deep-belief-network | Python implementation of Deep Belief Networks | Machine Learning library

by albertbup Python Version: Current License: MIT

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kandi X-RAY | deep-belief-network Summary

deep-belief-network is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow, Numpy applications. deep-belief-network 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.

This project works on Python 3.6 and follows the scikit-learn API guidelines. The code includes two implementations: one is built on top of TensorFlow while the other one just uses NumPy. To decide which one to use is as easy as importing the classes from the correct module: dbn.tensorflow for TensorFlow or dbn for NumPy.

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deep-belief-network has a low active ecosystem.

It has 400 star(s) with 182 fork(s). There are 20 watchers for this library.

It had no major release in the last 6 months.

There are 12 open issues and 35 have been closed. On average issues are closed in 49 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of deep-belief-network is current.

Quality

deep-belief-network has 0 bugs and 0 code smells.

Security

deep-belief-network has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

deep-belief-network code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

deep-belief-network 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

deep-belief-network 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.

It has 956 lines of code, 113 functions and 11 files.

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

Top functions reviewed by kandi - BETA

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

Perform a fine tuning step
Estimate stochastic gradient descent
Computes the activation layer
Backpropagation
Fit the model
Reconstruct the transformed units
Compute the reconstruction error
Perform stochastic gradient descent
Fine tuning step
Runs stochastic gradient descent
Builds the model
Predict probability for each sample
Predict class probabilities
Saves the model to a file
Return a dictionary representation of the model
Predict label for each label
Transform indices to labels
Nudge the input dataset
Predict input data
Predict the input data
Sample the visible units

Get all kandi verified functions for this library.

deep-belief-network Key Features

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deep-belief-network Examples and Code Snippets

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Community Discussions

Trending Discussions on deep-belief-network

Deep Belief Networks vs Convolutional Neural Networks performance on non-Image Classification Tasks

QUESTION

Deep Belief Networks vs Convolutional Neural Networks performance on non-Image Classification Tasks

Asked 2020-Jan-28 at 16:37

In the paper Improved Classification based on Deep Belief Networks, the authors have stated that for better classification, generative models are used to initialize the model and model features before training a classifier. Typically they are needed to solve separate unsupervised and supervised learning problems. Generative restricted Boltzmann machines and deep belief networks are widely used for unsupervised learning purposes.

My question is that, if I was to perform a non-image multi-class classification task through unsupervised learning, would it be better to use Deep Belief Networks or Convolutional Neural Networks without considering the fact that dataset matters as well?

A similar question related to image-classification tasks was asked here Deep Belief Networks vs Convolutional Neural Networks. The answer stated that DBNs are likely to perform better for non-image classification tasks than CNNs, but is there any evidence available regarding this, or any paper that explores this more deeply?

...

ANSWER

Answered 2020-Jan-28 at 16:37

The operations in a convolutional neural network are specifically tuned towards image processing. E.g the feature extraction convolution with parameter sharing is run on different parts of the image, also CNNs include subsampling layers which can be understood as producing smaller versions of the (processed) input image. Because of this, I would imagine that CNNs have an inherent disadvantage if the input data is not an image or sufficiently image-like.

Source https://stackoverflow.com/questions/59952965

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

Vulnerabilities

No vulnerabilities reported

Install deep-belief-network

You can download it from GitHub.
You can use deep-belief-network 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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