{"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":366,"pdf_page":382,"physical_index":382,"physical_index_base":0,"printed_page_status":"recognized","page_sha256":"0337a991f6c2790dc910540b533bc71f8b035ccda6849a216647b92d9fb5749e"},"chapter":"第10章","content":"人工智能与大数据技术导论\n\n3. 在 DNN 模型中使用嵌套\n\n下面我们使用嵌套列来实现DNN模型。嵌套列会将稀疏数据作为输入，并返回一个低维度密集矢量作为输出。从计算方面而言，embeddingcolumn通常用于在稀疏数据中训练模型最有效的选项。在下面的代码中，我们将数据投射到二维空间的embedding column来为模型定义特征列，并定义符合以下规范的DNNClassifier。\n\n●具有两个隐藏层，每个包含20个单元。\n\n●采用学习速率为0.1的AdaGrad优化方法。\n\n●gradient\\_clip\\_norm 值为 5.0。\n\n注意：在实际工作中，我们可能会将数据投射到2维以上（比如50或100）的空间中。但就目前而言，2维是比较容易可视化的维数。\n\n366","previous_page":365,"next_page":367}