It's the year-end and New Year holidays! Let's write the code! So, I tried the challenge of "doing Deep Learning from scratch with Haskell".

Why use Haskell?

Haskell is cool, isn't it?
It doesn't taste good if you just follow the book
There seems to be fusion, and even in the field of deep learning where calculation efficiency is likely to be required, Haskell is actually Isn't it suitable? I wanted to verify that

Conclusion

** Failure. Haskell wasn't strong enough **

I will give you the code I wrote with the feeling that I can overcome my corpse. https://github.com/spinylobster/my-deep-learning-from-scratch-in-haskell ~~ The progress is not good at all, I wrote the inference and learning of the neural network, but I have not confirmed the actual learning. Since the error back propagation method has not been implemented, it feels like it is in the middle of Chapter 4 ~~

Addition 2017/1/9: A little advanced, the state where the learning of the neural network was completed I'm also implementing AdaGrad, so it feels like it's in the middle of Chapter 6 * Haskell's power is low, so the code is complicated, and it does not follow the code of the book, but it may be a reference when using various libraries.

What you think is the cause

Spent too much time studying Haskell
Relearning forgotten knowledge, using Stack, monad transformers, data serialization, etc.
Originally, I did Wow H books a few years ago.
I spent a lot of time figuring out how to use Gnuplot
I had a calculation that had to have a state, but I didn't know how to write it
~~ Matrix calculation is not safe and I suffered from run-time errors ~~-> ** type It seems that safe calculation is possible! **
It's really hard to debug the running state in Haskell

What I used

Stack --Without this, you can install each package.
Haskell Super Primer-To remember the basic grammar
HMatrix --The equivalent of NumPy. Matrix calculation. There seems to be Repa, and I planned to compare the performance later.
Gnuplot --The equivalent of matplotlib. Drawing a graph. To understand how to use it, it is quick to see here.
GHCi Debugger --I first learned of its existence. Lazy evaluation and the debugger seem to be incompatible. It was really hard to use
Other libraries-see cabal file

Impressions of the book

** Insanely good ** Easy to understand
It covers from the introduction of neural networks to what to learn in the future. ** I think there is no better introductory book **
However, I don't really understand the essential deep learning.
I don't understand the design intent of the CNN convolution layer
I think it's possible that I didn't write it by myself
I want to understand it together with other materials

Helpful URL

Prepare Haskell environment with VS Code
http://qiita.com/DUxCA/items/8e7a68ffee522bdd8918
Install gnuplot on Mac
http://mashiroyuya.hatenablog.com/entry/installgnuplot
Use gnuplot from Haskell
http://d.hatena.ne.jp/mmitou/20120912/1347441319
Understanding how to use hMatrix
https://github.com/albertoruiz/hmatrix/tree/master/examples
You can use +-* / for element-wise calculations
The map for the matrix is cmap I feel that there are times when broadcasting like * matrix * 2 is possible and times when it is not possible. I'm not sure
Haskell Library Impressions 2016
http://syocy.hatenablog.com/entry/haskell-library-2016
Type-level programming in Haskell
http://konn-san.com/articles/2012-06-07-promoted-types-and-list-arguments.html