15. Transfer Learning Build a Flower & Monkey Breed Classifier/4. Build a Flower Classifier with VGG16 using Transfer Learning.srt

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Hi and welcome to Chapter 15 point tree where we're about to build a flow of classify and we're going

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to use transfer learning to do this.

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So let's take a look at how we actually.

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What is Flora ossify our flower data set.

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I should say so.

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It comes from the Oxford University's visual geometry group as called 17.

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And that's because there are 17 categories of flowers and their images in each class say the sets and

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not that much.

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So this is some sample images from the flow Josephite the flowers 17 there is that this is the web page

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from Oxford University.

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And this is the link you can go to if you want to download it from day itself or you can use that link

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I have on the left here on the demi site panel.

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Please use that link to actually download it because I've already preprocess the data into a format

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that is easily imported into Karris if you downloaded from Oxford University site you're going to have

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to do a pre-processing itself.

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And I don't think if you're a beginner you're not going to find that fun at all although it's a good

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exercise to do sometimes.

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So anyway our approach to this problem is that we're going to actually use a pre-trained Fiji A16 model

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with all of its way it's frozen except the top layer and we're only going to train the top ahead of

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the model with a final output of 17 classes.

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So let's go back to our I and notebook and get this done.

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OK.

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So welcome back to our virtual machine.

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I hope you downloaded the flowers dataset and extracted it to this folder here.

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That's this folder called transfer linning and financing and Plaisted right here so we can quickly just

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inspect it taking a look at some of those pictures.

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Let's put it on toenail view and it looks quite nice.

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So as you can see we don't have that many images in this data set.

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So let's see what kind of accuracy we can get without transfer learning on the Viji model.

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So let's go to it here.

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So no let me just close some of these windows open and let's quickly go back to this one here so you

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can actually see how I do it.

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It's 15.

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And we go to making a flower classifier.

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That's this file here.

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So now that we're in the file.

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Let's take a look at what's going on.

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So we import the BTG model that's easily done here.

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Viji was designed to work open 24 or 224 by 224 pixel image input's Isiah's.

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So let's keep the standard size and go forward.

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So let's load the model with out his weights or with the weights of image's nuts without the top layer.

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I should say so we do that.

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And let's just print out the layers in this model.

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OK.

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So as you can see default actually is loaded here and by default all the layers are trainable.

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True that means the default in of when you load EGD all the weights are trainable.

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So we now have to set this true to false.

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So that's what we do here.

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So we loaded with our top head with Image net weights and we set all the treatable as we said this flag

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to false.

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So let's do this quickly and that's done there.

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And now let's create the function where we add a fully connected head.

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This is where we delay as we add now back to the top of our Viji that network.

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Notice this is different to the layers we added in the mobile network and that's because PDG has a different

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design to mobile and that.

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So you're going to have to look at the final design BTG and replace easily as here.

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And this here this densely a number of densely as dense units here.

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By default we are going to use 256.

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However this function allows us to specify it in here we can add 128 and it would be 128 units here.

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So let's leave the default right.

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And then you said drop out who said these things we input a number of classes which is 17 from the flow

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was data set 17 17 Sivam should make sense you know.

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And we just concatenated models here.

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Well the parts of the model to get the full model and then printed out.

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So let's take a look at and we see there 14 million parameters.

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It's less than between 19 and 16 sorry BTD 19.

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And with treatable parameters only 135 tells him that's quite good.

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So let me just run this.

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So we have fresh and no we just do it data generators here to deflower validation and Floetry unfold

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as we said our size.

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We can go actually just keep it at 16.

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All right.

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And keep going here.

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So now we declare all callbacks right here and we just create we create a callback array which we pass

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in here and let's run this now.

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So I to leave you to run this over and run this already.

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And it takes quite some time.

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But what I want you to observe is look at the validation accuracy in 25 books.

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The highest we get was actually 95 percent which is quite good.

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So you keep going see did it ever pass 95 tree at one time.

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So this is quite good.

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So we've got 95 percent accuracy using transfer linning using Viji 16 in translating.

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So let's keep going let's see what else we can do.

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OK.

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So this section here.

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Can we speed this up.

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So let's try resizing the images to 64 by 64.

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You remember it was assigned to a can 224 224.

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Now let's do this to 64.

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So let's use this comment to setting the input size.

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Now to.

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All right and do the standard thing where we load with image that way it's we don't include the top

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specified in U shape and we make the last train with three syllables.

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So that's good.

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And now let's move on to this.

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Let us actually start treating the small so as we can see this model has a different input sites.

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And let's see what we get.

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So I've trained this before so you don't have to do it.

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So what I want you to see though is that what what's happened here previously before actually did not

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used the callbacks or that's it in view but I should have thought it and I.

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But what I've done now is a discipline we do.

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So we see some callbacks feedback from stopping so we see it's not increasing monitoring patients is

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good.

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So at the end Epopt 12 is what we use.

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So let's go back to Iraq 12 pastorate ago.

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That's this one 82 percent.

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So 82 percent was our best loess validation loss and our best accuracy.

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So you can see by resizing the images a 64 by 64 which is a substantial decrease in size 2 to 24 by

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224 we got it into possessory.

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How much was it again.

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Sorry.

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82 percent accuracy.

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So that's not too bad to be fair actually sorry 86 percent accuracy we got that was fifteen point five

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six five two.

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Right.

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So that is actually this one.

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So yep.

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So this is good.

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It's not great but is pretty good.