{"resolved":true,"match_type":"uuid","source_type":"original_book","book_id":"cd4f7678-cd9b-4971-bde0-e9ad46a6cc71","title":"人工智能与大数据技术导论","author":null,"edition":null,"publisher":null,"year":null,"isbn":"9787302517986","requested_by":"printed_page","book":{"source_type":"original_book","book_id":"cd4f7678-cd9b-4971-bde0-e9ad46a6cc71","id":"cd4f7678-cd9b-4971-bde0-e9ad46a6cc71","uuid":"cd4f7678-cd9b-4971-bde0-e9ad46a6cc71","title":"人工智能与大数据技术导论","normalized_title":"人工智能与大数据技术导论","author":null,"edition":null,"publisher":null,"year":null,"isbn":"9787302517986","isbns":["9787302517986"],"parsed":true,"ai_readable":true,"document_type":"book","page_count":490,"navigation_url":"https://workshop.closeclaw.top/ai/book/cd4f7678-cd9b-4971-bde0-e9ad46a6cc71","title_url":"https://workshop.closeclaw.top/ai/title/%E4%BA%BA%E5%B7%A5%E6%99%BA%E8%83%BD%E4%B8%8E%E5%A4%A7%E6%95%B0%E6%8D%AE%E6%8A%80%E6%9C%AF%E5%AF%BC%E8%AE%BA","fixed_page_url_template":"https://workshop.closeclaw.top/ai/book/cd4f7678-cd9b-4971-bde0-e9ad46a6cc71/page/{printed_page}","path_api_url_template":"https://workshop.closeclaw.top/api/ai/book/cd4f7678-cd9b-4971-bde0-e9ad46a6cc71/page/{printed_page}","read_url":"https://workshop.closeclaw.top/api/ai/read/cd4f7678-cd9b-4971-bde0-e9ad46a6cc71","page_url":"https://workshop.closeclaw.top/api/ai/books/cd4f7678-cd9b-4971-bde0-e9ad46a6cc71/page","search_url":"https://workshop.closeclaw.top/api/ai/books/cd4f7678-cd9b-4971-bde0-e9ad46a6cc71/search"},"page":{"printed_page":309,"pdf_page":325,"physical_index":325,"physical_index_base":0,"printed_page_status":"recognized","page_sha256":"7f8d12625d2402ff82183ead03b6aea18aea6a485d68c8df9ff0a57b71eadb08"},"chapter":"第10章","content":"在图10-4（颜色请参看下载包中的相关文件）所示的模型中，我们添加了一个表示中间值的“隐藏层”。隐藏层中的每个黄色节点（中间层）均是蓝色输入节点（最下面一层）值的加权和。输出的是黄色节点（中间层）的加权和。\n\n此模型是线性的，因为其输出仍是其输入的线性组合。在图10-5所示的模型中，我们又添加了一个表示加权和的“隐藏层”。\n\n此模型仍是线性的。当你将输出表示为输入的函数并进行简化时，只是获得输入的另一个加权和而已。该加权和无法对图10-2中的非线性问题进行有效建模。因为即便我们添加任意多的分层，所有线性函数的组合依然是线性函数。因此，我们要从其他方面着手。所谓的从其他方面着手，也就是说，我们需要添加非线性函数。\n\n10.1.2 激活函数\n\n要对非线性问题进行建模，我们可以直接引入非线性函数。这种非线性函数可位于任何小的隐藏式节点的输出中。我们可以用非线性函数将每个隐藏层节点像管道一样连接起来。在图10-6所示的模型中，在“隐藏层1”中的各个节点的值传递到下一层进行加权求和之前，我们采用一个非线性函数对其进行了转换。这种非线性函数称为激活函数。\n\n第10章 神经网络\n\n309","previous_page":null,"next_page":310}