Implementation of a Multimodal Deep Boltzmann Machine with Tensorflow.

This is a project realized in the course of Probabilistic Graphical Model, Object Recognition and Computer Vision.

The goal is to implement a Multi-DBM model with Tensorflow. This is inspired from the paper of Nitish Srivastava et al appeared at NIPS2012.

The code is built on the top of the Deep-Learning-Tensorflow

• Convolutional Network
• Recurrent Neural Network (LSTM)
• 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

• Multimodal Deep Boltzmann Machine
• Variational Autoencoders
• Deep Q Reinforcement Learning