Now, let us see the importance of a pulling lever in our network.
In the last video, we created a network in which there was one convolutional layer and one pulling
layer in the model, somebody.
We saw that the number of total parameters that are to be trained is nearly one point six million.
Now, if I remove this pulling layer.
I'll comment on this part that there is a pulling layer.
Run this part also.
And now if I look at the somebody, you can see that by just removing one pulling leered, the number
of parameters have now reached six point five million.
Earlier, it was one point six million.
But after that, moving this max, pulling lere, the number of parameters is six point five million.
You can imagine the computational load this will have on our system.
If you want to go through with this, let us update model to model to every bit.
It's configured model to.
And reduce the number of epochs to really run it.
So here we will see the no time taken by a network to train for one epoch.
So when we had that pulling layer, the time taken was nearly 32 seconds but epoch.
So it took nearly 15 minutes.
Fifteen minutes to train for 30 bucks.
Now, I have run it for only three blocks because I knew the time taken for E.T. book will be very lunch.
Let us see when it completes one epoch.
There you go.
For each epoch.
Now it is going to take one minute so that computational time has doubled because we have to move this
pooling layer.
Now, imagine having a network with multiple convolutional layers because from basic features, we wanted
to extract higher level features also.
So that is why I told you that we have a set of convolutional layers which bring out higher level features
out of these input images.
Now, if you have to use multiple convolutional layers, you need to have pooling layers in-between.
So that deep computational load stays under control.
So if you add another convolutional layer without adding the max pooling layer, it will increase the
processing time to it even more than this one minute body block.
So this is just the time taken by our system.
It is also taking up resources of our system, like Haraam or Deepu.
So if you have a lot of congressional layers in your network and you do not add pooling layers, you
may reach a point where your network cannot train because of limitation on your processing power of
your CPA or GP.
So it is very important that we keep decompositional law under control by using Max Pully.
So that is all in this we do.
I just wanted to highlight that when we have a large convolutional neural network model, pulling layers
are used along with congressional leaders to keep decompositional load under check.