simpleNN | simple package used for training Convolutional Neural | Machine Learning library

by cjlin1 Python Version: Current License: BSD-3-Clause

X-Ray Key Features Code Snippets Community Discussions(3)Vulnerabilities Install Support

kandi X-RAY | simpleNN Summary

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

SimpleNN is a simple package used for training Convolutional Neural Network (CNN) with following supports:. Currently, both implementations support two optimization methods: Newton method and stochastic gradient method (SG). The implementation document of Newton method is available at

Support

Quality

Security

License

Reuse

Support

simpleNN has a low active ecosystem.

It has 47 star(s) with 16 fork(s). There are 13 watchers for this library.

It had no major release in the last 6 months.

There are 9 open issues and 4 have been closed. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of simpleNN is current.

Quality

simpleNN has 0 bugs and 0 code smells.

Security

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

simpleNN code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

simpleNN is licensed under the BSD-3-Clause License. This license is Permissive.

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

Reuse

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

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

simpleNN saves you 379 person hours of effort in developing the same functionality from scratch.

It has 858 lines of code, 42 functions and 6 files.

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

Top functions reviewed by kandi - BETA

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

Calculate all ops in a minibatch
R Gauss - Newton V
Rop op
Inverse of tensors
Calculate the norm of a tensor
Vectorize tensors
Create a newton model
Perform a minibatch
Runs a singleton function
Gradient tracer
Run prediction on a given network
Creates a CNN
Read image data
Normalize images
Grain function
Perform prediction on a network
Parse arguments
Create a tf model

Get all kandi verified functions for this library.

simpleNN Key Features

No Key Features are available at this moment for simpleNN.

simpleNN Examples and Code Snippets

No Code Snippets are available at this moment for simpleNN.

Community Discussions

Trending Discussions on simpleNN

How can I use the LBFGS optimizer with pytorch ignite?

How to visualize our network in Matconvnet?

Can't replicate a matconvnet CNN architecture in Keras

QUESTION

How can I use the LBFGS optimizer with pytorch ignite?

Asked 2019-Sep-23 at 09:50

I started using Ignite recently and i found it very interesting. I would like to train a model using as an optimizer the LBFGS algorithm from the torch.optim module.

This is my code:

...

ANSWER

Answered 2019-Sep-23 at 09:50

The way to do it is like this:

Source https://stackoverflow.com/questions/57806980

QUESTION

How to visualize our network in Matconvnet?

Asked 2018-Mar-17 at 12:41

Is there any function to visualize the network like this?

I want to visualize both dagnn and simplenn architectures.

...

ANSWER

Answered 2018-Mar-17 at 11:19

Firstly, install Graph Visualization Software (Grphviz) by this link. Then as this describes, you should utilize dot format to take advantage of Grphviz. For instance if you wanna to plot the whole dag network follow as below :

Source https://stackoverflow.com/questions/49218879

QUESTION

Can't replicate a matconvnet CNN architecture in Keras

Asked 2017-Jul-04 at 21:08

I have the following architecture of a Convolutional Neural Network in matconvnet which I use to train on my own data:

...

ANSWER

Answered 2017-Jul-04 at 21:08

In your MatConvNet version, you use SGD with momentum.

In Keras, you use rmsprop

With a different learning rule you should try different learning rates. Also sometimes momentum is helpful when training a CNN.

Could you try the SGD+momentum in Keras and let me know what happens?

Another thing that might be different is that the initialization. for example in MatConvNet you use gaussian initialization with f= 0.0125 as the standard deviation. In Keras I'm not sure about the default initialization.

In general, if you don't use batch normalization, the network is prone to many numerical issues. If you use batch normalization in both networks, I bet the results would be similar. Is there any reason you don't want to use batch normalization?

Source https://stackoverflow.com/questions/44863192

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

Vulnerabilities

No vulnerabilities reported

Install simpleNN

You can download it from GitHub.
You can use simpleNN 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 .

Find more information at: