pydnn: High performance GPU neural network library for deep learning in Python

pydnn is a deep neural network library written in Python using Theano (symbolic math and optimizing compiler package). It was written for Kaggle's National Data Science Bowl competition in March 2015, where it produced an entry finishing in the top 6%. Continued development is planned, including support for even more of the most important deep learning techniques (RNNs...)

Design Goals

• Simplicity

  Wherever possible simplify code to make it a clear expression of underlying deep learning algorithms. Minimize cognitive overhead, so that it is easy for someone who has completed the deeplearning.net tutorials to pickup this library as a next step and easily start learning about, using, and coding more advanced techniques.

• Completeness

  Include all the important and popular techniques for effective deep learning and not techniques with more marginal or ambiguous benefit.

• Ease of use

  Make preparing a dataset, building a model and training a deep network only a few lines of code; enable users to work with NumPy rather than Theano.

• Performance

  Should be roughly on par with other Theano neural net libraries so that pydnn is a viable choice for computationally intensive deep learning.

Features

• High performance GPU training (courtesy of Theano)
• Quick start tools to instantly get started training on inexpensive Amazon EC2 GPU instances.

• Implementations of important new techniques recently reported in the literature:

  • Batch Normalization
  • Parametric ReLU activation function,
  • Adam optimization
  • AdaDelta optimization
  • etc.

• Implementations of standard deep learning techniques:

  • Stochastic Gradient Descent with Momentum
  • Dropout
  • convolutions with max-pooling using overlapping windows
  • ReLU/Tanh/sigmoid activation functions
  • etc.

Documentation

http://pydnn.readthedocs.org/en/latest/index.html

Installation

pip install pydnn

Home Page

https://github.com/zackriegman/pydnn

Usage

First download and unzip raw image data from somewhere (e.g. Kaggle). Then:

import pydnn
import numpy as np
rng = np.random.RandomState(e.rng_seed)

# build data, split into training/validation sets, preprocess
train_dir = 'home\ubuntu\train'
data = pydnn.data.DirectoryLabeledImageSet(train_dir).build()
data = pydnn.preprocess.split_training_data(data, 64, 80, 15, 5)
resizer = pydnn.preprocess.StretchResizer()
pre = pydnn.preprocess.Rotator360(data, (64, 64), resizer, rng)

# build the neural network
net = pydnn.nn.NN(pre, 'images', 121, 64, rng, pydnn.nn.relu)
net.add_convolution(72, (7, 7), (2, 2))
net.add_dropout()
net.add_convolution(128, (5, 5), (2, 2))
net.add_dropout()
net.add_convolution(128, (3, 3), (2, 2))
net.add_dropout()
net.add_hidden(3072)
net.add_dropout()
net.add_hidden(3072)
net.add_dropout()
net.add_logistic()

# train the network
lr = pydnn.nn.Adam(learning_rate=pydnn.nn.LearningRateDecay(
            learning_rate=0.006,
            decay=.1))
net.train(lr)

From raw data to trained network (including specifying network architecture) in 25 lines of code.

Short Term Goals

• Implement popular RNN techniques.
• Integrate with Amazon EC2 clustering software (such as StarCluster).
• Integrate with hyper-parameter optimization frameworks (such as Spearmint and hyperopt).

Authors

Isaac Kriegman