Implementation of Differentiable Neural Computer https://www.nature.com/articles/nature20101 as close as possible to the description in the paper. Task: char-level prediction. The repo also includes simple RNN (rnn-numpy.py) and LSTM (lstm-numpy.py). Some external data (ptb, wiki) needs to be downloaded separately.
OMP_WAIT_POLICY=PASSIVE OMP_NUM_THREADS=8 python dnc-debug.py
These versions are cleaned up.
python rnn-numpy.py
python lstm-numpy.py
python dnc-numpy.py
RNN code based on original work by A.Karpathy (min-char-rnn.py)
gist: https://gist.github.com/karpathy/d4dee566867f8291f086
post: http://karpathy.github.io/2015/05/21/rnn-effectiveness/
- RNN version still depends only on numpy
- Added batching
- Modified RNN into an LSTM
- Includes gradient check
- LSTM-controller
- 2D memory array
- content-addressable read/write
- softmax on key similarity causes crashes (divide by 0) - if you experience this, need to restart
- dynamic memory allocation/free
- faster implementation (PyTorch?)
- saving the model
- sample
Time, iteration, BPC (prediction error -> bits per character, lower is better), processing speed
0: 4163.009 s, iter 104800, 1.2808 BPC, 1488.38 char/s
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the middle column has ranges of computed analytical and numerical gradients (these should match more/less)
----
GRAD CHECK
Wxh: n = [-1.828500e-02, 5.292866e-03] min 3.005175e-09, max 3.505012e-07
a = [-1.828500e-02, 5.292865e-03] mean 5.158434e-08 # 10/4
Whh: n = [-3.614049e-01, 6.580141e-01] min 1.549311e-10, max 4.349188e-08
a = [-3.614049e-01, 6.580141e-01] mean 9.340821e-09 # 10/10
Why: n = [-9.868277e-02, 7.518284e-02] min 2.378911e-09, max 1.901067e-05
a = [-9.868276e-02, 7.518284e-02] mean 1.978080e-06 # 10/10
Whr: n = [-3.652128e-02, 1.372321e-01] min 5.520914e-09, max 6.750276e-07
a = [-3.652128e-02, 1.372321e-01] mean 1.299713e-07 # 10/10
Whv: n = [-1.065475e+00, 4.634808e-01] min 6.701966e-11, max 1.462031e-08
a = [-1.065475e+00, 4.634808e-01] mean 4.161271e-09 # 10/10
Whw: n = [-1.677826e-01, 1.803906e-01] min 5.559963e-10, max 1.096433e-07
a = [-1.677826e-01, 1.803906e-01] mean 2.434751e-08 # 10/10
Whe: n = [-2.791997e-02, 1.487244e-02] min 3.806438e-08, max 8.633199e-06
a = [-2.791997e-02, 1.487244e-02] mean 1.085696e-06 # 10/10
Wrh: n = [-7.319636e-02, 9.466716e-02] min 4.183225e-09, max 1.369062e-07
a = [-7.319636e-02, 9.466716e-02] mean 3.677372e-08 # 10/10
Wry: n = [-1.191088e-01, 5.271329e-01] min 1.168224e-09, max 1.568242e-04
a = [-1.191088e-01, 5.271329e-01] mean 2.827306e-05 # 10/10
bh: n = [-1.363950e+00, 9.144058e-01] min 2.473756e-10, max 5.217119e-08
a = [-1.363950e+00, 9.144058e-01] mean 7.066159e-09 # 10/10
by: n = [-5.594528e-02, 5.814085e-01] min 1.604237e-09, max 1.017124e-05
a = [-5.594528e-02, 5.814085e-01] mean 1.026833e-06 # 10/10
https://github.com/JoergFranke/ADNC https://github.com/RobertCsordas/dnc https://github.com/SeemonJ/reinvent-dnc https://github.com/bhpfelix/DNC-NumPy https://github.com/llealgt/DNC https://github.com/yashbonde/Differentiable-Neural-Computer https://github.com/vpegasus/dnc https://github.com/SiliconSloth/ADNC https://github.com/ivannz/krocki_dnc https://github.com/pchlenski/dnc-regex https://github.com/ksluck/sl-dnhc https://github.com/jaspock/dnc https://github.com/brandontrabucco/program-hdc https://arxiv.org/pdf/1505.00521.pdf