{"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":333,"pdf_page":349,"physical_index":349,"physical_index_base":0,"printed_page_status":"recognized","page_sha256":"40d010bede4aa430c92c6a9e826dc9a3fcc0a072b2caf706fa0aee3d33960f75"},"chapter":"第10章","content":"结果如图10-25、图10-26所示。\n\n下面我们尝试其他优化器，使用AdaGrad和Adam优化器并对比其效果。AdaGrad的核心是灵活地修改模型中每个系数的学习率。该优化器对于凸优化问题非常有效，但不一定适合非凸优化问题的神经网络训练。通过指定AdagradOptimizer（而不是GradientDescentOptimizer）来使用AdaGrad。对于AdaGrad，可能需要使用较大的学习率。\n\n对于非凸优化问题，Adam有时比AdaGrad更有效。要使用Adam，可调用tf.train.AdamOptimizer方法。此方法将几个可选超参数作为参数，但解决方案仅指定其中一个(learning\\_rate）。在应用设置中，我们应该谨慎指定和调整可选超参数。\n\n第10章 神经网络\n\n333","previous_page":332,"next_page":334}