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