Name: Training Model - Halite II 2017 Artificial Intelligence Competition p.6
Uploaded: 2020-03-31T18:17:20.000Z
Duration: 18 min 52 s
Description: Thousands of YouTube videos with English-Chinese subtitles! Now you can learn to understand native speakers, expand your vocabulary, and improve your pronunciation...

The imperative

And welcome to part three of our deep learning.

And how light in part six of our overall highlight tutorial, Siri's in this tutorial remaining is training a model on the training data.

I'm I'm just gonna keep letting this run in the background.

I stopped and I was like, I Price should just keep it running.

Uh, while I write the training code so we'll just keep it going, so almost actually have 100 will definitely be over 100 by It's how we're done.

So with that, let's go ahead and get started.

What you're gonna need tohave here is gonna be both tensorflow and caress.

Um, you could just do Pip, install tensor flow and pip install cares in your command line, but tensorflow, you'd probably really want to have the GPU version, So it should be tensorflow dash gpu.

And in order to do that, it could be a tedious to install.

So if you need help installing the GPU version of Tensorflow on both Windows or Linux, go to the text based versions.

Detroit I've linked toe both options in that text based tutorial.

It'll be in the There's a link in the description to the text based tutorial.

So head there if you need any help, Um, I'm also using T Q t m.

I'm curious how much longer it might be, but by no means you do you need it?

I think I'm just gonna keep doing what I was doing before.

I think that I think it's just more useful.

We've got so much data, it's so much code to cover, uh, to do it this way.

So let me just copy this over so important Paris because we're gonna use it.

That's just for your multi layer perception on feed forward type model.

We want dense layers won't drop out which dropout activation at that point also, if you want to learn more about deep learning in both python and just deep learning in general, I've also got links to tutorials there as well.

So anyway, uh, that's that load model so we can save in load models if we want, uh, random for shuffling our data.

Tiki gm for Like I said, it's for Iterating over models.

Like Progress Bar's basically for anything you're gonna operate over and then numb pie for obvious reasons.

So continuing along, um, we're gonna have a batch.

Size is just how much How much of a bachelor we're gonna feed through the neural network at a time.

I'm gonna go with 1 28 I found the 1 28 to be like the sweet spot.

Uh, we'll go with 10 summer between, like one and 10.

Um, I just kind of depends, wouldn't I haven't really found any benefit to going over 10 but you can if you want feel free to play with all this stuff and then test size.

How much of our data do we want to dedicate to add a sample testing?

Now, I'm gonna go ahead and do is create in bottling out model again.

This one's will take a little bit too, right.

Um, basically, this is gonna tell us what is our batch size and how many pox did we do?

We don't have an input model to start with, so I'm just passing 00 there.

But if you had one that you wanted to basically, you might you're gonna find that there's times where you train a model, and then maybe you get some more data or you get some new data and either you want to train at our larger data set or you want thio like you want to combine your new data with old data training couple pox there.

Or maybe you've got pure new data that you just kind of want to throw at it.

There's all sorts of reasons why you'd want to input a saved model, but anyway, Now we're gonna do is if we did have a model, we'll load it.

So I'm going to say load previous model will be false.

But if we want to load a model, then we'll load in that model.

I haven't actually covered terrace on my channel.

So if you're on my channel and you haven't used caress yet one really nice thing about Paris over tense or flow in general, a zealous TF learned, which is what I usually use in place of caress is you don't If you load a model, you don't have to define the model, which is really nice.

I always thought that was really dumb, that you had to define the model and then load it.

Um, if you save the model, it just seemed like, Why can't we just save the model?

Maybe that's something new with tensorflow like 1.4 or something like that that I just haven't experienced yet or something.

But at least Caris does that, and I think that's great.

So continuing along, um, now, let's go ahead and read in the data.

So again, I'm just gonna copy and paste this So here we're just opening the train file.

We split by new line, uh, and then we're gonna vow this stuff.

So one option we could have done rather than a pending to the file is like, save it as some sort of numb pie file or something like that.

But when we're just saving as this file, it's the format is a string.

I, um because it needs to be evaluated for what?

So this basically converts it toe actual data, not string.

Now we need to discuss balancing so with with all machine learning but deep learning too, Um, you wanna have balanced data?

We have attack minor own planets, mine and empty planet.

Chances are, aside from the initial part of the game, chances are the most common actual output vector is attack because most of the game you're gonna wind up with all the plants are occupied.

And so it's probably gonna be the case that, like, 75% of our data is attack, attack, attack, attack.

And the problem is those 22 major issues.

It's hard to assess how accurate you are if you have unbalanced data.

But it's also hard to train a model with unbalanced data because the model's gonna learn like, let's see, um, you know, I could be 75% accurate if I always say attack, because that's what the model is gonna try to do.

It's gonna try to figure out what's the quickest way to get to the best answer.

And so it's kind of just if 75% of your data, let's say his attack, it's going very quickly learn.

So we want to make sure our data is perfectly balanced for that reason.

But also, it comes time to evaluate our model and how accurate the model actually is.

If your data isn't balanced, it's really hard to determine.