In Torch, Module is a fundamental building block of modular neural networks. It can contain one or multiple layers, and can recursively nest other Modules. Modules can have parameters and define a forward function for implementing forward propagation operations. Modules provide a way to organize and manage the structure of neural networks, making the design and training of neural networks more flexible and convenient.<\/p>\n","protected":false},"excerpt":{"rendered":"
In Torch, Module is a fundamental building block of modular neural networks. It can contain one or multiple layers, and can recursively nest other Modules. Modules can have parameters and define a forward function for implementing forward propagation operations. Modules provide a way to organize and manage the structure of neural networks, making the design […]<\/p>\n","protected":false},"author":8,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_import_markdown_pro_load_document_selector":0,"_import_markdown_pro_submit_text_textarea":"","footnotes":""},"categories":[1],"tags":[960,75,944,1239,1238],"class_list":["post-3294","post","type-post","status-publish","format-standard","hentry","category-uncategorized","tag-deep-learning","tag-machine-learning","tag-neural-networks","tag-pytorch","tag-torch-module"],"yoast_head":"\n