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OK so you've just finished looking at your honest dataset you're a first model.

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So basically you've imported the data you've created your model you've trained it and if you've analyzed

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the results you've seen that you've loaded it and you've tested it on some really low.

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So now let's do the same for Safar 10.

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We're not going to break it down into all the steps together and we're actually going to show you know

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how we actually just do it all in one sequence of code.

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Similarly to what we did at the end of the chapter.

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But now a different data set.

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So let's get to it.

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But first let's talk a bit about c14.

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So foughten basically it's a huge image data set to get support 60000 images as well or maybe more.

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And basically it has 10 classes here Epley an automobile blade cat or dog frog horse chip truck.

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These are some sample images in it and we're going to try and classify that actually the techs what

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is seen in the image.

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So if you showed a picture of a car should notice a car a dog should know it's a dog.

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And for the other categories here.

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So let's get to it.

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OK.

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So opening our 8.1 one building a CNN image justification Safad 10 that's a violation on the books.

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The book full the here.

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Basically I just defined the categories again.

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And now we get straight to it.

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Let's just go through this quickly.

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We have all our imports here probably some that aren't even necessary.

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I tend to do that sometimes and I'm testing stuff.

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My boss gets irritated with me quite a bit for that.

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But fair enough.

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I still do it from time to time.

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We have a size.

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We have a number of classes we have.

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He parks it in right here which we imported from here as well.

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We just display our ships just to get a handle on what we're doing with our data.

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It's always a good idea to see just quantify how many data sets are called because your dataset.

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What's the shape what's the size of your test data set as well.

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So then we just do a quick formatting here because if turn is true dimensions we do actually have to

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add the mentioned on to it.

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It just comes up automatically here and then we just change it to float.

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We divide by 255 and we change the training to to test labels on the training labels too categorical

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or hot one in coding it.

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We define our model here which is basically this should be the same model as we did before.

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But I think I've added in yet I did it in two more convolution layers here also what's different about

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this model is that we have the two filters and the two filters here for the fisty convolutional is that

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we have the activation defined outside of it.

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I actually have done that on purpose because I remember when I was creating the stable model I actually

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wanted to shoot is a variety of ways to actually create these models.

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This isn't meant to confuse you.

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This is meant to actually show you that Karatz is very flexible and you will find a lot of example Cara's

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good where sometimes people find the activation as outside of the conflict here and some names is added

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in here you can easily have just gone put activation equal real you here as well.

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So going back to this we have to convert.

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So a second call here really Max puling dropout and we have two more convolutional is here each with

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64 filters and activations relo Max spooling and drop out here again and we flatten everything.

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And now we have a much larger dense layer here.

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This puts the specific next to 512 notes and then it goes to reload again.

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And then we have it connected here to this number of glasses which is 10 which we defined above and

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we use a different optimizer.

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Armis Propp and it's defined outside here we compile a model and print it.

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So let's print this and see how it looks.

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Very nice.

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And as you can see even though this model is more complicated.

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Number of promises doesn't increase that much which is good to know.

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It's always good to know to check a number of parameters in the model because the more premises they

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are usually the longer it takes to train.

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And you can train your model here and we just renamed it so I don't delete the previous models trend

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and basically run this.

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I'm going to run this now.

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There we go.

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Sick as you can see because the images are bigger even though this data set as you saw has only 50000

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I said 6:6 it doesn't before but does because I added the test and training.

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So we are training on 50000 images here and it's honestly not going that slow for CPE use a super training.

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We're going to do maybe about 10 epoxied I believe I said it at Ahwahnee book actually just for experimental

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purposes and you probably you're already seeing that.

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Basically an untrained classifier basically guessing would give you 10 percent accuracy one intentions

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and we're already close to 1 in 2 chance of 20 percent accuracy one in five chance of getting it right.

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When the training data sets you can see our model models slowly improving and we have a long way to

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go.

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We just want it back.

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So what leaves us I'll leave this view as an exercise.

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Once you two actually trained us for different monkeypox training maybe even change up some parameters

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here to start playing with us.

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It's quite fun playing with learning learning rates as well and can change it to the kids.

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Basically how much the leading grade decreases.

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I'll explain these concepts later on in others lives.

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But for now I just know that we have a variety of things we can tweak and deep learning is basically

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people say it's an art form and it kind of is because it's so many variations variations that are dependent

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on each other.

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You can simply change some layers here a number of layers change sizes change this and there's no hard

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science that defines what to do to get the best results.

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Mainly because it all depends on your dataset and your dataset is basically naturally occurring images

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which have so much naturally a naturally occurring variety in them.

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So it's once you can understand your data you can start figuring out how you should treat these parameters

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and I'll discuss these things later on in the slides as we build more and more complicated pacifies

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so left it could be to plot your charts here and you can run some tests as well.

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So for now I'm going to just stop this quickly

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what I'm going to do is I'm going to just load Hopefully all the imports I needed a little model train

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before and let's see if it works.

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Now it did not work because X isn't defined.

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So what we can do is just quickly run this block here because X to us and X train and all those things

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are here.

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And we can run this and this brings up this window.

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Here it is.

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So clearly this isn't a frog.

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This is probably a bit and then to it.

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This is not a dog.

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This is a dog.

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This is a horse not a cat.

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This is a frog.

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Yes automobile.

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Yes.

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Yes automobile.

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Not a frog.

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So we can see businesses 10 10 samples this model they trained here before which I probably trained

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maybe just 10 ebox has about 50 percent accuracy.

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So let's see if he can be debt accuracy on Safar has reached very very high like 99 percent.

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Let's see if you can get that.

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Get it out on your own.

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Good luck.