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Hi and welcome to Chapter 15 point to where we're going to build a monkey bridge justifier and basically

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use the concept of transfer learning to get very high accuracy very quickly.

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So let's take a look at this dataset.

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This is where it was taken a stick and from a Kaggle project and basically it has about 80 images I

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think of about 10 different types of monkeys each.

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Is it a species of monkeys here.

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And actually not 80 18:00 into the 152 images of each class.

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And these are some sample images here and you'll notice that some are quite small and differently.

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Different aspect ratios images of various sizes and quality as well.

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So it's pretty much like what you might build as your own data sets effectively.

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It's not well standardized not super neat not super high quality images just random images taken from

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the Internet.

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So now let's move onto Pitre notebook and begin creating this toxify OK.

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So before we begin I hope you downloaded your resource file monkey Breede datasets and have placed it

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inside of the territory here.

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This is the translating directory so you have monkey abused monkey read our victory here with our training

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images.

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And each one is in full to here.

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And let's go back and now hopefully that's set up correctly for you.

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So now we can go back so we went back to full and open up here.

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I already have it open right now so I'm going to go through this step by step so you understand exactly

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how we can apply transfer linning.

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All right.

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So we're doing this with more on that.

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And the reason I have to move on that for is because it actually trains quite quickly on C-p use.

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So let's import ballots here.

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And then let's define image rows and columns so we're going to use uniform square images of 2:24 by

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2:24 in size.

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And this is how we basically define that.

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When we loaded in we wanted to we had to be Image nets.

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We've seen this before in our pretreated Model S..

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However we haven't seen these parameters here.

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I will quickly discuss this with you what we're going to do is that we're going to include the top and

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said this at Falls.

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What this means is that the fully connectedly as the last layers on the top of the model are basically

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not included in the model.

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So I'm going to show you what it looks pretty soon and in what shape is of center thing.

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We just defined in what shape of this model to be this as why we define these parameters up here and

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tree means color depth of tree RGV.

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So this is a cool thing we can do with terrorist models upload.

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So we have a model here called Mobile that.

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So by addressing the layers within that Dudley as districting an array.

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And we can basically loop through these areas here and actually turn it off manually.

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The treatable parameter a flag that controls what it is should be trainable or not.

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So what we do in this two lines of code here is that we basically setting all the layers in Mobile and

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that's to be non-tradable basically fixed.

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This is how we freeze DeWitt's right here.

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So now we could actually print these layers here.

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And basically what we are printing is the name of Leo number.

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I go to the loop and we're going to print the flag Liautaud trainable what it's treatable.

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True or false.

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So you get to see all the layers now which is quite a bit a mobile that are set to false.

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So this is pretty awesome already.

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So I hope you're following simple code so far.

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So now we're going to do is we're going to create a simple function here that basically adds to fully

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connected head back onto the model we loaded here because remember we loaded it.

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But we didn't get to the top.

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So now we have a model without any top.

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So no actually I want to show you something quickly.

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What if we said this to true.

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All right.

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How would this model look.

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So we saw we had 86 differently as the last one being removed.

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So let's now print this and see what it looks

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takes about five to 10 seconds to run.

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There we go.

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Oh good.

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So before we head up to 86 now we see we have basically this is the top fully connected head.

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This is what we left out before previously.

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So now let's put it back in.

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OK.

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Because what we're going to do we're going to add a head here.

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These are really as we we are going to add onto the model now.

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So how do we use this function.

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This function takes a number of classes.

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I do our data sets.

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We specify how many classes we want.

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So for a monkey breed they is that it's going to be 10 and the bottom bottom model is basically this

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model here.

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Well not at all it's for us and it's.

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So let's quickly see what this function does.

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It takes a lot of muddled model here.

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Guess's gets the output part of it here and we create basically the top model now.

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So what we do now we have to find a top model like this here and now the top model we just simply basically

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add these layers here.

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It's a different way of Ardingly as no one cares.

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So we added an adjusted to the top model here.

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So for us we do global pooling Tuti we do a densely with a thousand and 28 nodes that again another

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densely a here and then we do a final densely with soft Macksville attend classes we want.

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And then what this does retune the model Top Model back.

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OK so now what we do below is obviously we just load all Olias of need and defined number of classes

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but now we can actually use our function here where we actually enter a number of classes.

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We enter the mobile in that model that we created we loaded before and we add a top.

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That's actually a we defined here to this model and that's why we call it the C.

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And what we do know is that we use this cross model function so we use it now to get inputs here which

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are defined as a mobile at model output speed the other possible are we going to train.

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And basically this combines it into one model now one model where it looks like this when print printed

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out.

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So a lot of layers I just saw before 86 Lia's But now we have four sort six Malis is now these are three

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to find here.

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And that's going to show up right here.

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So this is pretty cool.

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And look at this here.

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So we have five million parameters five point equally actually and trainable parameters.

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Only 2.6.

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And the non-tradable parameters which are the width of we froze our trillion.

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So effectively we've taken a model of at MIT How was pretty complex not super complex like a Viji and

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couple of others but complex enough and we've made it into a much simpler model to train.

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So let's get to training of monkey breed.

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They just had no training on Lockerby to classify.

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So we loaded data sets using imaged digit data generators that you've seen before.

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We do a standard thing here which you of which you should be pretty familiar with by now and then we

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define some checkpoints and Colback sorry.

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So we use stopping and checkpointing here and then we train for only five bucks for now.

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That's because we don't want it to be like take too long.

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And actually treating it separately in this window here.

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So I've actually already trained almost five e-books and realize so much time.

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So look at this here you can see after this epoch which took just under five minutes our validation

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accuracy was 88 percent already.

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That is actually pretty damn good for such a short space of time.

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Now and a second night duration because it's such a early start to the trading hour.

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Even though the trading loss is much lower the and accuracy is a little bit less 84 percent.

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That's OK.

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We can sort of live it out.

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We'll let a train from what ebox and see how it evolves because training these pre-treat models when

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it's something which is frozen is a little bit different than how we turn to CNN's they basically do

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they do effectively converge and get a very high value.

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However you do sometimes see some odd fluctuations like this.

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And look we have it back up to 91 percent 90 percent.

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If you wait a few minutes sorry about 20 seconds at least here we can actually see what evaluation accuracy

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is at the end of the fifth book.

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So let's wait and see what it looks.

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One thing to note is that you can actually see our callback stopping callback actually telling us how

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validation loss did not improve did not improve if we left this for 20 bucks and we had actually it

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was here as well so basically no matter what this is going to be the last epoch because I'm pretty sure

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I said my patience to Tree Hill.

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Yep.

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I usually always do.

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So right now what it's doing had a reason why I stuck it two seconds even did two seconds would have

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passed by the time I started the sentence is that it's predicting on treating all validation data set.

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Now that's something that a lot of beginners don't know.

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Take the seat pause at the end of it like a note stuck.

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It isn't actually stuck.

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It's just waiting to run on the validation data set now.

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So it takes a little while to honestly because sometimes validation data sets are quite big.

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Ah there we go.

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So look at this.

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We got 93 percent accuracy in such a short space of time.

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So this is quite good.

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So no it's actually go back to this main page here.

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Let's look at our model which takes me about 10 seconds.

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And what are we going to do once this model is loaded We're going to basically use open C-v because

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messy.

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But of course I wrote quickly that loads the images here and it runs into predictive that we just loaded

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here and so on already and we're actually going to see the monkey class see how accurate a real ossify

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really is 90 percent accurate.

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So let's find out.

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There we go.

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So this is the truth.

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US battled.

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Yes that's like a Japanese monkey.

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OK so fiercely it got this one wrong.

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This is what Elmo predicted Whitehead had a cabbage in and no it was not a white hat.

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Let's see if he gets it right.

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Yeah it did.

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Got this one right.

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Pick me.

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I'm almost at.

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Let's see what the other is.

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Gary langar definitely.

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Right.

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Pygmy marmosets again.

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Got it right.

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Got it right.

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Got that right.

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Got it right.

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Got it right again.

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Got it right.

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So seems pretty good.

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So aside from the first one model got basically nine out of 10 right.

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Which kind of corresponds to 90 percent accuracy.

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We got here.

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So you've just learnt to create a model a basically a train model using transfer learning and you see

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how simple it is.

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You just basically linnets load it with the weight speed frozen and the top being not included.

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Then you build the function to add the top whatever top you want to add.

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I didn't hear all these make sure the Lasley is number of classes you have in your dataset.

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Then you basically compare concatenates and compile the bottles here.

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Well combinable I should say you do it your image under it.

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It does define your check points and callbacks compile and we go and train.

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So it's really very simple and I hope you find a disruptive quite useful.

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Thank you.