specified in indices (which is a tuple of Tensors). The expression tensor.index_put_(indices, values) is equivalent to tensor[indices] = values. Returns self. If accumulate is True, the elements in values are added to self. If accumulate is False, the behavior is undefined if indices contain duplicate elements. Parameters
indices (tuple of LongTensor) β tensors used to index into self.
values (Tensor) β tensor of same dtype as self.
accumulate (bool) β whether to accumulate into self | torch.tensors#torch.Tensor.index_put_ |
index_select(dim, index) β Tensor
See torch.index_select() | torch.tensors#torch.Tensor.index_select |
indices() β Tensor
Return the indices tensor of a sparse COO tensor. Warning Throws an error if self is not a sparse COO tensor. See also Tensor.values(). Note This method can only be called on a coalesced sparse tensor. See Tensor.coalesce() for details. | torch.sparse#torch.Tensor.indices |
inner(other) β Tensor
See torch.inner(). | torch.tensors#torch.Tensor.inner |
int(memory_format=torch.preserve_format) β Tensor
self.int() is equivalent to self.to(torch.int32). See to(). Parameters
memory_format (torch.memory_format, optional) β the desired memory format of returned Tensor. Default: torch.preserve_format. | torch.tensors#torch.Tensor.int |
int_repr() β Tensor
Given a quantized Tensor, self.int_repr() returns a CPU Tensor with uint8_t as data type that stores the underlying uint8_t values of the given Tensor. | torch.tensors#torch.Tensor.int_repr |
inverse() β Tensor
See torch.inverse() | torch.tensors#torch.Tensor.inverse |
isclose(other, rtol=1e-05, atol=1e-08, equal_nan=False) β Tensor
See torch.isclose() | torch.tensors#torch.Tensor.isclose |
isfinite() β Tensor
See torch.isfinite() | torch.tensors#torch.Tensor.isfinite |
isinf() β Tensor
See torch.isinf() | torch.tensors#torch.Tensor.isinf |
isnan() β Tensor
See torch.isnan() | torch.tensors#torch.Tensor.isnan |
isneginf() β Tensor
See torch.isneginf() | torch.tensors#torch.Tensor.isneginf |
isposinf() β Tensor
See torch.isposinf() | torch.tensors#torch.Tensor.isposinf |
isreal() β Tensor
See torch.isreal() | torch.tensors#torch.Tensor.isreal |
istft(n_fft, hop_length=None, win_length=None, window=None, center=True, normalized=False, onesided=None, length=None, return_complex=False) [source]
See torch.istft() | torch.tensors#torch.Tensor.istft |
is_coalesced() β bool
Returns True if self is a sparse COO tensor that is coalesced, False otherwise. Warning Throws an error if self is not a sparse COO tensor. See coalesce() and uncoalesced tensors. | torch.sparse#torch.Tensor.is_coalesced |
is_complex() β bool
Returns True if the data type of self is a complex data type. | torch.tensors#torch.Tensor.is_complex |
is_contiguous(memory_format=torch.contiguous_format) β bool
Returns True if self tensor is contiguous in memory in the order specified by memory format. Parameters
memory_format (torch.memory_format, optional) β Specifies memory allocation order. Default: torch.contiguous_format. | torch.tensors#torch.Tensor.is_contiguous |
is_cuda
Is True if the Tensor is stored on the GPU, False otherwise. | torch.tensors#torch.Tensor.is_cuda |
is_floating_point() β bool
Returns True if the data type of self is a floating point data type. | torch.tensors#torch.Tensor.is_floating_point |
is_leaf
All Tensors that have requires_grad which is False will be leaf Tensors by convention. For Tensors that have requires_grad which is True, they will be leaf Tensors if they were created by the user. This means that they are not the result of an operation and so grad_fn is None. Only leaf Tensors will have their grad populated during a call to backward(). To get grad populated for non-leaf Tensors, you can use retain_grad(). Example: >>> a = torch.rand(10, requires_grad=True)
>>> a.is_leaf
True
>>> b = torch.rand(10, requires_grad=True).cuda()
>>> b.is_leaf
False
# b was created by the operation that cast a cpu Tensor into a cuda Tensor
>>> c = torch.rand(10, requires_grad=True) + 2
>>> c.is_leaf
False
# c was created by the addition operation
>>> d = torch.rand(10).cuda()
>>> d.is_leaf
True
# d does not require gradients and so has no operation creating it (that is tracked by the autograd engine)
>>> e = torch.rand(10).cuda().requires_grad_()
>>> e.is_leaf
True
# e requires gradients and has no operations creating it
>>> f = torch.rand(10, requires_grad=True, device="cuda")
>>> f.is_leaf
True
# f requires grad, has no operation creating it | torch.autograd#torch.Tensor.is_leaf |
is_meta
Is True if the Tensor is a meta tensor, False otherwise. Meta tensors are like normal tensors, but they carry no data. | torch.tensors#torch.Tensor.is_meta |
is_pinned()
Returns true if this tensor resides in pinned memory. | torch.tensors#torch.Tensor.is_pinned |
is_quantized
Is True if the Tensor is quantized, False otherwise. | torch.tensors#torch.Tensor.is_quantized |
is_set_to(tensor) β bool
Returns True if both tensors are pointing to the exact same memory (same storage, offset, size and stride). | torch.tensors#torch.Tensor.is_set_to |
is_shared() [source]
Checks if tensor is in shared memory. This is always True for CUDA tensors. | torch.tensors#torch.Tensor.is_shared |
is_signed() β bool
Returns True if the data type of self is a signed data type. | torch.tensors#torch.Tensor.is_signed |
is_sparse
Is True if the Tensor uses sparse storage layout, False otherwise. | torch.sparse#torch.Tensor.is_sparse |
item() β 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 | torch.tensors#torch.Tensor.item |
kthvalue(k, dim=None, keepdim=False) -> (Tensor, LongTensor)
See torch.kthvalue() | torch.tensors#torch.Tensor.kthvalue |
lcm(other) β Tensor
See torch.lcm() | torch.tensors#torch.Tensor.lcm |
lcm_(other) β Tensor
In-place version of lcm() | torch.tensors#torch.Tensor.lcm_ |
ldexp(other) β Tensor
See torch.ldexp() | torch.tensors#torch.Tensor.ldexp |
ldexp_(other) β Tensor
In-place version of ldexp() | torch.tensors#torch.Tensor.ldexp_ |
le(other) β Tensor
See torch.le(). | torch.tensors#torch.Tensor.le |
lerp(end, weight) β Tensor
See torch.lerp() | torch.tensors#torch.Tensor.lerp |
lerp_(end, weight) β Tensor
In-place version of lerp() | torch.tensors#torch.Tensor.lerp_ |
less()
lt(other) -> Tensor See torch.less(). | torch.tensors#torch.Tensor.less |
less_(other) β Tensor
In-place version of less(). | torch.tensors#torch.Tensor.less_ |
less_equal(other) β Tensor
See torch.less_equal(). | torch.tensors#torch.Tensor.less_equal |
less_equal_(other) β Tensor
In-place version of less_equal(). | torch.tensors#torch.Tensor.less_equal_ |
le_(other) β Tensor
In-place version of le(). | torch.tensors#torch.Tensor.le_ |
lgamma() β Tensor
See torch.lgamma() | torch.tensors#torch.Tensor.lgamma |
lgamma_() β Tensor
In-place version of lgamma() | torch.tensors#torch.Tensor.lgamma_ |
log() β Tensor
See torch.log() | torch.tensors#torch.Tensor.log |
log10() β Tensor
See torch.log10() | torch.tensors#torch.Tensor.log10 |
log10_() β Tensor
In-place version of log10() | torch.tensors#torch.Tensor.log10_ |
log1p() β Tensor
See torch.log1p() | torch.tensors#torch.Tensor.log1p |
log1p_() β Tensor
In-place version of log1p() | torch.tensors#torch.Tensor.log1p_ |
log2() β Tensor
See torch.log2() | torch.tensors#torch.Tensor.log2 |
log2_() β Tensor
In-place version of log2() | torch.tensors#torch.Tensor.log2_ |
logaddexp(other) β Tensor
See torch.logaddexp() | torch.tensors#torch.Tensor.logaddexp |
logaddexp2(other) β Tensor
See torch.logaddexp2() | torch.tensors#torch.Tensor.logaddexp2 |
logcumsumexp(dim) β Tensor
See torch.logcumsumexp() | torch.tensors#torch.Tensor.logcumsumexp |
logdet() β Tensor
See torch.logdet() | torch.tensors#torch.Tensor.logdet |
logical_and() β Tensor
See torch.logical_and() | torch.tensors#torch.Tensor.logical_and |
logical_and_() β Tensor
In-place version of logical_and() | torch.tensors#torch.Tensor.logical_and_ |
logical_not() β Tensor
See torch.logical_not() | torch.tensors#torch.Tensor.logical_not |
logical_not_() β Tensor
In-place version of logical_not() | torch.tensors#torch.Tensor.logical_not_ |
logical_or() β Tensor
See torch.logical_or() | torch.tensors#torch.Tensor.logical_or |
logical_or_() β Tensor
In-place version of logical_or() | torch.tensors#torch.Tensor.logical_or_ |
logical_xor() β Tensor
See torch.logical_xor() | torch.tensors#torch.Tensor.logical_xor |
logical_xor_() β Tensor
In-place version of logical_xor() | torch.tensors#torch.Tensor.logical_xor_ |
logit() β Tensor
See torch.logit() | torch.tensors#torch.Tensor.logit |
logit_() β Tensor
In-place version of logit() | torch.tensors#torch.Tensor.logit_ |
logsumexp(dim, keepdim=False) β Tensor
See torch.logsumexp() | torch.tensors#torch.Tensor.logsumexp |
log_() β Tensor
In-place version of log() | torch.tensors#torch.Tensor.log_ |
log_normal_(mean=1, std=2, *, generator=None)
Fills self tensor with numbers samples from the log-normal distribution parameterized by the given mean ΞΌ\mu and standard deviation Ο\sigma . Note that mean and std are the mean and standard deviation of the underlying normal distribution, and not of the returned distribution: f(x)=1xΟ2Οeβ(lnβ‘xβΞΌ)22Ο2f(x) = \dfrac{1}{x \sigma \sqrt{2\pi}}\ e^{-\frac{(\ln x - \mu)^2}{2\sigma^2}} | torch.tensors#torch.Tensor.log_normal_ |
long(memory_format=torch.preserve_format) β Tensor
self.long() is equivalent to self.to(torch.int64). See to(). Parameters
memory_format (torch.memory_format, optional) β the desired memory format of returned Tensor. Default: torch.preserve_format. | torch.tensors#torch.Tensor.long |
lstsq(A) -> (Tensor, Tensor)
See torch.lstsq() | torch.tensors#torch.Tensor.lstsq |
lt(other) β Tensor
See torch.lt(). | torch.tensors#torch.Tensor.lt |
lt_(other) β Tensor
In-place version of lt(). | torch.tensors#torch.Tensor.lt_ |
lu(pivot=True, get_infos=False) [source]
See torch.lu() | torch.tensors#torch.Tensor.lu |
lu_solve(LU_data, LU_pivots) β Tensor
See torch.lu_solve() | torch.tensors#torch.Tensor.lu_solve |
map_(tensor, callable)
Applies callable for each element in self tensor and the given tensor and stores the results in self tensor. self tensor and the given tensor must be broadcastable. The callable should have the signature: def callable(a, b) -> number | torch.tensors#torch.Tensor.map_ |
masked_fill(mask, value) β Tensor
Out-of-place version of torch.Tensor.masked_fill_() | torch.tensors#torch.Tensor.masked_fill |
masked_fill_(mask, value)
Fills elements of self tensor with value where mask is True. The shape of mask must be broadcastable with the shape of the underlying tensor. Parameters
mask (BoolTensor) β the boolean mask
value (float) β the value to fill in with | torch.tensors#torch.Tensor.masked_fill_ |
masked_scatter(mask, tensor) β Tensor
Out-of-place version of torch.Tensor.masked_scatter_() | torch.tensors#torch.Tensor.masked_scatter |
masked_scatter_(mask, source)
Copies elements from source into self tensor at positions where the mask is True. The shape of mask must be broadcastable with the shape of the underlying tensor. The source should have at least as many elements as the number of ones in mask Parameters
mask (BoolTensor) β the boolean mask
source (Tensor) β the tensor to copy from Note The mask operates on the self tensor, not on the given source tensor. | torch.tensors#torch.Tensor.masked_scatter_ |
masked_select(mask) β Tensor
See torch.masked_select() | torch.tensors#torch.Tensor.masked_select |
matmul(tensor2) β Tensor
See torch.matmul() | torch.tensors#torch.Tensor.matmul |
matrix_exp() β Tensor
See torch.matrix_exp() | torch.tensors#torch.Tensor.matrix_exp |
matrix_power(n) β Tensor
See torch.matrix_power() | torch.tensors#torch.Tensor.matrix_power |
max(dim=None, keepdim=False) -> Tensor or (Tensor, Tensor)
See torch.max() | torch.tensors#torch.Tensor.max |
maximum(other) β Tensor
See torch.maximum() | torch.tensors#torch.Tensor.maximum |
mean(dim=None, keepdim=False) -> Tensor or (Tensor, Tensor)
See torch.mean() | torch.tensors#torch.Tensor.mean |
median(dim=None, keepdim=False) -> (Tensor, LongTensor)
See torch.median() | torch.tensors#torch.Tensor.median |
min(dim=None, keepdim=False) -> Tensor or (Tensor, Tensor)
See torch.min() | torch.tensors#torch.Tensor.min |
minimum(other) β Tensor
See torch.minimum() | torch.tensors#torch.Tensor.minimum |
mm(mat2) β Tensor
See torch.mm() | torch.tensors#torch.Tensor.mm |
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