deep neural network library in Python
pydnn is a deep neural network library written in Python using Theano (symbolic math and optimizing compiler package). I wrote it as a learning project while competing in Kaggle’s National Data Science Bowl in March 2015 (where it produced an entry finishing in the top 6%) and plan to continue developing it by adding support for the most important deep learning techniques (including RNNs).
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.
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.
Should be roughly on par with other Theano neural net libraries so that pydnn is a viable choice for computationally intensive deep learning.
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:
Parametric ReLU activation function,
- Implementations of standard deep learning techniques:
Stochastic Gradient Descent with Momentum
convolutions with max-pooling using overlapping windows
ReLU/Tanh/sigmoid activation functions
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).
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