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Welcome to the session in which we shall be focusing on data visualization.

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So we want to be able to generate visualizations such that we have images and their corresponding labels.

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Now we're going to make use of matplotlib for that.

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So let's go through our data set.

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So for I sample in enumerate um train data set take let's say we want to visualize 16 samples.

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The first 16 samples we will have uh make use of subplots from matplotlib subplot.

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And then it's going to be 4x4.

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Then we increment I and then we have plot image show to show the image.

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But here we have our sample.

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And then here we specify image.

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There we go.

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And then we have the title.

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Now the use of this title is simple.

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We want to be able to have the corresponding label.

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So we have um sample.

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And then now we have label.

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So let's run that and then see what we get.

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We get in subplot not defined.

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This should be actually plot.

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So we're making use of matplotlib for that.

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Now, with this next error, what we could do is simply print out our sample label and you'll find that

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we have this integer, um, tensor, which we get.

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Now what we could do is we could simply do numpy.

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NumPy.

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And then uh, once we obtain the one from there we would simply say um get label or get label string.

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So this takes in an integer.

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So here is going to be the label.

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And then we're going to return the corresponding string.

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So if so if our label is equal is equal zero we return um we return parasitized parasitized.

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And then else else we return on infected.

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So that's it.

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So um if it's zero then we have parasitized.

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If it is one then we have uninfected.

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Let's run that.

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And then now we will have get label get label string.

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And then we get that corresponding label.

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So now we have numpy.

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There we go.

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We close that up and then we run this.

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Looking at this here you could see the corresponding uh the different labels.

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So we should could now take this off.

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And here we have our corresponding plot.

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So you see we have this image uninfected uninfected uninfected.

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And you have the parasitized.

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So that's it.

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So we have our different images and their corresponding labels.