"PaddlePaddle From Beginner to Alchemist" - Part 11: Custom Image Dataset Recognition
This note mainly introduces how to use PaddlePaddle for training and prediction in image classification tasks, which specifically includes the following parts: ### 1. Dataset Preparation The author extracted 240 images from a dataset containing 6 categories of fruit images as the training set and organized them into CSV file format. ### 2. Model Construction A simple LeNet model structure was defined using PaddlePaddle. The model consists of two convolutional layers, two pooling layers, a fully connected layer, and finally performs classification through Softmax.
Read MoreImplementing a Simple Web Crawler with Python2
This project is a simple web crawler designed to scrape relevant content from CSDN blogs and save it as HTML files. It includes the basic process of a crawler: crawling, parsing, and storage. ### Crawling Process 1. **Scheduler (`spider_main.py`)**: - This is the entry point of the entire project. - It calls `HtmlOutputer` to output data, `Downloader` to download web page content, and `HtmlParser` to parse the downloaded content (parsing logic continues...).
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