Name: チャールズ2.0は変更を超えて行く - パイソンはGTA Vを果たしている - 自走車 (Charles 2.0 going over changes - Python Plays GTA V - Self-driving Car)
Uploaded: 2021-01-14T12:27:59.000Z
Duration: 15 min 23 s
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And welcome to a video on the python plays G t A.

Siri's, uh, just want to go through some of the code aspects of the Charles 2.0, as well as address some of the frequently posted comments.

Actually ended up having to train this model twice The first time I forgot to save the model.

Anyway, Um, really, This one took 24 hours.

So, like just one whole day, and that's 24 hours per epoch.

And all I did was train this model for one epoch on.

I went that way because, at least with mobile net, I trained that for multiple pox and then went back and saw how each iPAQ did overtime in terms of validation, accuracy and after one iPAQ validation accuracy did only one thing, and that's go down.

So, with exception, I honestly have not run it beyond one iPAQ looking at validation, accuracy and validation loss especially, you know, like all validation losses done has gone up after, you know, I don't know.

So after that, like this looks like it's about to start coming down anyways, plus validation losses and continuing to drop.

At some point I may end up pushing this model a little further just to see, but I don't think it's gonna do any better.

You can kind of see how mobile Net compared.

Very, very close in terms of their performance, just a little different like exception dropped quite quickly with with a loss.

Why's this graph like all goofy, I guess, causes the validation laws.

But if I un smooth it anymore, you can't see these mean ones.

But you can see here that exception was just slightly better the whole time in all of the metrics that you can track so or at least all metrics that we tracked so anyways, exception, it waas.

So, uh, next thing I want to talk about is, um Well, one thing was for sure, the the image Pre processing, just divided by 2 55 So all of your image raise need to get divided by 2 55 That's really simple.

I don't think I need to show code for that.

Just understand that that was one of the changes that got made.

So I'm gonna pull the get hub fact useful, screen it and come to two files.

One is the exception model itself And the other one is this waiting distribution by class.

So coming over here, this is just the setup for the exception model, because all you really have to do is just imported from Caracas.

And then you set the, uh, the input shape for that first layer.

And obviously you don't, except there waits.

They do offer, like, pre trained weights that you can use transfer learning with.

I've got plenty of data that wasn't an issue, so I didn't need to do transfer learning, specify the shape again to 70 by 4 80 This is quite a big resolution If you want, you could take a picture.

Maybe even in in here, in the scroll down real quick.

On cyber dot io Uh, I have an image of a 4824 80 by 27 years ago.

And if we just kind of, like, zoom in a little bit I mean, I feel like at this resolution, you can see all the things that you really need to see in a model.

This might be a motorcycle, but it's so far off in the distance, I don't really care.

I can tell these air cars Traffic light is easily discernible.

Is that a bench or is that a car, you know, And so I think that's a pretty good resolution.

So we're gonna go ahead and just continue with that resolution.

Honestly, it's just use exception rather than inception v three.

The next thing is the waiting class distributor.

I'll come up with a better name at some point, but basically what it does is it distributes the weights, um, by red adjusts waits in order to effectively distribute classes evenly.

So in the prepared prepared data data set, Uh oh, this is actually I think this will be That's the training data.

But the actual validation data is something like, It's like 500 gigabytes worth of data.

Uh, like 500 megabytes worth of training did.

It's ah, few samples from each of this segments of things that we did.

So I created some training data, and other people had created some training did, and we kind of pull them all together.

I also created training did at different times.

At one point, I started using the speedometer to attempt to see if we could teach Charles like how fast he was going at a given time, but then also the way points weren't always there.

So this this model's been trained with waypoints without way points with a speedometer without his barometer.

And then in all the other scenarios, like at night in the daytime police, no police and so on storms, all that stuff.

So this model has training and all that, and that's really why this model works without the, you know, like the GPS and stuff like that.

I suspect that's why this model performs much, much better.

One of the things that a lot of people said is like, Hey, I see this model goes way too fast.

The whole point of the model is to drive erratically like I don't know about you, but when I play Grand Theft Auto, I drive pretty darn erratically, and I think most people do as well.

So the whole point of Charles is to drive like that, and some people are claiming he doesn't break.

Maybe you weren't here for the other models.

I just noticed this that can't be correct.

I think I would have to go in there and fix that.

Is that I wonder when that that has to have come in?

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チャールズ2.0は変更を超えて行く - パイソンはGTA Vを果たしている - 自走車 (Charles 2.0 going over changes - Python Plays GTA V - Self-driving Car)

stuff

basically

script

dude