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 >= 0.8

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

karlnapf/Deep-Learning-TensorFlow