Now let us look at another CNN architecture.
Google it was the winner of 2014 challenge Google and had one new concept and the concept was often
Inception module Inception Module looks something like this the input to the inception module is given
to four different layers three of these layers are convolution layers and the fourth one is a max puller.
If you look at these convolution layers these are also one by one kernel that is the window is of the
size of a single pixel.
Usually we have been using continuously layers with two by two or three by 2.0.
But in the inception module you can see that Windows or is one by one.
Used this oneness represents this trade so it has a straight of one the output of these two convolution
alert and this match pulling layer then went into three different congressional layers.
The output of these four was then put into a debt concatenated will not be discussing that concatenated
here.
This whole thing is called an inception module and the actual architecture of Google it was something
like this input of images went through here.
Then there was a convolution layer a max puller a local response normally to convolution layers and
so on.
All of these are staggered Inception modules.
So this is the inception layer.
This is an separate layer so many of these Inception layers.
The output of these goes on to another set of Inception layers and then we finally have a fully connected
neural network
so if we look at it it is a very complex network although it had very few training parameters for mostly
working professionals or students working in the field of data science and machine learning.
Creating such architectures on their machines is not possible.
So one of the good things that is coming with our gate US library is that we are able to use these pre
trained models for our problem
these architectures were created to solve one particular problem but tenants allow us to use these architectures
for other problems.
Also let us see how.