12. Optimizers, Learning Rates & Callbacks with Fruit Classification/4. Build a Fruit Classifier.srt

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Hi and welcome to 12. tree where we start building our fruit classifier and we start using some of these

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callbacks we learnt in the previous section.

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So let's talk a bit about fruit datasets called fruit 360.

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Basically it was it was part of a Kaggle competition.

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This is a link to the actual dataset here.

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It consists of 81 types of fruits.

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That's 81 classes and approximately 45 images per class and all images are rendered by 100 pixels and

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in color.

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So these are some examples of fruits here.

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Honestly I can't identify some of these myself.

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But you we're going to try and justify it to do just that.

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So let's go to I put it in the book.

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OK.

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So here we are at Chapter 12 the building of a fruit classifier.

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Let's bring up this file.

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But before we begin I hope you downloaded your fruit trees 60 datasets.

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And I wanted you to put that at File.

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Let's go to it here.

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Into this folder that it should have extracted into this for the here.

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And basically I want you to make sure that it's named train and validation.

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It should be as I've zipped it or compressed it correctly and you can take a look at the fruits here.

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So you can take a look at some mangoes of mangos all look quite similar to each other.

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OK.

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So let's go back to this here.

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So let's go back to the book and Firstly like we have done in our dogs with cats CNN we declare our

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doctors here and we just create some image data generators for training and validation and are trained

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and ready to and validation generates a notice with no categorical and binary.

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And also notice we have to declare a number of classes here as well.

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This dataset even though the data set is encoded into 100 by 100 pixels I'm going to use a resize as

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to the two Bertelli to basically make our training faster.

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We use a similar scene and never used for Safar here as well.

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And then we declare this is important but that's creates all callbacks So as you can see in the presentation

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this one is a checkpoint Colback and checkpointing basically ensures I received a best model after every

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box.

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If you train for 20 bucks and the best model is an e-book 16 it'll be that will be the one we save here.

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We need to specify.

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Finally I'm actually didn't mention that in our slides but it's much just the directory it's actually

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a file and we want to see it as otherwise it will not work.

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Ill be stopping here really stopping the problems as we have said here and it is something basically

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tells us when if this thing has stopped improving it'll stop letting the plateau which wasn't actually

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used in this example here.

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However we could have used it in another example it really happens.

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Unless you have training for exhaustive number of ebox but it's always good to have it just in case.

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So we create our callbacks here and actually did not add it in here.

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Quickly put it in.

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And basically what we do when we compile sorry not comparable with that model is that we point our callbacks

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to a callback array here.

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So as you can see you know we've trained for five hypoxia and basically you can see this up with this.

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Basically this tells us that a model was saved to hear after every epoch.

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And basically what happened is that after here it noted that valediction loss didn't improve didn't

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improve.

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If we are trained for more ebox which I probably should have left it for this example.

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But if we are trained for more POCs we would've initiated the stopping metric and this would have stopped

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training.

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So basically I could have said it to train at 10 epochs which I probably should have done away with

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it.

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I did find a box here.

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Sorry.

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And you can do it on your own.

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Set it to return the box and train it and you'll see it's going to stop after maybe six ebox.

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So let's take a look at the confusion metrics here.

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It's not printed correctly and I'll show you how we solve this too.

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And that's because we have 81 classes in this year.

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Our pacification report is well laid out.

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But it's a bit tedious to read don't get a ton of information unless you actually drill down and see

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something like oh pomegranates are basically bad.

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OK so here's another way to visualize the confusion matrix which was probably not best visualize at

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all like that.

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So here we go.

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Now there's a problem here with this we can actually increase the plot size here.

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Let's try 20 by 20.

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I'll probably take about maybe 10 seconds to run.

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Of Allision that did degenerate that isn't run.

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So let's quickly go back here and run this in the beginning.

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It's good that actually you actually see these areas and see how I solve it.

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OK.

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That's because when the book was saved would actually run.

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And let's run this this should work now.

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It's loading on model that's a model I see.

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Prior to training that one I think it was a fairly good model if I'm not mistaken.

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There we go.

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So this 20 20 did make a nice difference.

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We have a nice legend here as well.

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So you can see there's a nice diagonal in the middle here.

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So that is good.

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Now we can see little spots here and there.

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This one is here.

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I'm not sure what it is corresponds to.

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Let's see.

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Looks like kumquats.

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And it's been mixed up with and I think this is mandarin's entirely sure.

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But you can analyze this on your own and take a look and see what's being confused as what.

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So this is a nice visual representation of all confusion matrix here.

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OK.

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So let's test this here.

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Now I've created this open see the thing that actually brings up our fruits and tells us to predict

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value and what it actually was.

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So let's run this.

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Here we are.

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So predicted a passion for it actually was a passion for it.

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And that is not whole depression.

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I've seen look but fair enough.

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Brandied is a red banana and it got it correctly as a red banana tomato tree.

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I think that's type tree good here.

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Group.

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White or green.

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Looks like a light green to me.

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Avocado ripe.

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Good to know.

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That's right.

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Goldberry said actually getting everything right.

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This is quite quite good classify.

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And basically we can take a look at old classified results which probably should have mentioned before

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it is 3 percent.

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But is that the one we actually use.

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No.

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The one we actually would be with checkpointing which is very useful.

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The one we actually use probably was one of about 92 points of nine percent almost 90 percent after

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destry pox.

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In each Epopt of what just over five under five minutes to run.

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So this is quite good for treating such a complicated dataset using OCP.

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So let's just run it want one more time.

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Let's see if we get Shirleys get 1 in 10 wrong.

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It's a good one.

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We're all very good.

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So yes pomegranates are a problem.

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I'll be fair to be fair to stars that I can apply to me.

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However this part of it is something like an apple good.

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So everything else is correct.

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So that's it for basically using carrots is called Back future.

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We've seen checkpoints stopping and leaning read adjustments on too.

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That's it for the Shapter.