Lynn, it is a very simple architecture.
It has three convolutional layers.
Two of the congressional leaders also have average pulling leered.
Notice that it is not max pooling.
It was average pulling, as I told you earlier.
Also, average billing was more popular in the earlier days.
And later on, Max Pooling became more popular.
So Limit being one of the earliest congressional neural network architectures, had average pooling.
So the first convolutional it and in congressional it have average pulling toward converting it straight
away.
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, what did you do?
And after the convolutional Lears, we had 120 such features of one by one size.
These would fit into a fully connected neural network.
This problem was run on amnestied only, which is handwriting recognition data.
And it was able to achieve very good accuracy's.
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.