{"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":277,"pdf_page":293,"physical_index":293,"physical_index_base":0,"printed_page_status":"recognized","page_sha256":"2fddd2c186b46370607299c823f25cc423737bb06700a52baa80a772f8d844d3"},"chapter":"第9章","content":"上述函数可以提供0\\~1的有界值，两边有渐近线。其中， $\\mathrm { z } { = } \\mathrm { b } { + } \\mathrm { w } _ { 1 } \\mathrm { x } _ { 1 } { + } \\mathrm { w } _ { 2 } \\mathrm { x } _ { 2 } { + } . . . { + } \\mathrm { w } _ { \\mathrm { N } } \\mathrm { x } _ { \\mathrm { N } }$ 。w是模型权重，b是偏差，x是特征变量。所以，图9-10也可以描述为如图9-11所示的曲线。\n\n从数学的角度，我们还可以得出如下公式：\n\n$$\nz = l o g ( \\frac { y } { 1 - y } )\n$$\n\n下面我们来看一个例子。假定有一个逻辑回归模型，它有三个特征变量，权重和偏差是b=1, $\\mathrm { w } _ { 1 } = 2 , ~ \\mathrm { w } _ { 2 } = - 1 , ~ \\mathrm { w } _ { 3 } = 5$ 。假定我们有一个样本数据进来： $x _ { 1 } = 0 , ~ x _ { 2 } = 1 0 , ~ x _ { 3 } = 2$ 。那么，我们可以计算出 $z = ( 1 ) + ( 2 ) ( 0 ) + ( - 1 ) ( 1 0 ) + ( 5 ) ( 2 ) = 1$ o\n\n$$\ny ^ { \\prime } = { \\frac { 1 } { 1 + e ^ { - ( 1 ) } } } = 0 . 7 3 1\n$$\n\n所以，概率为73.1%，如图9-12所示。\n\n第9章 TensorFlow 高级知识\n\n277","previous_page":276,"next_page":278}