kandi X-RAY | bpnn Summary
kandi X-RAY | bpnn Summary
bpnn is a Python library. bpnn has no bugs, it has no vulnerabilities and it has low support. However bpnn build file is not available. You can download it from GitHub.
bpnn
bpnn
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Quality
Security
License
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Support
bpnn has a low active ecosystem.
It has 65 star(s) with 65 fork(s). There are 4 watchers for this library.
It had no major release in the last 6 months.
There are 2 open issues and 0 have been closed. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of bpnn is current.
Quality
bpnn has 0 bugs and 0 code smells.
Security
bpnn has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
bpnn code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.
License
bpnn 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.
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bpnn releases are not available. You will need to build from source code and install.
bpnn has no build file. You will be need to create the build yourself to build the component from source.
bpnn saves you 43 person hours of effort in developing the same functionality from scratch.
It has 114 lines of code, 23 functions and 1 files.
It has medium code complexity. Code complexity directly impacts maintainability of the code.
Top functions reviewed by kandi - BETA
kandi has reviewed bpnn and discovered the below as its top functions. This is intended to give you an instant insight into bpnn implemented functionality, and help decide if they suit your requirements.
- Example example function
- Updates the layer weights
- Calculate the activation layer
- Train the model
- Save weights to file
- Get weights for each unit
- Calculate the error for a set of points
- Run test
- Get the weighted weight
- D sigmoid function
- Load weights from file
- Set weights for each unit
- Set weight
- Calculate sigmoid
- Return the sigmoid of x x
Get all kandi verified functions for this library.
bpnn Key Features
No Key Features are available at this moment for bpnn.
bpnn Examples and Code Snippets
No Code Snippets are available at this moment for bpnn.
Community Discussions
Trending Discussions on bpnn
QUESTION
Using Back Propagation Neural Network given continuous class labels
Asked 2017-Oct-23 at 06:36
I am given a dataset where the class labels are continuous values between [-1,1]. Based on this, I have few questions:
- Can I use Back-Propagation Neural Network (BPNN) for this problem? Previously, I had a different dataset where the labels are binary classes but for this dataset I am not sure since it is now a regression problem.
- In case neural network can work with this dataset, what activation function should I use?
Sigmoid, hyperbolic tan function (tanh), or rectified linear unit (relu)?
Thank you.
...ANSWER
Answered 2017-Oct-23 at 06:36Basically yes, Backpropagation works fine for both classification and regression problems.
At the output layer you should use tanh, as it matches the range of your output ([-1, 1]), but for hidden layers you should use ReLU or similar. Do not use sigmoid or tanh for hidden layers (only in recurrent networks) as they will produce the vanishing gradient problem
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install bpnn
You can download it from GitHub.
You can use bpnn 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.
You can use bpnn 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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