Ready to use implementations of various Deep Learning algorithms using TensorFlow.
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blackecho Merge pull request #57 from MajidD4t1qbit/master
Fixed incorrect ordering of params in RBM._train_model function
Latest commit ddeb1f2 Sep 24, 2017

README.md

Deep Learning algorithms with TensorFlow

This repository is a collection of various Deep Learning algorithms implemented using the TensorFlow library. This package is intended as a command line utility you can use to quickly train and evaluate popular Deep Learning models and maybe use them as benchmark/baseline in comparison to your custom models/datasets. If you want to use the package from ipython or maybe integrate it in your code, I published a pip package named yadlt: Yet Another Deep Learning Tool.

Requirements:

  • tensorflow >= 1.0

List of available models:

  • Convolutional Network
  • Restricted Boltzmann Machine
  • Deep Belief Network
  • Deep Autoencoder as stack of RBMs
  • Denoising Autoencoder
  • Stacked Denoising Autoencoder
  • Deep Autoencoder as stack of Denoising Autoencoders
  • MultiLayer Perceptron
  • Logistic Regression

Installation

Through pip:

pip install yadlt

You can learn the basic usage of the models by looking at the command_line/ directory. Or you can take a look at the documentation.

Note: the documentation is still a work in progress for the pip package, but the package usage is very simple. The classes have a sklearn-like interface, so basically you just have to create the object (e.g. sdae = StackedDenoisingAutoencoder()) and call the fit/predict methods, and the pretrain() method if the model supports it (e.g. sdae.pretrain(X_train, y_train), sdae.fit(X_train, y_train) and predictions = sdae.predict(X_test))

Through github:

  • cd in a directory where you want to store the project, e.g. /home/me
  • clone the repository: git clone https://github.com/blackecho/Deep-Learning-TensorFlow.git
  • cd Deep-Learning-TensorFlow
  • now you can configure the software and run the models (see the documentation)!

Documentation:

You can find the documentation for this project at this link.

Models TODO list

  • Recurrent Networks (LSTMs)
  • Variational Autoencoders
  • Deep Q Reinforcement Learning