{"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":346,"pdf_page":362,"physical_index":362,"physical_index_base":0,"printed_page_status":"recognized","page_sha256":"ca10a5e9490d60c1ddca34035aa06ab30c1c70f8e3414527b261c43bcbca05bf"},"chapter":"第10章","content":"下面显示一个随机样本及其对应的标签，代码如下：\n\n结果如图10-44所示。\n\n下面我们为MNIST构建线性模型。首先，创建一个基准模型，作为比较对象。LinearClassifier可提供一组k类一对多分类器，每个类别（共k个）对应一个分类器。除了报告准确率和绘制对数损失函数随时间变化的曲线图之外，我们还展示了一个混淆矩阵。混淆矩阵会显示错误分类为其他类别的类别。另外，我们使用1og\\_loss函数跟踪模型的错误。读者不应将此函数与用于训练的LinearClassifier内部损失函数相混淆。\n\n人工智能与大数据技术导论\n\n346","previous_page":345,"next_page":347}