Name: Running with Trained Model - Deep Learning in Halite AI competition p.6
Uploaded: 2020-03-31T18:17:58.000Z
Duration: 23 min 1 s
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Adverbs of degree

Everybody welcome to part six of the M l and Deep Learning with the highlight three challenge tutorials in this video, we're just gonna continue building on the last one, which I believe I left off fixing a slight bug that we weren't able to train for multiple E pox.

Adverbs of manner

So let me quickly bring up tensor board tensor board.

Lauder Equal lips blogger equals longs doing it.

And for some reason I get a bunch of this.

I think it's because we're really we're kind of overriding the steps with my crappy way of doing things, but we'll get through it.

So then, um, I found that the best way to get there is with local host Colin 6006.

If I use like 1 27 001 that doesn't work.

And if I use the machine name Colon 6006 that doesn't work either.

Uh, the orange and blue is just one iPAQ.

I'm gonna switch these two relative so you can kind of see those compared, Um, but really, after three A pox, Really?

I mean, there's a mean, maybe it's slightly comes up, or at least it levels out in the in sample.

But then at a sample, not too too impressive.

I have to switch it to relative to show you the iPAQ Val ac the again because of the way they were doing things.

So again, the orders line is actually only one iPAQ.

The red line is three pox in the blue line is one he parked.

The only difference is the Orange line was the time when we actually had to load the data and prepare it.

So, as you can see preparing the data, it takes, like, three times as long.

So anyway, accuracy somewhere between 24 a half and 25%.

That's pretty good, considering, you know, random choice would be 20%.

And in this case, you know, we've trained a model off, you know, using a threshold.

Sure, but we're training off of models that are moving randomly.

So the fact that we got 4 to 5% over random is actually pretty impressive.

That tells me our threshold probably worked, but now it's time to find out.

So now we begin the massively iterative process where we've trained a model.

Now we want to run a bunch of games with that model and some threshold.

And then the hope is that our average or mean kind of Hal I collected has gone up a little bit.

Any little bit will help, especially these earlier games.

So, uh, so what I'd like to do is, First of all, let's go ahead and just copy training data.

In fact, I figured this That'll take too long.

But I'd like to do let me just delete this.

We'll just call this training data and I'm gonna call it underscore one just for the first set and then we'll make a new folder.

And then we can run those stats that we did where we do the hist a gram, but also just like the mean on all that we can run some stats and see, Has the model actually improved at all?

We also could do the model versus the random and see which one wins more often or something like that.

But anyways, what we shall do now is implement the aye aye rather than random incentive on.

So I'm gonna copy Paste Center by and now that's just sent Abi.

And then it'll be Dash, not an ill will be.

And I am that I just close everything else.

So we have to make a few imports also because of the way Like how light uses standard aRer outputs and stuff for the, you know, the game and python to talk to each other.

So we have to make sure we silence all that stuff.

So I'm gonna make a few imports here in Port Au s, Simon import cysts.

Well, it's a standard error equals sister s t d air.

And they were, say, at sister S t d air equals open.

Oh s dot definable with the intention to write.

And then we're going to import tensor flow as t f.

Oh, it's cause you hit enter for the new line.

I don't know how you get around that actually.

Anyway, system standard error equals standard air.

So the issue is when you first import tensorflow in, it's gonna tell you things.

You're not the back end that you're using.

Butt's gonna tell you things about, like, that's what caress it's gonna tell you things about, like, your GPU and stuff like that.

Basically, to silence all that output if you have it, if the game is gonna air out.

So now Ah, Santa ma m l can remain the name.

Uh, next thing we need to do is continue to silence any of the tensorflow outputs.

And I thought about just copying and pasting X.

This is just kind of cookie cutter code that was used last year to silence stuff from Hal I TTE Anyway, os dot environ, uh, and then what we want to say in here is T f c p p men long level, and we're gonna set that equal to three and string.

And then now we want to set the GPU options that's gonna be equal to t f dot All caps GPU capital.

Oh, options on this will be per process GPU memory fraction and we set that to 0.5 This is a small model.

Well, by default, the TENSORFLOW is gonna allocate as much as it can and then keep stuff just floating in memory to attempt to make things as fast as possible for you.

The problem is, this is going to exhaust a lot of power Now.

The next thing is, I'd like to run three or four simultaneously simultaneous instances.

So you have to keep in mind that at any given time, upto like, say, 16 a eyes could be trying to run and make predictions at any given time.

So it's really important that we set this number very low now, depending on how many you can run at any given time.

Given your certain CPU and all that, you can adjust this fraction, but I've actually found it runs perfectly fine and actually it seems to run faster on a low GPU memory for action.

And I'm unsure why, but it seems much faster, like 10 times faster.

Anyway, I think that must be some sort of bug or something.

That is very unintended, but definitely something I would look into in the future with a larger model.

Even so anyways, now we're gonna set our session cess equals T f session.

And then we're doing this so that when we loaded our model, it's gonna follow this session for us and use our Jeep you options that we set.

So config equals t f dot com vig proto and then GPU underscore Options equals GPU Underscore options.

Now we're ready to load in our model t f equals t f dot care.

I forgot the name and the other thing we want is random.

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Running with Trained Model - Deep Learning in Halite AI competition p.6

stuff

process

basically

improve