Convolutional Neural Network LeNet

LeNet, also known as LeNet-5, is an early convolutional neural network (CNN) architecture developed by Yann LeCun and his team in 1997 specifically for handwritten digit recognition tasks.Gradient-Based Learning Applied to Document Recognition"The network structure and training process of LeNet-5 are introduced in detail.

LeNet-5 is a pioneering work in the field of deep learning and convolutional neural networks, which laid the foundation for many key concepts in modern deep learning, such as convolutional layers, pooling layers, and fully connected layers.

The architecture of LeNet-5 is relatively simple, with a total of 7 layers (excluding the input layer), including two convolutional layers (C1 and C3), two pooling layers (S2 and S4), followed by two fully connected layers (F6 and the output layer). The convolutional layer is responsible for extracting image features, while the pooling layer is responsible for reducing the spatial dimension of the features while increasing the invariance to image displacement. The final fully connected layer is used for classification tasks.