[In-depth understanding of pytorch] PyTorch Convolutional Neural Network (CNN)
Introduction to PyTorch Convolutional Neural Networks (CNN)
Convolutional Neural Network (CNN) is a deep learning model widely used in computer vision tasks. Through components such as convolutional layers, pooling layers, and fully connected layers, it can effectively extract image features and achieve high-accuracy image classification, object detection, and semantic segmentation. This article will introduce the principle of CNN in detail and demonstrate how to implement a simple CNN model using PyTorch.
The principle of convolution layer
A convolutional layer is one of the core components of a CNN, which uses filters (also known as convolution kernels) to slide over the input image for feature extraction. Each element of the filter is multiplied by the pixel value at the corresponding position of the input image, and all the multiplication results are summed to obtain the output feature map. The convolution operation has the characteristics of parameter sharing and local receptive field, so that CNN can effectively capture the spatial structure information of the image.
The principle of pooling layer
The pooling layer is used to reduce the spatial size of the feature map, reduce the number of model parameters, and enhance the robustness of the model to position changes. A common pooling operation is the maximum pooling (Max Pooli