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OK.

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So let's talk about how we save our models that we just trained.

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We spend some time training a model and we don't want to lose this model in clothes.

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I buy the book.

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So let's figure out how we save and load our models.

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OK so we just spend so much time printing our model.

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So let's get to saving it now.

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So if you scroll down below these charts to see if she's part of the model object you just specified

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directory and file him as you want.

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So basically we can just call this chapter 8 which is from 8 here eminence simple CNN Tennie box can

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actually put on display here make it look a little bit nicer.

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And voila it saved instantly.

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So now if you want to redo it on what all this is into and you found him here and we use Chris's model

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which is part of the models library here.

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And this basically this is what's strange to most people loading isn't as instantaneous as saving and

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that's because it has to actually unpack everything now and put it back into the model format.

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It's a bit tricky to understand I actually spend some time trying to figure it out because I had a project

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where I was loading 50 different models at once and it was taking maybe about five 10 minutes to live

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with all of them together.

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So let's just run this quickly.

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And there we go.

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Stick what 15 seconds.

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And so there's no snow or model which was called the model before.

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It's called classifier.

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And now we can use it here classify it just like we were using model before they are interchangeable

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now because our model is effectively classified.

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It's just a different name.

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What I mean by that is you see how we declared this as a model equals sequential No what we've related

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to our model.

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I didn't want to overwrite what we had see if it was a model.

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So effectively model is now as we have seen it and loaded it.

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It's now effectively Plus a classified called classify.

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Should say.

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And we can load it here and basically run some tests.

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This has an error in it.

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Him just to leave this quickly.

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And it should work fine now.

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There we go.

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So we're actually running some test animals here.

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So this is a zero a tree.

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It can see how accurate this model is.

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It's quite cool.

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And it actually looks and what it actually is because it's a bit tilted.

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So that's good.

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It's great.

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Actually we've just trained we've just loaded and trained and tested and plotted and analyzed and done

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all of the settings for this model.

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So if you wanted to put it all together because you don't actually need to have every block separate

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like this this was basically for teaching purposes I actually never separate these things that much

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but let's put it all together so quickly you can see this is everything here and you run this and you

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basically start training instantaneously.