tltorch.CPTensor

class tltorch.CPTensor(*args, **kwargs)[source]

CP Factorization

Parameters:

weights

factors

shape

rank

Attributes:

decomposition

Returns the factors and parameters composing the tensor in factorized form

Methods

from_tensor(tensor[, rank])	Create a factorized tensor by decomposing a dense tensor
new(shape, rank[, device, dtype])	Main way to create a factorized tensor
normal_([mean, std])	Inialize the factors of the factorization such that the reconstruction follows a Gaussian distribution
to_tensor()	Reconstruct the full tensor from its factorized form
transduct(new_dim[, mode, new_factor])	Transduction adds a new dimension to the existing factorization

init_from_tensor

classmethod new(shape, rank, device=None, dtype=None, **kwargs)[source]

Main way to create a factorized tensor

Parameters:

shapetuple[int]

shape of the factorized tensor to create

rankint, ‘same’ or float, default is ‘same’

rank of the decomposition

factorization{‘CP’, ‘TT’, ‘Tucker’}, optional

Tensor factorization to use to decompose the tensor, by default ‘Tucker’

Returns:

TensorFactorization

Tensor in Factorized form.

Raises:

ValueError

If the factorization given does not exist.

Examples

Create a Tucker tensor of shape (3, 4, 2) with half the parameters as a dense tensor would:

>>> tucker_tensor = FactorizedTensor.new((3, 4, 2)), rank=0.5, factorization='tucker')

classmethod from_tensor(tensor, rank='same', **kwargs)[source]

Create a factorized tensor by decomposing a dense tensor

Parameters:

tensortorch.tensor

tensor to factorize

rankint, ‘same’ or float

rank of the decomposition

factorization{‘CP’, ‘TT’, ‘Tucker’}, optional

Tensor factorization to use to decompose the tensor, by default ‘CP’

Returns:

TensorFactorization

Tensor in Factorized form.

Raises:

ValueError

If the factorization given does not exist.

property decomposition

Returns the factors and parameters composing the tensor in factorized form

to_tensor()[source]

Reconstruct the full tensor from its factorized form

normal_(mean=0, std=1)[source]

Inialize the factors of the factorization such that the reconstruction follows a Gaussian distribution

Parameters:

meanfloat, currently only 0 is supported

stdfloat

standard deviation

Returns:

self

transduct(new_dim, mode=0, new_factor=None)[source]

Transduction adds a new dimension to the existing factorization

Parameters:

new_dimint

dimension of the new mode to add

modewhere to insert the new dimension, after the channels, default is 0

by default, insert the new dimensions before the existing ones (e.g. add time before height and width)

Returns:

self