Name: 展開モデル-ハライトⅡ 2017年人工知能競技会 p.7 (Deploying Model - Halite II 2017 Artificial Intelligence Competition p.7)
Uploaded: 2021-01-14T12:27:54.000Z
Duration: 18 min 23 s
Description: VoiceTubeの動画で発音を聞きながら英語表現を覚えよう！学べる英語：

現在進行形

And welcome apart seven of the highlight to tutorial Siri's as well as part four of the Deep Learning in Hallinan Siri's.

So, uh, in the tutorial leading up to this point, we built a bought two randomly play against itself, basically taking random choices.

Uh, and then we took the winner and save that to training data.

So we trained in a I to, uh, basically play like the random winners.

And now we're ready to do is take our A I the actual bought the hell I bought and have it play as whatever the deep learning classifier says.

So, uh, in order to do that, let's go ahead.

I guess we'll just copy my by it was copied this one copy paste, and then I'm gonna leave this one called just straight my But mostly because, well, I'll be submitting it in this tutorial, so I just wanted it has to be called my body.

So, um, so we're gonna do is, uh we're going to open this and right away.

What I'm gonna I'm gonna do here is do this.

So one of the things that we will do is will compete, uh, locally and just make sure it works, and then we'll go so and upload it.

Okay, Sonny's fit this to screen real quick.

Okay, so there's only a few changes that we need to make and really, the crux of this entire A I is going to exist here.

So if the ship doesn't have a plan or we want to change our plans, the aye aye is going to take over this sector and that's it said Super simple.

We do need to make a few imports in such and such, but it's pretty simple.

We're gonna import tensor flow as TF and then from KERA stop models import lewd model.

And then what we want to go ahead and do is, uh, due to, um, so tense airflow and care us both output, uh, some stuff to your do some standard output and how, like uses that output to communicate.

And so what we need to dio and clearly three Houston like line number or output number or something.

So what we have to do is actually silence, silence the outputs of tensorflow.

So I'm just copies over, as always, links in the description for this code.

But basically, that's gonna do is gonna set that log level 2 99 so hopefully doesn't log anything.

And then also, uh, we're gonna set that verbosity to Onley report errors, so hopefully it won't tell us anything about what it's doing.

Now, what we want to do is load a model, so same model equals load underscore model.

And we're basically gonna load exactly what we had before, but at least for now, I'm just gonna copy this, So copy that lips of this back up here.

Then, um, also, I guess I'll just I'm just You don't have to have this bit of code, but I'll explain it anyway.

If, in fact, actually, this would probably be done basically here.

Not really sure which wouldn't should come first.

This is only gonna be used locally, though, so it probably doesn't matter Either way, I'm gonna put it here, though I think that makes most sense anyway, what this is gonna do.

The main issue here is we want to set the memory fraction.

So if you want to run locally, um and you would have liked to a eyes competing against each other.

This will say, each day I can use a 40% of your GP you is what I used to So I could train to a eyes competing against each other, uh, and not and then also still use my computer.

So basically, it was only using 80% But normally, if you didn't have this code here, um, the first a I is gonna basically load its model, and it's gonna attempt to use 100% of your jeep.

You It's gonna attempt to use all that it can, which is a problematic when you attempt to load a second model home to that GPU.

Okay, so now we're gonna go down to that pick a new plans.

So I'm gonna search pick a really new plans, Okay.

Now Um, yes, I'm just copy and paste from the text based version.

So what this is going to do is it's going around that input vector, basically, each item because it goes out to, like, the super high precision That's not necessary.

Um, and then the output vector is model that predicts output.

Max is just where that art max is because model that predict is gonna not produce.

But anyway, it's gonna produce something more like this.

Like I'm trying make sure I sort of him correct.

Okay, something like this is gonna be the output vector.

It's not gonna be a beautiful 010 or something like that.

Max will say, Okay, they aren't Max Victor.

And then we set the arcamax equal to one.

We logged the information and we save it.

Okay, but basically, the model is picking the output vector, and that's it.

So let's go ahead and run it real quick and see what we screwed up safely.

And now we get to go through the lovely, lovely, just super enjoyable debugging process of highlight.

So we got set session not defined so clearly.

Uh, we need to import something more from tens of flu.

I really just wanted to show you guys that as an example of what you needed to do to run it locally, But clearly, um, it's something we need.

I don't know if I could just from tensorflow import set session.

If not, um, from tense or flow Import Said session.

Run that really quickly and see if that saves us.

I wanna get the right right information for you.

So actually, it's from Kara Ross to do it for Caris, I guess, But anyway, from kera, stop back in tensorflow underscore Back end import set session and let me see if that works.

And then, um, because it's important to run it locally eventually.

If you want to continue that iterative process, Um, don't tell me I had another error.

I'm gonna get rid of that just because it might be the order, but so still another air.

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展開モデル-ハライトⅡ 2017年人工知能競技会 p.7 (Deploying Model - Halite II 2017 Artificial Intelligence Competition p.7)

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