simpleCNN | simple neural networks framework with CNN Pooling layer

by beekbin Python Version: Current License: No License

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kandi X-RAY | simpleCNN Summary

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

It has CNN layer, Pooling layer, FC layer, and softmax layer. The CNN layer, Pooling layer and FC layer can be stacked up to construct deeper neural networks. A convolution layer and a max pooling layer are added to my vanilla neural network framework. With these two additional layers, a CNN can be built via this simple framework. The main.py file demonstrates how to use the simple framework to build a CNN, and how to train the CNN with MNIST dataset.

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Support

simpleCNN has a low active ecosystem.

It has 0 star(s) with 1 fork(s). There are no watchers for this library.

It had no major release in the last 6 months.

simpleCNN has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of simpleCNN is current.

Quality

simpleCNN has 0 bugs and 0 code smells.

Security

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

simpleCNN code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

simpleCNN does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

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

simpleCNN 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.

It has 1739 lines of code, 182 functions and 18 files.

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

Top functions reviewed by kandi - BETA

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

Construct a CNN .
Calculate the convolution layer .
Get a slice of the image at the given indices .
Construct a network layer .
Connects the layers .
Load data .
Return a list of all the kernels for n .
Calculate the weight of the input layer .
Find the maximum value of a given layer .
Train the network .

Get all kandi verified functions for this library.

simpleCNN Key Features

No Key Features are available at this moment for simpleCNN.

simpleCNN Examples and Code Snippets

No Code Snippets are available at this moment for simpleCNN.

Community Discussions

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Vulnerabilities

No vulnerabilities reported

Install simpleCNN

You can download it from GitHub.
You can use simpleCNN 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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