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Right.

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So we've just imported the data into carious However that is not in the ideal or the correct format

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that cares needs to train on.

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So this chapter is going to be a tiny chapter in how to get us data into the correct shape.

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So let's look at the next slide.

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So as I said before we've brought this in here and help with it it's here.

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All right.

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How ever terrorists requires it to be in a special shape and that she basically as number of rows columns

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a number of samples rows of columns and Dept.

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So this is how extreme it looks here but healthcare is actually once it is 16000 to 28 and 1.

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So we sort of have to add a fourth dimension antoh data and that's how we do it here.

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So now let's do this in Iraq.

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And the reason Chris is looking for a fourth dimension here is because when you load a greyscale image

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dataset it doesn't add the dimension of the depth to up.

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Mainly because it's a greyscale and is Nakul adept.

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If it was a color image dataset you would have the tree popping up at the end here.

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However we each need to add this one just to indicate to Cara's that it's a greyscale image data set.

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Otherwise it's going to return an error is an incorrect format so to change this you would just use

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non-place reship function which is quite simple to use.

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You just use extreme thought reshape.

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Take the first 60000 digits.

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That's when you just when you use shape.

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This is a first I mentioned at 0 addresses.

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Then you enter 28 28 or image or image columns and add 1 and it's quite simple to use.

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So let's get into it.

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I buy that book now.

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OK.

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So like we just saw this is a section of code where we reshape and add the fourth dimension onto these

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treating datasets.

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Untested sets.

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I didn't point this out to you before but we just get image rows and image columns by simply addressing

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the first mention of the shape here extreme.

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Now we use extreme zero and extreme in one we don't need to use Xorn we can use X test as well because

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they're the same dimensionality.

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However this just gives us the rows and columns so let's actually print this out so you can actually

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see what's going on here.

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So this run print that you'll see it turns 28.

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So this is getting to 28 here.

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And if we were to just use this for columns two you'll see will also get 28 right.

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So moving on this is fairly simple if you guys make it nice and neat.

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Again this is how we make the input shape.

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Now you've seen before we needed input shape dimension in the first layer of the convolutional on that.

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And this is how we just create our image shape here.

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It's a tuple That's combined.

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What rows columns and the dimensionality of the depth of the image.

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This would be tree.

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Again if it was a color image dataset and because Kurus expected it to be in a floated to.

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Right now I believe it's going to be in some sort of integer format here.

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So we just need to change this.

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So we just change extra in as type flow to the two and do it for X test as well.

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We don't need to do this for the labels just the turning of data.

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And this is how we normalized data we normalize all data by dividing it by 255 because remember images

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range from 0 to 255.

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So if we divide it all the image data by 255 we basically bring it into range 0 to 1.

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So that's quite simple here now.

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And basically we just print this out again if we need to do that because we did it before.

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But just do a sanity check on our data.

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So let's run this again.

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If you want to see what extreme It actually looks like now this is it.

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You can see basically the decimal points because it's going to hidden here.

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But all the data ranges from 0 to 256 0 to 1 right now.

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OK.

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So let's move on to heart encoding labels.