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.
deepy now supports training on multiple GPUs, see the following example for training neural machine translation models.
https://github.com/zomux/neuralmt
http://deepy.readthedocs.org/en/latest/
# 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])
source bin/cpu_env.sh
source bin/gpu_env.sh
python experiments/mnist/mlp.py
python experiments/mnist/mlp_dropout.py
python experiments/mnist/mlp_prelu_dropout.py
python experiments/mnist/mlp_maxout.py
python experiments/mnist/deep_convolution.py
python experiments/mnist/mlp_elastic_distortion.py
python experiments/attention_models/baseline.py
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
python experiments/lm/baseline_rnnlm.py
python experiments/lm/class_based_rnnlm.py
python experiments/lm/lstm_rnnlm.py
python experiments/lm/char_lstm.py
python experiments/lm/char_rnn.py
pip install Flask-SocketIO
python experiments/deep_qlearning/server.py
http://localhost:5003
python experiments/auto_encoders/rnn_auto_encoder.py
python experiments/auto_encoders/recursive_auto_encoder.py
python experiments/scipy_training/mnist_cg.py
python experiments/scipy_training/mnist_lbfgs.py
See https://github.com/uaca/deepy-draw
# Train the model
python mnist_training.py
# Create animation
python animation.py experiments/draw/mnist1.gz
python experiments/highway_networks/mnist_baseline.py
python experiments/highway_networks/mnist_highway.py
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