‫Now, let us look at another CNN architecture.
‫GoogleNet was the winner of 2014 challenge.
‫GoogleNet had one new concept and the concept was of an Inception module. Inception module
‫Looks something like this.
‫That input to the Inception module is given to four different layers.
‫Three of these layers are convolutional layers.
‫And the fourth one is a max pool layer.
‫If you look at these convolutional 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 convolutional layers with two by two or three by two window.
‫But in the Inception module, you can see that windows or size one by one are used.
‫This one as represents the stride.
‫So it has a strode of one, the output of these two convolutional layers.
‫And this max pooling layer then went into three different convolutional layers.
‫The output of these four was then put into a depth concatinator.
‫Will not be discussing that depth concatinator here.
‫This whole thing is called an inception module.
‫And the actual architecture of GoogleNet was something like this input of images right through here.
‫Then there was a convolutional layer, a max pool layer, a local response normal layer, two convolutional layers
‫and so on.
‫All of these are stacked inception modules.
‫So this is the Inception layer.
‫This is Inception layer.
‫So many of these inception layers, the output of these goes into 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
‫most of the working professionals or student 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 Guide 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 Tenent allow us to use these architectures for other problems.
‫Also, let us see how.