# `tltorch`.FactorizedTensor¶

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

Tensor Factorization

Important

All tensor factorization must have an order parameter

Attributes
`name`

Factorization name (‘tucker’, ‘tt’, ‘cp’, …)

`ndim`

Order of the tensor

Methods

 Order of the tensor `forward`([indices]) To use a tensor factorization within a network, use `tensor.forward`, or, equivalently, `tensor()` `from_tensor`(tensor, shape, rank[, factorization]) Create a factorized tensor by decomposing a full tensor `new`(shape, rank[, factorization]) Main way to create a factorized tensor `normal_`([mean, std]) Inialize the factors of the factorization such that the reconstruction follows a Gaussian distribution `size`([index]) shape of the tensor Reconstruct the full tensor from its factorized form
classmethod `new`(shape, rank, factorization='CP', **kwargs)[source]

Main way to create a factorized tensor

Parameters
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.

classmethod `from_tensor`(tensor, shape, rank, factorization='CP', **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.

`forward`(indices=None, **kwargs)[source]

To use a tensor factorization within a network, use `tensor.forward`, or, equivalently, `tensor()`

Parameters
indicesint or tuple[int], optional

use to index the tensor during the forward pass, by default None

Returns
TensorFactorization

tensor[indices]

`to_tensor`()[source]

Reconstruct the full tensor from its factorized form

`dim`()[source]

Order of the tensor

Notes

fact_tensor.dim() == fact_tensor.ndim

property `ndim`

Order of the tensor

Notes

fact_tensor.dim() == fact_tensor.ndim

`size`(index=None)[source]

shape of the tensor

Parameters
indexint, or tuple, default is None

if not None, returns tensor.shape[index]

See also

`shape`
`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
property `name`

Factorization name (‘tucker’, ‘tt’, ‘cp’, …)