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README.md

deepy: A highly extensible deep learning framework based on Theano

Build Quality PyPI version Requirements Status Documentation Status MIT

deepy is a deep learning framework for designing models with complex architectures.

Many important components such as LSTM and Batch Normalization are implemented inside.

Although highly flexible, deepy maintains a clean high-level interface.

From deepy 0.2.0, you can easily design very complex computational graphs such as Neural Turing Machines.

Example codes will be added shortly.

Recent updates

deepy now supports training on multiple GPUs, see the following example for training neural machine translation models.

https://github.com/zomux/neuralmt

Dependencies

  • Python 2.7 (Better on Linux)
  • numpy
  • theano
  • scipy for L-BFGS and CG optimization

Tutorials (Work in progress)

http://deepy.readthedocs.org/en/latest/

Clean interface

# A multi-layer model with dropout for MNIST task.
from deepy import *

model = NeuralClassifier(input_dim=28*28)
model.stack(Dense(256, 'relu'),
            Dropout(0.2),
            Dense(256, 'relu'),
            Dropout(0.2),
            Dense(10, 'linear'),
            Softmax())

trainer = MomentumTrainer(model)

annealer = LearningRateAnnealer(trainer)

mnist = MiniBatches(MnistDataset(), batch_size=20)

trainer.run(mnist, controllers=[annealer])

Examples

Enviroment setting

  • CPU
source bin/cpu_env.sh
  • GPU
source bin/gpu_env.sh

MNIST Handwriting task

  • Simple MLP
python experiments/mnist/mlp.py
  • MLP with dropout
python experiments/mnist/mlp_dropout.py
  • MLP with PReLU and dropout
python experiments/mnist/mlp_prelu_dropout.py
  • Maxout network
python experiments/mnist/mlp_maxout.py
  • Deep convolution
python experiments/mnist/deep_convolution.py
  • Elastic distortion
python experiments/mnist/mlp_elastic_distortion.py
python experiments/attention_models/baseline.py

Variational auto-encoders

  • Train a model
python experiments/variational_autoencoder/train_vae.py
  • Visualization the output when varying the 2-dimension latent variable
python experiments/variational_autoencoder/visualize_vae.py
  • Result of visualization

Language model

Penn Treebank benchmark

  • Baseline RNNLM (Full-output layer)
python experiments/lm/baseline_rnnlm.py
  • Class-based RNNLM
python experiments/lm/class_based_rnnlm.py
  • LSTM based LM (Full-output layer)
python experiments/lm/lstm_rnnlm.py

Char-based language models

  • Char-based LM with LSTM
python experiments/lm/char_lstm.py
  • Char-based LM with Deep RNN
python experiments/lm/char_rnn.py

Deep Q learning

  • Start server
pip install Flask-SocketIO
python experiments/deep_qlearning/server.py
  • Open this address in browser
http://localhost:5003

Auto encoders

  • Recurrent NN based auto-encoder
python experiments/auto_encoders/rnn_auto_encoder.py
  • Recursive auto-encoder
python experiments/auto_encoders/recursive_auto_encoder.py

Train with CG and L-BFGS

  • CG
python experiments/scipy_training/mnist_cg.py
  • L-BFGS
python experiments/scipy_training/mnist_lbfgs.py

Other experiments

DRAW

See https://github.com/uaca/deepy-draw

# Train the model
python mnist_training.py
# Create animation
python animation.py experiments/draw/mnist1.gz

Highway networks

python experiments/highway_networks/mnist_baseline.py
python experiments/highway_networks/mnist_highway.py

Effect of different initialization schemes

python experiments/initialization_schemes/gaussian.py
python experiments/initialization_schemes/uniform.py
python experiments/initialization_schemes/xavier_glorot.py
python experiments/initialization_schemes/kaiming_he.py

Sorry for that deepy is not well documented currently, but the framework is designed in the spirit of simplicity and readability. This will be improved if someone requires.

Raphael Shu, 2016

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A highly extensible deep learning framework

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