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‫In this lecture, we are going to see the impact of pooling layer on the number of parameters we have

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‫to train and the execution time on our CNN model.

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‫First, we will run the CNN model that we discussed in our last lecture with the polling layer.

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‫Then we will remove the polling layer from that model to see its impact on the execution time.

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‫So this is the architecture of the model that we developed in the last lecture.

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‫First we have the input layer, then cony layer, then pulling layer.

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‫Then we have two dense layer and then output layer.

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‫We are going to remove this pulling layer to notice its impact on the execution time

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‫So the code will remain the same.

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‫First we have the corn layer.

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‫Then pulling layer.

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‫Then platter layer.

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‫Then Dense Layer.

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‫We are calling this model as model underscore A

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‫This is same as the model we develop in our last lecture.

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‫And then we have a second model in which we are not taking this layer.

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‫So first we have the corn layer, then flaten layer, then two dense layer and one output layer.

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‫We are calling this model as model underscore B.

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‫And later on we will compare the performance of Model A versus performance of Model B.

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‫Let's just run this.

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‫We can also look at the summary to get an idea of how many parameters our model our optimizing.

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‫So here you can see for the first dense layer we have around one point six million parameters to train

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‫if we compare it with Model B values.

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‫You can see in our second model that we do not have any pulling layer, the number of cranial parameters

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‫is around six point five million.

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‫So overall you can say that there are four times more trainable parameter in your model without pulling

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‫layer

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‫And we know that the execution time is directly dependent on the number of parameters that we are going

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‫to train.

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‫So obviously we can expect that Model B will take a lot more time than model A

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‫Let's just combine both of these models and then we will run model A four three epox

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‫Then we will run Model B for three Epox

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‫And after that, we'll compare the execution time for both models.

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‫Let's just first run model A.

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‫Now, we have trained our model

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‫And as you can see, for each epox the execution time is around 31 seconds.

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‫And after the completion of third epox, we are getting the validation accuracy of 82 percent.

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‫And same as the accuracy for training data as well.

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‫Now, let's run the model without the pulling layer.

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‫Let's run this

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‫So now we have trained our second model as well

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‫And as you can see, the execution time for each epox is around sixty two to sixty three seconds,

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‫whereas for our model one, the execution time for each epox was around 30 to 31 seconds.

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‫So the execution time is almost double for our model B.

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‫That is the model without the polling layer as compared to a model with polling layer.

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‫You can also look at the accuracy score on training set, the accuracy of our model B is more and on

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‫the validation set.

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‫The accuracy is almost the same for both the models.

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‫This is because when we are polling for pixels and the one pixel, there is some information loss and

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‫that information loss is resulting in the lower accuracy.

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‫So if you are using polling layer, your execution time will be less and the accuracy will also be a

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‫little less as compared to a model without polling their.

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‫In this case, we have only used one convolutional layer, but in real life scenario, you may have

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‫to use multiple convolutional layer.

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‫And in such cases, a use of polling layer becomes much more important.

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‫So using polling layer, you can significantly reduce your execution time without impacting much accuracy.