A short overview of TensorLy to get started quickly.
1.1. Tensor operations
First import TensorLy:
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.
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 TensorLy’s backend system.
Tensors can be created, e.g. from numpy arrays:
import numpy as np # create a random 10x10x10 tensor tensor = np.random.random((10, 10, 10))
You can then easily perform basic tensor operations:
# 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)
1.2. Tensor algebra
More ‘advanced’ tensor algebra functions are located in the aptly named
1.3. Tensor decomposition
Decompositions are in the
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])
1.4. Tensor regressions
Located in the
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.
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
1.6. Sampling random tensors
To create random tensors, you can use the