NumPy 中的微分神经计算
可微分神经计算 https://www.nature.com/article/nature20101 的实现尽可能接近于本文的描述。任务：char-level 预测。报告还包括简单RNN(RNN-numpy.py)和LSTM(LSTM-numpy.py)。一些外部数据(ptb、wiki)需要单独下载。
译者前言
本文的项目的作者是一个老外 krocki，关于dnc、rnn、lstm 的实现源码都在他的github仓库 https://github.com/krocki/dnc。
快速开始
python dnc-debug.py
这些版本已完成。
python rnn-numpy.py
python lstm-numpy.py
python dnc-numpy.py
积分
RNN代码基于A.Karpath(min-char-rnn.py)的原始工作
gist: https://gist.github.com/karpathy/d4dee566867f8291f086
文章: http://karpathy.github.io/2015/05/21/rnn-effectiveness/
特性
- RNN版本仍然依赖numpy
- 添加批处理
- 将RNN修改为LSTM
- 包括梯度检测
DNC
实施
- LSTM控制器
- 2D存储器数组
- 内容可寻址的读/写
问题
关键相似度的softmax会导致崩溃（除以0） - 如果遇到这种情况，需要重新启动
将要做
- 动态内存分配/自由
- 更快的实现（使用PyTorch？）
- 保存模型
- 例子
示例输出：
时间，迭代，BPC（预测误差 - >每个字符的位数，越低越好），处理速度
0: 4163.009 s, iter 104800, 1.2808 BPC, 1488.38 char/s
模型中的样本（alice29.txt）：
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
检查反向传递的数值梯度（最右边的列应该具有值<1e-4）;
中间列具有计算的分析和数值梯度范围（这些应该更多/更少）
----
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
文章出处
由NumPy中文文档翻译，原作者为 krocki，翻译至：https://github.com/krocki/dnc
关于dnc、rnn、lstm 的实现源码都在https://github.com/krocki/dnc。