tltorch.BlockTT

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

Attributes:

decomposition

Returns the factors and parameters composing the tensor in factorized form

Methods

from_tensor(tensor, tensorized_shape, rank, ...)	Create a factorized tensor by decomposing a full tensor
new(tensorized_shape, rank[, device, dtype])	Main way to create a Tensorized Matrix
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

init_from_tensor

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

Main way to create a Tensorized Matrix

Parameters:

tensorized_shapetuple[int]

rankint, ‘same’ or float

rank of the decomposition

n_matricestuple or int, default is ()

if not (), indicates how many matrices have to be jointly factorized

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

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

Returns:

TensorizedTensor

Tensor in Tensorized and 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

classmethod from_tensor(tensor, tensorized_shape, rank, **kwargs)[source]

Create a factorized tensor by decomposing a full tensor

Parameters:

tensortorch.tensor

tensor to factorize

shapetuple[int]

shape of the factorized tensor to create

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.