tensorly | TensorLy : Tensor Learning in Python | Machine Learning library

by tensorly Python Version: 0.8.1 License: Non-SPDX

X-Ray Key Features Code Snippets(10)Community Discussions(4)Vulnerabilities Install Support

kandi X-RAY | tensorly Summary

tensorly is a Python library typically used in Artificial Intelligence, Machine Learning, Pytorch, Tensorflow, Numpy applications. tensorly has no bugs, it has no vulnerabilities, it has build file available and it has high support. However tensorly has a Non-SPDX License. You can install using 'pip install tensorly' or download it from GitHub, PyPI.

TensorLy: Tensor Learning in Python.

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Support

tensorly has a highly active ecosystem.

It has 1387 star(s) with 271 fork(s). There are 44 watchers for this library.

It had no major release in the last 12 months.

There are 51 open issues and 183 have been closed. On average issues are closed in 289 days. There are 5 open pull requests and 0 closed requests.

It has a positive sentiment in the developer community.

The latest version of tensorly is 0.8.1

Quality

tensorly has 0 bugs and 0 code smells.

Security

tensorly has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

tensorly code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

tensorly has a Non-SPDX License.

Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

Reuse

tensorly releases are available to install and integrate.

Deployable package is available in PyPI.

Build file is available. You can build the component from source.

It has 11484 lines of code, 838 functions and 149 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed tensorly and discovered the below as its top functions. This is intended to give you an instant insight into tensorly implemented functionality, and help decide if they suit your requirements.

Calculate parafac .
Validate constraints .
r Constraints a tensor .
Perform a cross product of the input tensor .
Compute the parafac model .
Calculate the non - negative tucker Hessian .
Calculate non - negative parafac hals .
Calculate the HAL decomposition problem .
Generate RST directive .
Import phantom module .

Get all kandi verified functions for this library.

tensorly Key Features

No Key Features are available at this moment for tensorly.

tensorly Examples and Code Snippets

Install guide,To use sparse matrices

Python

Lines of Code : 4

License : Permissive (MIT)

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$ git clone https://github.com/pydata/sparse/ & cd sparse
$ pip install .

$ git clone https://github.com/jcrist/tensorly.git tensorly-sparse & cd tensorly-sparse
$ git checkout sparse-take-2

Optimizing Deep Neural Network,Requirements

Python

Lines of Code : 2

License : No License

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pip install keras
pip install tensorly

prgds,Dependencies:

Python

Lines of Code : 2

License : No License

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conda install -c anaconda gcc

conda install -c conda-forge gsl

partial tucker decomposition

Python

Lines of Code : 45

License : Strong Copyleft (CC BY-SA 4.0)

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import mxnet as mx
import numpy as np
import tensorly as tl
import matplotlib.pyplot as plt
import tensorly.decomposition

# Load data
mnist = mx.test_utils.get_mnist()
train_data = mnist['train_data'][:,0]


err = np.zeros([28,28]) # here

Specific tensor decomposition

Python

Lines of Code : 18

License : Strong Copyleft (CC BY-SA 4.0)

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import tensorly as tl
from tensorly import random
from tensorly.decomposition import partial_tucker

size = 10
order = 3
shape = (size, )*order
tensor = random.random_tensor(shape)

core, fac

Nonnegative tensor decomposition example using Tensorly

Python

Lines of Code : 15

License : Strong Copyleft (CC BY-SA 4.0)

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import tensorly as tl
import numpy as np

X = tl.tensor(np.random.random((10, 11, 12)))

from tensorly.decomposition import robust_pca

D, E = robust_pca(X)

from tensorly.decomposition impor

Multiplication of two-dimensional and three-dimensional tensors

Python

Lines of Code : 11

License : Strong Copyleft (CC BY-SA 4.0)

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import tensorly as tl
import numpy as np
tl.set_backend('tensorflow')

k = 2; m = 3; n = 5; h = 4

A = tl.tensor(np.random.random((m, n)))
B = tl.tensor(np.random.random((k, n, h)))

res = tl.tenalg.contract(A, 1, B, 1)

Python analogue of "ktensor" function from matlab

Python

Lines of Code : 18

License : Strong Copyleft (CC BY-SA 4.0)

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import numpy as np
import tensorly as tl
tensor = tl.tensor([[ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
                    [ 0.,  0.,  0.,  0.,  1.,  1.,  1.,  1.,  0.,  0.,  0.,  0.],
                    [ 0.,  0.,  0.

Kronecker product source code in TensorLy

Python

Lines of Code : 19

License : Strong Copyleft (CC BY-SA 4.0)

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a = [[1, 2, 3],
     [2, 3, 4],
     [3, 4, 5]]
b = [[6, 7]]

[[[[1], [2], [3]]],
 [[[2], [3], [4]]],
 [[[3], [4], [5]]]]

[[[[6, 7]]]]

[[[[1*6, 1*7], [2*6, 2*7], [3*

Truncate SVD decomposition of Pytorch tensor without transfering to cpu

Python

Lines of Code : 5

License : Strong Copyleft (CC BY-SA 4.0)

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import tensorly as tl
tl.set_backend('pytorch')

U, S, V = tl.truncated_svd(matrix, n_eigenvecs=10)

Community Discussions

Trending Discussions on tensorly

partial tucker decomposition

Python tensorly parafac returning ValueError

How do I design a test for the partial_tucker function from tensorly?

tensorly.kruskal_to_tensor() method explanation

QUESTION

partial tucker decomposition

Asked 2021-Dec-28 at 21:06

I want to apply a partial tucker decomposition algorithm to minimize MNIST image tensor dataset of (60000,28,28), in order to conserve its features when applying another machine algorithm afterwards like SVM. I have this code that minimizes the second and third dimension of the tensor

...

ANSWER

Answered 2021-Dec-28 at 21:05

So if you look at the source code for tensorly linked here you can see that the documentation for the function in question partial_tucker says:

Source https://stackoverflow.com/questions/70466992

QUESTION

Python tensorly parafac returning ValueError

Asked 2021-May-07 at 09:33

I have a set of manufactured data (generated from an explicit mathematical function) stored in a 3-dimensional tensor called A. When I try to run parafac, I receive the following:

...

ANSWER

Answered 2021-May-07 at 09:33

In the latest version of TensorLy, parafac returns a CPTensor that acts as a tuple (weight, factors) : in addition to the factors of the decomposition, you also get a vector of weights. This is because the CP decomposition expresses the original tensor as a weighted sum of rank-1 tensors.

In other words, if you are using the latest version of TensorLy, your code should be either:

Source https://stackoverflow.com/questions/67415972

QUESTION

How do I design a test for the partial_tucker function from tensorly?

Asked 2020-Apr-23 at 14:05

I try to design a test in order to verify that the partial_tucker function from tensorly works as I expect it to work. In other words, I want to design an input for the partial_tucker function along with its associated expected output.

So, what I have tried to do is to take an initial random tensor A (of order 4), compute its "low rank" tucker decomposition by hand then reconstruct the tensor of same shape than the initial tensor, say A_tilde. I think the A_tilde tensor is then the "low rank approximation" of the initial tensor A. Am I correct?

Then I would like to us the partial_tucker function on that A_tilde tensor and I expect the result to be the same as the tucker decomposition that I have computed by hand. It is not the case so I guess my handcrafted tucker decomposition is wrong. If so, why?

...

ANSWER

Answered 2020-Apr-23 at 14:05

You are implicitely making a lot of assumptions here: you are assuming, for instance that you can just trim a rank-R decomposition to get the rank-(R-1) decomposition. This is in general not true. Also, note that the Tucker decomposition you are using is not just Higher-Order SVD (HO-SVD). Rather, HO-SVD is used for initialization and followed by Higher Order Orthogonal Iteration (HOOI).

You are also assuming that the low-rank decomposition is unique, for any given rank, which would allow you to compare the factors of the decomposition directly. This is also not the case (even in the matrix case, and with strong constraints such as orthonormality, you still would have sign indeterminacy).

Instead you could for example check the relative reconstruction error. I suggest you have a look at the tests in TensorLy. There are lots of good references on this, if you are starting with tensors. For instance, the seminal work by Kolda and Bader; for Tucker in particular, the work by De Lathauwer et al (eg.on best low-rank approximation of tensors), etc.

Source https://stackoverflow.com/questions/61027712

QUESTION

tensorly.kruskal_to_tensor() method explanation

Asked 2020-Mar-27 at 13:27

I'm trying to understand tl.kruskal_to_tensor () method in tensorly package. In the webpage I understand that it takes as input a list of matrices and produces the tensor whose cp-decomposiiton are the matrices? It takes as input a list of matrices.

But I saw the following code:

...

ANSWER

Answered 2020-Mar-27 at 13:27

This version of kruskal_to_tensor is documented in the dev version of the API.

The np.ones corresponds to the weight of the Kruskal tensor: a Kruskal tensor expresses a tensor as a weighted sum of rank one tensors (outer product of vectors, collected as the columns of the factor matrices). In your case, the weights of the sum are all ones and accumulated in this vector of ones.

Note that you could normalize the factors of your Kruskal tensor and absorb their magnitude in theses weights.

Source https://stackoverflow.com/questions/60876383

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install tensorly

You can install using 'pip install tensorly' or download it from GitHub, PyPI.
You can use tensorly like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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