The Architecture and Implementation of LeNet-5

This very old neural network architecture was developed in 1998 by a French-American computer scientist Yann André LeCun, Leon Bottou, Yoshua Bengio, and Patrick Haffner. This architecture was developed for the recognition of handwritten and machine-printed characters. It is the basis of other deep learning models. The architecture consists of a total of 7 layers consisting- 2 sets of Convolution layers and 2 sets of Average pooling layers which are followed by a flattening convolution layer. After that, we have 2 dense fully connected layers and finally a softmax classifier.