https://github.com/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.
https://github.com/thtrieu/essence

auto-differentiation cartpole computational-graphs convolutional-networks convolutional-neural-networks deep-learning deep-q-network dropout lenet lstm lstm-model machine-learning mnist neural-turing-machines question-answering recurrent-neural-networks reinforcement-learning

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

• Host: GitHub
• URL: https://github.com/thtrieu/essence
• Owner: thtrieu
• License: gpl-3.0
• Created: 2016-12-20T19:00:54.000Z (almost 8 years ago)
• Default Branch: master
• Last Pushed: 2020-02-27T16:44:03.000Z (over 4 years ago)
• Last Synced: 2024-11-04T18:45:04.995Z (9 days ago)
• Topics: auto-differentiation, cartpole, computational-graphs, convolutional-networks, convolutional-neural-networks, deep-learning, deep-q-network, dropout, lenet, lstm, lstm-model, machine-learning, mnist, neural-turing-machines, question-answering, recurrent-neural-networks, reinforcement-learning
• Language: Python
• Homepage:
• Size: 19.4 MB
• Stars: 76
• Watchers: 7
• Forks: 18
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
*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:

![img](turing.png)

- `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)