Lane it is a very simple architecture.
It has three convoluted layers two of the Congressional leaders also have average pooling layer.
Notice that it is not Max pooling it was average pulling as I told you earlier also average pooling
was more popular in the earlier days and later on Max pooling became more popular.
So limit being one of the ugliest congressional neural network architectures had average pooling.
So the first convolution led and training conversion led have average pooling third conversion led straight
up it gives its output to a fully connected neural network.
If you look at the input image it took in an image of size 32 by 32.
And after the convolution layers we had 120 such features of one by one size these would fit into a
fully connected neural network.
This problem was run on amnesty only which is handwriting recognition data and it was able to achieve
very good accuracies.
This architecture is very simple.
In fact I would encourage you to make this architecture in your system you know everything that you
need to know to create this architecture and you can run this.