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A highly extensible deep learning framework
| Date | Stars |
|---|---|
| 2026-07-31 | 423 |
| 2026-08-05 | 423 |
| 2026-08-06 | 423 |
| 2026-08-13 | 423 |
| 2026-08-14 | 423 |
| 2026-08-18 | 423 |
| 2026-09-20 | 423 |
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growth rate 0.00%/day
deepy: A highly extensible deep learning framework based on Theano
===
[](https://travis-ci.org/zomux/deepy)
[](https://scrutinizer-ci.com/g/zomux/deepy/?branch=master)
[](https://badge.fury.io/py/deepy)
[](https://requires.io/github/zomux/deepy/requirements/?branch=master)
[](http://deepy.readthedocs.org/en/latest/)
[](https://github.com/zomux/deepy/blob/master/LICENSE)
*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/](http://deepy.readthedocs.org/en/latest/)
Clean interface
===
```python
# 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
```
- Recurrent visual attention model
- [Result visualization](http://raphael.uaca.com/experiments/recurrent_visual_attention/Plot%20attentions.html)
```
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
```
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