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In this lecture, we are going to see the impact of bullying 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 leader.

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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 then put layer, then on layer, then pulling layer.

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Then we have to 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 day.

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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 like then layer.

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Then Dennis Layer.

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We are calling this model as model underscored 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 flat and layer, then two dense layer and one output layer.

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We are calling this model S 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 try this.

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Beacon also look at somebody to get an idea of how many parameters our model out optimizing.

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So here you can see for the first dance, let we have our on one point six million parameters to train

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if we compared with Model B values.

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You can see in what 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 all you can say that there are four times more trainable parameter in your model without pulling

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leer.

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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 more delay.

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Let's just combine both of these models and then we will run model eight four three box.

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Then we will run Model B for three books.

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

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Let's just first model A.

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Now, we have drained our water.

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And as you can see, for each boat, the execution time is on 31 seconds.

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And after the completion of Pertti book, 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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Then this.

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So now we have trained our second Märta lesbian.

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

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whereas for one model one, the execution time for each box 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 pulling layer as compared to a model with pulling layer.

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You can also look at the accuracy scored on training set, the accuracy of over model BS 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 pulling 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 pulling 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 pulling 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 pulling layer becomes much more important.

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