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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. | |