cTensor | super light-weight deep learning library | Machine Learning library

by sebgao Python Version: Current License: MIT

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kandi X-RAY | cTensor Summary

cTensor is a Python library typically used in Institutions, Learning, Education, Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Numpy applications. cTensor has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However cTensor build file is not available. You can download it from GitHub.

The cTensor (crafted tensor) is a super light-weight deep learning library (perhaps we cannot even call it a libray). It's based on numpy and furthermore its only one dependency is actually numpy. Features include dynamic graph, autograd and user-defined operations in numpy. The line number of core code is within ~~300~~ 400, making it friendly for study and teaching purpose. It mimics PyTorch framework defacto. Stars are welcomed! : ).

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cTensor has a low active ecosystem.

It has 57 star(s) with 13 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

There are 2 open issues and 2 have been closed. On average issues are closed in 54 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of cTensor is current.

Quality

cTensor has 0 bugs and 0 code smells.

Security

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

cTensor code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

cTensor 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

cTensor releases are not available. You will need to build from source code and install.

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

cTensor saves you 211 person hours of effort in developing the same functionality from scratch.

It has 518 lines of code, 82 functions and 9 files.

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

Top functions reviewed by kandi - BETA

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

Backward computation
Convert an image into bchwkl
Multiply a convolution of a convolution matrix
Batch convolutional function
Convolutional layer
Batch convolutional layer
2d convolutional layer
Mean squared error
Return the mean of this tensor
Compute the tensor
Wrapper for sigmoid
Compute the greater or equal operator
Makes a tensor - like object
Returns a Tensor with leaky_rate
Leaky ReLU activation
Return a new tensor
R Compute the Relu operator
Binary cross entropy
Return the logarithm of the tensor
Step through the model
Zero gradient
A 2d convolutional layer

Get all kandi verified functions for this library.

cTensor Key Features

No Key Features are available at this moment for cTensor.

cTensor Examples and Code Snippets

No Code Snippets are available at this moment for cTensor.

Community Discussions

Trending Discussions on cTensor

How to call a python file using Tensorflow library in C++ environment?

QUESTION

How to call a python file using Tensorflow library in C++ environment?

Asked 2018-Jan-04 at 14:48

When I tried to call a python file using Tensorflow library in C++ environment, I got a problem like this. I have no idea how to solve it.

This is my C++ code.

...

ANSWER

Answered 2018-Jan-04 at 14:48

You import the tensorflow module - and during that import it attempts to use sys.argv (as you can see from your stack trade) and then sometime later in some function, you have

Source https://stackoverflow.com/questions/48097044

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

Vulnerabilities

No vulnerabilities reported

Install cTensor

You can download it from GitHub.
You can use cTensor 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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