Even though I don’t really play go these days anymore, it’s still very nice to see an app like A Master of Go being developed and updated as frequently as it has been.

Please don’t mind my following suggestions even though I have not purchased the app.

While I am not sure how Apple’s Apps Store handles searches, it may be easier for people to find the app if you include terms such as the Japanese, Korean and Chinese names of the game of Go as well as their English equivalents such as igo, baduk, and weiqi in the app description.

Can the app function in landscape instead of portrait orientation? If not, it would be nice to have this function because not a lot of iPad covers or stands support portrait orientation.

For some reason, based on the preview screenshots, I find the aesthetics of the app may be able to be improved. I mean it is already using slate and shell stone, grained board and tatami images, but maybe you could try putting together similar images with different colour temperature/balance? Perhaps the variety of colours like the purple buttons and the black background chart at the top could have a more coherent colour scheme with the board and stones? Is there a way to hide some of the buttons and chart during a game or review?

I wonder if having Go stones and/or board in the app icon would make it more relatable to the game of Go.

In any case, it’s really nice to see your efforts in this app. Please keep it up!

pragmaticleas san,

Thank you for your interest in my app!

Nice suggestion about English description. I put multi-byte character words only for localized descriptions.
I will put them in English description.

The app support landscape mode too though no special features are added.

I agree that the aesthetics is not good since I am not a designer^^;
The theme color is purple, which is the color of Tao, but no adjustments between it and images are applied.
I will think about it and the app icon in the next major version.

Thank you for your advices!

Yuji, could you please explain the use of "coins" in the app? I generally avoid buying apps that require an in-app purchase. The changelog for your app says that the coins are used for "scoring", because it is a server-side function. How important is "scoring" to the effective use of this app? Would I need or want this scoring function in order to study games?

trgo san,

Thank you for your interest in the app!

Basic use case of scoring is to know how much stones you “lost” at the of end of game with AI.
(When you won, it usually means that AI resigned before end game.)
Scoring is only available in study mode.
Since you enter study mode right after the end of game, you can check the score immediately.

0% of winrate does not mean a big defeat, so some people care the score.

If you do not care, you do not need to pay any extra money.

I hope that this comment will be helpful for you.

Thank you.

Thank you for your prompt reply, Yuji-san. It sounds like I won't need the coins.

If anyone has a chance to test it with the new iPhone, I'm curious how well the app performs on it. I assume somewhat better than on the last edition, but have no idea whether it'll be a big difference.

_________________
Occupy Babel!

just tested on a brand new Iphone 11 pro Max. Must say it was terrific for the A13 chips are very strong. I use the Elf engine and also the Elf V1 weight, average nps around 150-200/s and eps around 130-150/s. I supposed it could do better but need some time to adjust. I also have a problem what is meaning of "Ladder thinking expending max moves", what is the proper number setting for this? Another problem is the "batch size number", there are 1-8 options, default setting is 1, should I set to the max?

elliotdong san,

Thank you for using my app!

About "Max. times of time-extensions for ladders",
when a primary variation of a move which the AI selects ends with some Atari position, the AI extends reading.
The AI needs at least a few hundreds playouts to read simple ladders.
Since A13 is pretty fast, you do not need to care the setting if you give the AI more than 3 seconds per move.
This setting is for devices older than iPhone XS.

About batch size,
if you choose 8, maybe you will get 10% more eps(evaluations per second) on iOS 13 depending on weight you use.
(On iOS 12, I was sorry that there were no effects)
But honestly I am not sure it is effective for strength or not.
batch( = parallelism) may loose accuracies of playouts even if it increases playout speed.

I hope that the information will helpful for you.

Thanks for the reply, Mr. Y_ich. I still had a question that I tried many combinations of the Engines and the weights. Seemed the ElfOpenGo engine and Elf V1/V2 weights were the slowest combination of the nps and eps playouts. what I mean is that if you try the Leela master or leela 15b weights, both the nps and eps playouts will be more faster. Is it that means the Elf weights are stronger than the others? or as well as the Elf Engine is stronger too. I Just try to find an appropriate combination for the A13 chips. Thanks again.

elliotdong san,

You are right, you will get more playouts by 15b weights than by ELFs.
But it does not mean that they are stronger at the condition of time parity.

It seems that ELFs are stronger than 15b LeelaMaster but LeelaMaster plays better than ELFs for handicapped games.
And it seems that Leela Zero #242 may be a little stronger than ELFs.

All these weights are super human, so I recommend you to find your favorite one by seeing their styles rather than strenghs.

Tired of borrowing my wife's iPad mini (most recent version), I got a new iPhone 11 Pro.

Running side-by-side with her iPad it seems to be at least 20% faster on the leelaMaster gx89 version, less so on LeelaZero 242.

I’m happy enough. Will explore more.

_________________
- Brady
Want to see videos of low-dan mistakes and what to learn from them? Brady's Blunders

Hi y_ich,
1) On my iPhone 11 pro, running iOS 13.1.3, increasing batch size seems to make it a little faster, but so far, it also finds difficult-to-find better moves much quicker. I'm currently using batch size of 5.

2) Are the settings defined anywhere? What do all the options do? For example, what is low thread priority, resign threshold, Cinit, softmax temperature, and virtual loss? Can I assume that networkLeelaZero_242 the official 40-block network 242 converted to your format?

3) I would like to help you update the networks, but I can't follow the directions on your GitHub page: https://github.com/new3Rs/a_master_of_g ... /README.md

Just for starters, the sample dropbox links provided tell me I don't have access, so I can't see your examples.

I'm not a programmer. However, I am able to follow step-by-step instructions, and was able to compile / install lizzie / LZ / Katago. So if you can either help me directly, or clarify the readme, I'd be happy to build the json / converted weight files, to make available the most current 15b, 20b, and 40b Leela Zero weights. I'm using macOS.

If you'd rather leave it to a gifted programmer, or if one is reading this and wants to help, that is fine with me.

Current strongest models:

LZ15b_245
http://zero.sjeng.org/networks/0d2694c5 ... 7e9c9c3.gz

LZ20b_248
http://zero.sjeng.org/networks/9d0457b1 ... 6da8751.gz

LZ40b_249
https://zero.sjeng.org/networks/6ee288b ... 6cf0538.gz

Thanks.

_________________
- Brady
Want to see videos of low-dan mistakes and what to learn from them? Brady's Blunders

wineandgolover san,

Thank you for keeping using my app!

About No.2, I amd sorry for lack of documentation.
If you set "low thread priority" on, you will reduce speed of playouts and get longer battery duration.
"resign threshold" is the winrate at which AI resigns.
"Cinit" determines wideness of search tree. The larger Cinit is, the wider tree become.
"softmax temperature" determines broadness of policy(next move intuition). The smaller it is, the narrower candicate moves become.
"virtual loss" adjusts how much the search avoids same variations for parallelism. the larger it is, the better AI avoids. It does not mean the stronger though.

You can leave these parameters alone, or select one among "instant parameter settings"

And yes, LeelaZero_242 was converted from the official #242 weight.

About No.3,
Could you give me a log with error?
I may help you or fix the problem if it is a bug.

You seem to want to get the very strongest AI at present.
Good, me too^^

But I want to point out that the difference of Elo rating between #249 and #242 is only 155 points, which is about 70% winrate.
(It sounds big but do you really want to choose best sparring partner among　Tyson, Foreman, Ali, et al.? I am kidding.)
I will update the weight when the difference of rating become 200 point bigger.

Thanks.

Grünauer san,

I have succeeded to run KataGo on iOS 13 though I am not sure it supports handicap stones and variable komi well.
Do you have good experiences of KataGo on PC?

Still I need time to embed it into my app.

Please look forward.

One excuse. You may need extra money for KataGo since it works only on iOS 13.
(Otherwise, iOS 11/12 users will have to pay for a function they cannot use...)

So you mean that KataGo works well for variable komi and better than Leela Zero with handicap stones.
Amazing.

Please understanding that posting about my flailing is a bit embarrassing.
Regarding not being able to see the json examples from your dropbox account, this is the error I get when I try to access "https://dl.dropboxusercontent.com/s/4ue9d86yaglengl/LeelaMaster_GX89_fp16.json" :


Furthermore, I assume I have to install the converter onto my computer, just as I did LZ and Lizzie, right? I assume that is how I will run this command:



Below you can see the two commands I ran to try to do so (I also tried both commands with ".git" on the end.) I'm sure I made a simple and stupid mistake.



This is why I asked for step-by-step instructions on how to convert networks, where the steps include installing the appropriate stuff from GitHub.



My interest lies less in sparring with a better partner, and more with optimizing for the hardware I have.

I understand and agree with not updating too frequently, and it makes sense for the 40b because it's pretty recent. But given the new A13 processor, I think it is worthy to try a 20 block network, which I suspect will still be pretty fast. And once I can do that successfully, perhaps updating the 15 block is also worthwhile, since the old one is based on 226, six months old.

I'd be happy to do this myself if I can, and let you focus on more important things, like say, the KataGo conversion. Alas, I am stuck.

Thanks again for your software, and for putting up with me.

_________________
- Brady
Want to see videos of low-dan mistakes and what to learn from them? Brady's Blunders

wineandgolover san,

I am sorry for my poor description in README.md.

1. JSON in README.md is an example.
You should replace "https://dl.dropboxusercontent.com/s/4ue9d86yaglengl/LeelaMaster_GX89_fp16.json" to the URL for your own model which you will convert according to the instruction in README.md.
By the way, this means that you need your web server to put your model like Dropbox in the example.

2. Your understanding is right for make.sh, but you do not need to add "-b next" option when git clone.


I am developing these scripts on macOS and I am not sure that they work well on other platforms.
Please let me know your platform.

3. optimization
I am sorry that I was kidding.
You fun is my fun.

(Finding optimized hyper parameters(Cinit, softmax temperature, virtual loss, etc.) sounds very difficult.
AlphaZeo, Leela Zero, and ELF OpenGo projects studied it, experimented many games, and optimized their parameters. Then they got different conclusions.)

About 20b block networks, win‐loss records show that ELFv2 is still strongest, it is stronger than the latest 20b(15b) weight which learned 40b self-games.
But ELFv2 is slower than ELFv1, I think ELFv1 is the best 20b for "A Master of Go", so I keep it preinstalled.

Anyway, you can try conversions and enjoy to watch their games.
Please feel free to tell me your troubles in conversion.

Grünauer san,

I am sorry that unlocking KataGo is not available yet due to my mistake of procedure.
Please wait for a few days.

About coins, I have no idea about accuracy of KataGo's score estimation yet.
I have been using GnuGo estimation for a long time, at least it is quite good at grading.
So it is still there. You do not need to use them.
I hope that I may add whole game analysis on server some day. Coins will be used for it.

Thank you for your patience.

I am very excited that katago is coming to Master of Go.

Y_ich, I hope you are charging a single, up-front fee for this upgrade, rather than having ongoing fees. I think you should be rewarded for your efforts, and I’m happy to pay, but I personally feel disincentivized by apps that keep requesting money to utilize standard functions. For katago, the functions I’d expect to use the most are its built-in score estimation, variable komi, handicap capabilities, and ability to play well on various board sizes.

Out of interest, in your testing, is katago running pretty fast?

Thanks!

_________________
- Brady
Want to see videos of low-dan mistakes and what to learn from them? Brady's Blunders

wineandgolover san,

“Unlocking KataGo” is non-consumable. Once you buy it, you can use it forever with no charge.

KataGo on the app is not as fast as other weights.
I do not succeed to let it use Neural Engine yet.
Its performance is about 19 eps on the latest iPad Pro.
And currently the KataGo engine supports only 19x19.
I will extend to 9x9 and 13x13 later.

Now both the version 4.0.1 and KataGo item are available.

Enjoy!