theanet | Convolutional Neural Network for Image Classification | Machine Learning library

by rakeshvar Python Version: Current License: Apache-2.0

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

kandi X-RAY | theanet Summary

theanet is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow, Keras applications. theanet has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.

Uses Theano to build a convolutional neural network with features like.

Support

Quality

Security

License

Reuse

Support

theanet has a low active ecosystem.

It has 49 star(s) with 23 fork(s). There are 9 watchers for this library.

It had no major release in the last 6 months.

There are 0 open issues and 2 have been closed. On average issues are closed in 161 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of theanet is current.

Quality

theanet has 0 bugs and 31 code smells.

Security

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

theanet code analysis shows 0 unresolved vulnerabilities.

There are 1 security hotspots that need review.

License

theanet is licensed under the Apache-2.0 License. This license is Permissive.

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

Reuse

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

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

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

theanet saves you 678 person hours of effort in developing the same functionality from scratch.

It has 1571 lines of code, 101 functions and 19 files.

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

Top functions reviewed by kandi - BETA

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

Append the next layer
Compute the trin model
Get updates for a given cost function
Calculate the cost function
Calculates the WeightedT cost
Return the value of a shared variable
Truncate negative log likelihood
Load MNIST dataset
Computes the test function
Calculates the mean error rate and second error rate
Run test
Evaluate the test test
Compiles the test function
Reshape the shape of an image
Print info about the fit
Prints information about all layers
Return a description of the ws
Generate test indices
Test the test

Get all kandi verified functions for this library.

theanet Key Features

No Key Features are available at this moment for theanet.

theanet Examples and Code Snippets

No Code Snippets are available at this moment for theanet.

Community Discussions

Trending Discussions on theanet

How to use a theanets neural network model in deeplearning4j?

QUESTION

How to use a theanets neural network model in deeplearning4j?

Asked 2017-Jan-05 at 11:13

I have trained theanets neural network model and I want to use the same model in deeplearning4j, any suggestions?

...

ANSWER

Answered 2017-Jan-05 at 11:13

We have keras model import if you can map it to that. That's as close as it's going to get though.

https://deeplearning4j.org/model-import-keras

Beyond that it depends on the mdoel.

Source https://stackoverflow.com/questions/41482962

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

Vulnerabilities

No vulnerabilities reported

Install theanet

You can download it from GitHub.
You can use theanet 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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