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OK.

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So welcome to 8.7 where we start training at ossify.

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So let's switch into Python lookbook now and get started actually in doing some training.

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OK.

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So welcome back to Python the book where we're going to start training our model.

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So let's go through this line by line.

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All right.

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So remember sites which is basically how many images we process in one batch.

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Effectively this depends on how much RAM of RAM you have available because this is a small data set.

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You can probably even get away with going up to 128.

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However let's put it at 32 to be safe in case you have little specs on your system.

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Epoxy usually we can use ten or twenty five even but for this demo let's just use one.

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So you can actually see it around and actually see what happens at the end.

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So now let's talk about model that fit and you have something called History equal model not fit.

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Now ignore history for a bit for a second.

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The key part here is model that fit.

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You don't actually need this history stuff here.

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This history is mainly needed to plot or graphs afterward.

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But for now you can you can create a model without doing this.

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You can simply run this line and it trains.

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So let's look at what inputs we need into model a lot of that we need to training data we need the training

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labels in the format we specified above.

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We need to specify OBOT size which is to to POCs which would be one vobis.

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Basically that just tells us how much information we want to see.

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While training I recommend Vivus one because it's nice to look at and provide more information and less

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lead validation data which would be extra us in the White us.

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And this has also appeared in a tuple as you can see just brackets basically make sure it's in shorts

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a tuple and that's what we fit a model on.

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And lastly let's take a look at this line.

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This is where we get the evaluation metrics at end of our training evaluate basically just gives us

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a score at the end at law school and accuracy accuracy score.

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So let's now run this and visualize how training is done.

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Amazing.

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So now I mean I find this quite interesting to watch but you may be bored.

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But right now this is basically going to add images here how many images we have seen so far in this

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data set the estimated time to completion two in two and a half minutes left a loss.

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And you can see it continuously going down slowly as she is pretty fast.

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To be fair and accuracy This is a part of like to watch too as well.

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Oh accuracy is slowly going up the more and more they do we fito model.

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And this is on one epoch and we're really at 60 percent accuracy.

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So if you wait to sell it I'm going to Fasold a video while we watch this.

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You can actually see after one book we actually reach quite good politician and training accuracy.

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However you don't see the validation accuracy and validation loss just yet because right now what's

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happening is that we're passing a data set of a training data set to a model and back propagating it

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and updating the weights.

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And that is how these things are improving.

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Losses going down.

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Accuracy going up and once one epoch is complete then we passed a test data into it and then we see

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a test of validation loss or Test loss and validation accuracy or test accuracy.

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So let's wait until it's done.

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OK good.

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There we go.

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We're finished.

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So now let's look at the results here and try to figure out how we analyze what had just happened.

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So as you can see after we fed all 60 images 60000 images into a model and train that updated awaits.

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We now have a loss that is decently low point five nine Well accuracy on our training data set.

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That's a point 8 1 4 5 8 1 and 1/2 percent roughly.

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And but interestingly our validation loss is even much lower than what treating loss and validation

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accuracy is 93 percent.

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Now that's not normal but it's a good problem to have.

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It means that our model has generalized very well and is actually quite accurate on our test data.

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Now this doesn't always happen usually always on most cases have higher accuracy and training then when

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the validation set.

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However let's wouldn't complain about this.

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This is a good problem to have.

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And basically we have a loss and test accuracy printed at the summary here.

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It's also the same values here but we just run this again just to basically see it.

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And if you have many ebox as well this is a summary at the end of the mall Okay.

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So now we're going to move on to plotting or loss accuracy charts.

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Basically what you should do is run this for maybe 10 epoxy can actually have nice graphs.