‫LeNet, it is a very simple architecture.
‫It has three convolutional layers.
‫Two of the convolutional layers also have average pooling layer
‫Notice that it is not max pooling.
‫It was average pooling, as I told you earlier.
‫Also, average pooling was more popular in the earlier days.
‫And later on, Max Pooling became more popular.
‫So LeNet being one of the earliest convolutional neural network architectures, had average pooling.
‫So the first convolutional it and in convolutional layer have average pooling, third convolutional layer straight
‫away
‫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 convolutional layers, we had 120 such features of one by one size.
‫These were fed into a fully connected neural network.
‫This problem was run on mnest data 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.