yadll 0.0.1

Yet Another Deep Learning Lab. Ultra light Deep Learning framework based on Theano

Yadll

Yet another deep learning lab.

This is an ultra light deep learning framework written in Python and based on Theano. It allows you to very quickly start building Deep Learning models and play with toy examples.

If you are looking for a light deep learning API I would recommend using Lasagne or keras instead of yadll, both are mature, well documented and contributed projects.

Read the documentation at Read the doc

Its main features are:

• Layers:

  • Input Layer

  • Dropout Layer

  • Pool Layer

  • Conv Layer:

    • ConvPool Layer

  • Dense Layer:

    • Logistic Regression

    • Dropconnect

    • Unsupervised Layer:

      • Autoencoder (denoising autoencoder)

      • Restricted Boltzmann Machine

  • RNN

  • LSTM

  • GRU

• Optimisation:

  • Sgd

  • Momentum

  • Nesterov momentum

  • Adagrad

  • Adadelta

  • Rmsprop

  • Adam

  • Adamax

• Hyperparameters grid search

Installation

git clone [e-mail]:pchavanne/yadll.git
cd yadll
pip install -e .

Example

Different networks tested on mnist:

• Logisitic Regression

• Multi Layer Perceptron

• MLP with dropout

• MLP with dropconnect

• Conv Pool

• LeNet-5

• Autoencoder

• Denoising Autoencoder

• Gaussian Denoising Autoencoder

• Contractive Denoising Autoencoder

• Stacked Denoising Autoencoder

• Restricted Boltzmann Machine

• Deep Belief Network

• Recurrent Neural Networks

• Long Short-Term Memory

• Gated Recurrent unit

get the list of available networks:

python mnist_dl.py --network_list

trainning a model for example lenet5:

python mnist_dl.py lenet5

grid search on the hyperparameters:

python hp_grid_search.py