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.
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
e garden as she very dunced.
Alice fighting be it. The breats?
here on likegs voice withoup.
`You minced more hal disheze, and I done hippertyou-sage, who say it's a look down whales that
his meckling moruste!' said Alice's can younderen, in they puzzled to them!'
`Of betinkling reple bade to, punthery pormoved the piose himble, of to he see foudhed
just rounds, seef wance side pigs, it addeal sumprked.
`As or the Gryphon,' Alice said,
Fith didn't begun, and she garden as in a who tew.'
Hat hed think after as marman as much the pirly
startares to dreaps
was one poon it
out him were brived they
proce?
CHAT, I fary,' said the Hat,' said the Divery tionly to himpos.'
`Com, planere?"'
`Ica--'
Onlice IN's tread! Wonderieving again, `but her rist,' said Alice.
She
sea do voice.
`I'mm the Panthing alece of the when beaning must anquerrouted not reclow, sobs to
`In of queer behind her houn't seemed
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