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And welcome to chapter 8 point 1 0 where we're actually going to use curus to display a visual output

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or model.

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So remember before previously I was drawing all this nice visualization of our model.

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Well carrots can actually do something not quite as nice as this but it produces a pretty decent model

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visualization that helps you basically show people and explain your model.

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So let's see how we do it.

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Let's go back to what I thought in the book.

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OK.

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So now we're about to visualize our model so to visualize our model we need to import this library from

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Carousel dysfunction.

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It's called plot model and it's found in Cara's utilities thought visualization utilities vid's underscore

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utilities for short.

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So what we do we create or recreate or model first.

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We don't necessarily have to do this but it's good practice just in case we didn't do it before and

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it previously as in the previous cells and this I Pitre notebook.

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So let's go ahead and run the slime on this block and we get this table which is our same model output

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from before.

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All right.

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This in itself is a pretty decent visualization.

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However it's not like a visual diagram.

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What we're going to do is produce a visual Vaga now.

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So to do this we use a plot model function and a plot modeled function basically takes a model that

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we find here we take we enter a pot for the file to be saved.

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So we just use this path here which is the way our train models are saved and we give it a phylum model

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and scale plot PNB we can be more descriptive and give it like this model.

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All right.

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And then after we use matplotlib to actually show.

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So we just point matplotlib here that image directory of where we see that and we plotted here and it

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comes up below right here.

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So let's see how this works.

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Let's run this block and then we go here's a nice model visualization.

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What's cool about this is that we have inputs and outputs coming in and out is actually quite nice.

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This is basically a random number.

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Never have been able to figure out what this number actually is pretty much ignored for now.

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What school is that.

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We actually have Olias coming in here.

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We have all kinds of layers tool is here Max pooling.

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It shows input outputs what dropout does doesn't change a thing just drops out some layers.

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While training Flaten which we know what it does now are dense connections here or drop out again and

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are fully connectedly the end here.

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Which is also for the connectedly here.

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And basically this is it.

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So if you were to go to this directory here let's go to the planning directory.

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Let's go to train models.

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We can see here it is and it's clearer and sharper than I in the book being a DNG file and that's it.

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So we've just successfully saved.