Tensordot | Code generator for tensor contraction

by smorita Python Version: v0.1 License: MIT

X-Ray Key Features Code Snippets(1)Community Discussions(10)Vulnerabilities Install Support

kandi X-RAY | Tensordot Summary

Tensordot is a Python library. Tensordot has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However Tensordot build file is not available. You can download it from GitHub.

Code generator for tensor contraction.

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Security

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Support

Tensordot has a low active ecosystem.

It has 7 star(s) with 5 fork(s). There are 3 watchers for this library.

It had no major release in the last 12 months.

There are 2 open issues and 4 have been closed. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of Tensordot is v0.1

Quality

Tensordot has no bugs reported.

Security

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

License

Tensordot is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

Tensordot releases are available to install and integrate.

Tensordot has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions are not available. Examples and code snippets are available.

Top functions reviewed by kandi - BETA

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

Generate a script for a tensor
Creates a new tensor
Clone the TensorNetwork
Find bonds between two tensors
Read configuration from file
Connect this edge to two tensors
Add tensors to the graph
Set the style
Optimized search
Contract two tensors
Computes the cost between two bonds
Multiply a vector
Generate the script for a multiplication vector
Transpose a script into a script
Generate a transpose script
Calculate memory from rpn
Parse command line arguments
Gets the math from the rpn string
Output the result script
Output the tensor
Check if the vector is in the Vectors

Get all kandi verified functions for this library.

Tensordot Key Features

No Key Features are available at this moment for Tensordot.

Tensordot Examples and Code Snippets

r Compute the tensor product of a and b .

python

Lines of Code : 178

License : Non-SPDX (Apache License 2.0)

Copy

def tensordot(a, b, axes, name=None):
  r"""Tensor contraction of a and b along specified axes and outer product.

  Tensordot (also known as tensor contraction) sums the product of elements
  from `a` and `b` over the indices specified by `axes`.

Community Discussions

Trending Discussions on Tensordot

Apply matrix dot between a list of matrices and a list of vectors in Numpy

Is there a clever way to get rid of these loops using numpy?

Why is trace not preserved in tensordot

One line einsum functions with "interleaved" output indexing impossible to recreate using tensordot?

3d input for Dense Layer Keras

Keras custom loss function - shape mismatch despite returning same shape as categorical crossentropy

Elegant Numpy Tensor product

Dot product between rows of two matrices with same batch dimension

Efficient way to compute an array matrix multiplication for a batch of arrays

Modifying a matrix to include various data features

QUESTION

Apply matrix dot between a list of matrices and a list of vectors in Numpy

Asked 2021-May-28 at 23:36

Let's suppose I have these two variables

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ANSWER

Answered 2021-May-28 at 23:36

The documentation for @ is found at np.matmul. It is specifically designed for this kind of 'batch' processing:

Source https://stackoverflow.com/questions/67745922

QUESTION

Is there a clever way to get rid of these loops using numpy?

Asked 2021-May-12 at 17:55

I'm reaching the maximum recursion depth and I've been trying to use np.tensordot() I couldn't really get an insight into how to use it in this case.

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ANSWER

Answered 2021-May-12 at 17:52

You can make use of a transposition and broadcasting like the following (untested) code.

Source https://stackoverflow.com/questions/67508499

QUESTION

Why is trace not preserved in tensordot

Asked 2021-May-04 at 16:09

I am trying to take a tensor product of three density matrices and express it in the product basis. Each of these matrices have trace 1 and theoretically, the product matrix should as well. But doing this in numpy seems to have some unintended effects. Even reshaping the intermediary array to a 2d form gives the same answer.

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ANSWER

Answered 2021-May-04 at 15:06

np.tensordot is not a tensor product, but rather a dot product for tensors. The tensor prouct is analoguous to an outer product.

Source https://stackoverflow.com/questions/67387175

QUESTION

One line einsum functions with "interleaved" output indexing impossible to recreate using tensordot?

Asked 2021-May-01 at 16:02

The similarities and differences between NumPy's tensordot and einsum functions are well documented and have been extensively discussed in this forum (e.g. [1], [2], [3], [4], [5]). However, I've run into an instance of matrix multiplication using einsum that I'm finding very difficult, if not impossible, to replicate using tensordot: If our two arrays are,

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ANSWER

Answered 2021-May-01 at 16:02

As I stress in my earlier answers, tensordot is an extension of np.dot, allowing us to specify which dimensions are used in the sum-of-products. The dot default is last of A, 2nd to the last of B.

This illustrates how dot handles dimensions greater than 2:

Source https://stackoverflow.com/questions/67341970

QUESTION

3d input for Dense Layer Keras

Asked 2021-May-01 at 01:07

Is there any example of how Keras Dense layer handles 3D input.

The documentation explains the following:

If the input to the layer has a rank greater than 2, then Dense computes the dot product between the inputs and the kernel along the last axis of the inputs and axis 1 of the kernel (using tf.tensordot).

But I could not understand the internal matrix calculation

For example:

...

ANSWER

Answered 2021-Apr-27 at 07:14

You can reproduce the operation on your own...

Source https://stackoverflow.com/questions/67275213

QUESTION

Keras custom loss function - shape mismatch despite returning same shape as categorical crossentropy

Asked 2021-Apr-30 at 14:00

I've created a custom loss function based on cosine:

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ANSWER

Answered 2021-Apr-30 at 14:00

Your loss function should be able to take in a batch of predictions and ground truth and return a batch of loss values. At the moment, that's not the case, as a tensordot with axis=1 is a matrix multiplication, and you have a conflict of dimensions when you start to introduce a batch dimension.

You can probably use the following instead:

Source https://stackoverflow.com/questions/67334720

QUESTION

Elegant Numpy Tensor product

Asked 2021-Apr-28 at 15:40

I need to take the product over two tensors in numpy (or pytorch):

I have

...

ANSWER

Answered 2021-Apr-28 at 15:40

One way would be to use numpy.einsum.

Source https://stackoverflow.com/questions/67302419

QUESTION

Dot product between rows of two matrices with same batch dimension

Asked 2021-Apr-20 at 03:01

Suppose I have two 2-dimensional tensors with the same batch dimension (that is, same number of rows).

...

ANSWER

Answered 2021-Apr-20 at 00:55

Since both t1 and t2 are 2d arrays, numpy.dot does matrix multiplication. For your case, you can just multiply the two arrays element wise and then sum rows:

Source https://stackoverflow.com/questions/67171060

QUESTION

Efficient way to compute an array matrix multiplication for a batch of arrays

Asked 2021-Apr-05 at 01:17

I want to parallelize the following problem. Given an array w with shape (dim1,) and a matrix A with shape (dim1, dim2), I want each row of A to be multiplied for the corresponding element of w. That's quite trivial.

However, I want to do that for a bunch of arrays w and finally sum the result. So that, to avoid the for loop, I created the matrix W with shape (n_samples, dim1), and I used the np.einsum function in the following way:

...

ANSWER

Answered 2021-Apr-05 at 01:17

In [455]: W = np.arange(1,7).reshape(2,3); A = np.arange(1,13).reshape(3,4)

Source https://stackoverflow.com/questions/66946841

QUESTION

Modifying a matrix to include various data features

Asked 2021-Mar-06 at 15:56

I have the following correlations:

...

ANSWER

Answered 2021-Mar-06 at 15:56

You could try with a dot product and a conditional multiply:

Note that your corr matrix in the setup is missing the index labels hence this additional step, you can ignore this if the corr matrix is what you show in the image.

Source https://stackoverflow.com/questions/66506780

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

Vulnerabilities

No vulnerabilities reported

Install Tensordot

You can download it from GitHub.
You can use Tensordot 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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