To reference or cite the objects present in the Software Heritage archive, permalinks based on SoftWare Heritage persistent IDentifiers (SWHIDs) must be used.
Select below a type of object currently browsed in order to display its associated SWHID and permalink.
Quick-Start =========== A short overview of TensorLy to get started quickly. Tensor operations ----------------- First import TensorLy: .. code-block:: python import tensorly as tl In the code written in TensorLy, you may notice we use function from tensorly rather than, say, NumPy. This is because we support several backends and we want the correct function to be called depending on the backend. For instance `tensorly.max` calls either the MXNet, NumPy or PyTorch version depending on the backend. There are other subtlties that are handled by the backend to allow a common API regardless of the backend use. .. note:: By default, the backend is set to NumPy. You can change the backend using `tensorly.set_backend`. For more information on the backend, refer to :doc:`./backend`. Tensors can be created, e.g. from numpy arrays: .. code-block:: python import numpy as np # create a random 10x10x10 tensor tensor = np.random.random((10, 10, 10)) You can then easily perform basic tensor operations: .. code-block:: python # mode-1 unfolding (i.e. zeroth mode) unfolded = tl.unfold(tensor, mode=0) # refold the unfolded tensor tl.fold(unfolded, mode=0, shape=tensor.shape) Tensor algebra -------------- More '*advanced*' tensor algebra functions are located in the aptly named :py:mod:`tensorly.tenalg` module. Tensor decomposition -------------------- Decompositions are in the :py:mod:`tensorly.decomposition` module. .. code-block:: python from tensorly.decomposition import tucker, parafac, non_negative_tucker # decompositions are one-liners: factors = parafac(tensor, rank=5) core, factors = tucker(tensor, ranks=[5, 5, 5]) core, factors = non_negative_tucker(tensor, ranks=[5, 5, 5]) Tensor regressions ------------------ Located in the :py:mod:`tensorly.regression` module, tensor regression are objects that have a scikit-learn-like API, with a fit method for optimising the parameters and a predict one for applyting the method to new unseen data. Metrics ------- Whether you are training a tensor regression method or combining deep learning and tensor methods, you will need metrics to train and assess your method. These are implemented in the :py:mod:`tensorly.metrics` module Sampling random tensors ----------------------- To create random tensors, you can use the :py:mod:`tensorly.random` module.