Author: Zen and the Art of Computer Programming
1 Introduction
Convolutional Neural Network (CNN) is a deep learning technology and one of the most commonly used models in machine learning. This model performs well in processing multimedia data such as images, videos, and voices. CNN extracts local features by performing convolution operations on the input data, further reduces the dimension and enhances the abstraction of features through pooling operations, and finally outputs the classification results. Compared with other models, CNN has the following advantages:
- Strong feature learning ability: CNN can automatically extract the global structural information and local features in the image from the original data, and convert it into useful data, allowing subsequent layers to learn and classify data more effectively.
- Modular design: The connection relationship between each layer of CNN can be very flexible, so the model can be fine-tuned in different task scenarios, while sharing the same underlying convolution kernel.
- Weight sharing: The weight parameters of CNN are often shared in different layers, which means that the model requires fewer parameters, thereby reducing the computational complexity.
However, due to the high nonlinearity and depth of CNN, it still has certain difficulties in modeling image data. In order to solve this problem, it is necessary to use some special structure CNN models to improve image classification performance. Among them, the LeNet model is a representative model. LeNet is an early and famous convolutional neural network model that is widely used in the field of image recognition. Next, we will build an image classification system based on LeNet from scratch, and gradually explain the composition and main workflow of the CNN model.
2. Core concepts and terminology
2.1 LeNet model structure
The LeNet model consists of five layers:
C1: The first layer is a convolutional layer, including 6 convolution kernels, each with a size of 5x5, the convolution step size is 1, and the activation function is a sigmoid function.