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‫LeNet, it is a very simple architecture.

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‫It has three convolutional layers.

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‫Two of the convolutional layers also have average pooling layer

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‫Notice that it is not max pooling.

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‫It was average pooling, as I told you earlier.

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‫Also, average pooling was more popular in the earlier days.

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‫And later on, Max Pooling became more popular.

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‫So LeNet being one of the earliest convolutional neural network architectures, had average pooling.

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‫So the first convolutional it and in convolutional layer have average pooling, third convolutional layer straight

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‫away

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‫gives its output to a fully connected neural network.

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‫If you look at the input image it took in an image of size 32 by 32

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‫And after the convolutional layers, we had 120 such features of one by one size.

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‫These were fed into a fully connected neural network.

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‫This problem was run on mnest data only, which is handwriting recognition data.

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‫And it was able to achieve very good accuracy's.

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‫This architecture is very simple.

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‫In fact, I would encourage you to make this architecture in your system.

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‫You know, everything that you need to know to create this architecture.

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‫And you can run this.