thtrieu / essence

AutoDiff DAG constructor, built on numpy and Cython. A Neural Turing Machine and DeepQ agent run on it. Clean code for educational purpose.

What I cannot create, I do not understand - Richard Feynman

essence

A directed acyclic computational graph builder, built from scratch on numpy and C, including auto-differentiation.

This was not just another deep learning library, its minimal code base was supposed to demonstrate how to:

• Build neural net modules.
• Put the modules together.
• Efficiently compute gradients from this design.

Demos

• mnist-mlp.py: Depth-2 multi layer perceptron, with ReLU and Dropout; 95.3% on MNIST.

• lenet-bn.py: LeNet with Batch Normalization on first layer, 97% on MNIST.

• lstm-embed.py: LSTM on word embeddings for Vietnamese Question classification + Dropout + L2 weight decay. 85% on test set and 98% on training set (overfit).

• turing-copy.py: A neural turing machine with LSTM controller. Test result on copy task length 70:

• visual-answer.py. Visual question answering with pretrained weight from VGG16 and a stack of 3 basic LSTMs, on Glove word2vec.

Q: What is the animal in the picture?      . A: cat
Q: Is there any person in the picture?     . A: no
Q: What is the cat doing?                  . A: sitting
Q: Where is the cat sitting on?            . A: floor
Q: What is the cat color?                  . A: white
Q: Is the cat smiling?                     . A: yes

• dqn-cartpole.py: A classic solved with DQN, with experience replay and target network ofcourse. (Illustration below is one-take)

TODO: Memory network and GAN, for that I need to improve my speed of im2col and gemm for conv module first.

License

GPL 3.0 (see License in this repo)