曾元（Yuan Tseng） 2022.02.18 Outline ● Background: Prerequisites & What is Pytorch? ● Training & Testing Neural Networks in Pytorch ● Dataset & Dataloader ● Tensors ● torch.nn: Models, Loss Functions Training & Testing Neural Networks Validation Testing Training Guide for training/validation/testing can be found here. Training & Testing Neural Networks - in Pytorch Validation Testing Training Load 1. torch.utils.data.Dataset & torch.utils.data.DataLoader Dataset & Dataloader Training: True Testing: False ● Dataset: stores data samples and expected values ● Dataloader: groups data in batches

experiencing a drop in predictive power during testing and validation, the recommended workaround is to not add the .eval() flag on your model when doing testing or validation. PyTorch RN-08516-001_v23.07 representation or warranty that products based on this document will be suitable for any specified use. Testing of all parameters of each product is not necessarily performed by NVIDIA. It is customer’s sole responsibility the product is suitable and fit for the application planned by customer, and perform the necessary testing for the application in order to avoid a default of the application or the product. Weaknesses in

embedding Classification Matching Wordpiece Keras tokenizer Jieba Hanlp Model Saving Deployment Testing Vocab Sequence labeling Huawei tokenizer word2vec Elmo pb ckpt H5 (Keras) RESTful API

value of J(θ) increases or even blows up, adjust your learning rate and try again. We recommend testing alphas at a rate of of 3 times the next smallest value (i.e. 0.01, 0.03, 0.1, 0.3 and so on). You

的算法，我们会在各自章节中介绍。其中本课程中，用梯度下降法作为主要的 优化算法。 26 机器学习的概念-模型评估 当损失函数给定时，我们将基于模型训练数据的误差(Training Error)和测试数据的 误差(Testing Error)作为模型评估的标准。 测试误差的具体定义为：????? = 1 ?′ ෍ ?=1 ?′ L ??, መ? ?? 其中，?′为测试数据数量，L(??, መ?(??))是损失函数，

show_heatmaps(attention_weights.unsqueeze(0).unsqueeze(0), xlabel='Sorted training inputs', ylabel='Sorted testing inputs') 10.2.4 带参数注意力汇聚 非参数的Nadaraya‐Watson核回归具有一致性（consistency）的优点：如果有足够的数据，此模型会收敛到 最优结果。尽管 show_heatmaps(net.attention_weights.unsqueeze(0).unsqueeze(0), xlabel='Sorted training inputs', ylabel='Sorted testing inputs') 392 10. 注意力机制 小结 • Nadaraya‐Watson核回归是具有注意力机制的机器学习范例。 • Nadaraya‐Watson核回归的注意力汇聚是对训