{"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":321,"pdf_page":337,"physical_index":337,"physical_index_base":0,"printed_page_status":"recognized","page_sha256":"ca5f26acdf26694ac53168f5917e2132c000e9883ae5e42a739ed1b8c608f64f"},"chapter":"第10章","content":"如图10-17右图所示，根据数据自动学习网络的权重，以便让所有输入xinput的预测输出Youtput接近目标Ytarget。为了衡量与该目标的差距，我们使用了一个误差函数E（见图10-17右图的右上端）。常用的误差函数是：\n\n$$\n\\begin{array} { r } { E ( y _ { o u t p u t } , y _ { t a r g e t } ) { = } \\frac { 1 } { 2 } ( y _ { o u t p u t } - y _ { t a r g e t } ) ^ { 2 } } \\end{array}\n$$\n\n针对图10-17，我们首先看一下什么是正向传播（即从图的底部往上看）。取一个输入样本 $( x _ { i n p u t } , y _ { t a r g e t } )$ ，并更新网络的输入层。为了保持一致性，我们将输入视为与其他任何节点相同，但不具有激活函数，所以：\n\n$$\ny _ { 1 } = x _ { i n p u t }\n$$\n\n然后，更新第一个隐藏层。我们取上一层节点的输出y，并使用权重来计算下一层节点的输入x，即：\n\n$$\nx _ { j } = \\sum _ { i \\in i n ( j ) } w _ { i j } y _ { i } + b _ { j }\n$$\n\n有了上面公式中的x值之后，更新第一个隐藏层中节点的输出。为此，我们使用激活函数\n\n第10章神经网络\n\n321","previous_page":320,"next_page":322}