Early Stop,Dropout 主讲人：龙良曲 Tricks ▪ Early Stopping ▪ Dropout ▪ Stochastic Gradient Descent Early Stopping ▪ Regularization How-To ▪ Validation set to select parameters ▪ Monitor validation performance

(NGC) container registry installation documentation based on your platform. ‣ Ensure that you have access and can log in to the NGC container registry. Refer to NGC Getting Started Guide for more information 4 21.03 NVIDIA CUDA 11.2.1 1.9.0a0+df837d0 TensorRT 7.2.2.3 21.02 NVIDIA CUDA 11.2.0 1.8.0a0+52ea372 TensorRT 7.2.2.3+cuda11.1.0.024 20.12 NVIDIA CUDA 11.1.1 1.8.0a0+1606899 TensorRT 7.2.2 20.11 4 21.03 NVIDIA CUDA 11.2.1 1.9.0a0+df837d0 TensorRT 7.2.2.3 21.02 NVIDIA CUDA 11.2.0 1.8.0a0+52ea372 TensorRT 7.2.2.3+cuda11.1.0.024 20.12 NVIDIA CUDA 11.1.1 1.8.0a0+1606899 TensorRT 7.2.2 20.11

However, there are other ways to make BOS run quicker by using smaller datasets, early stopping or low resolution inputs etc. Early Stopping can even be applied with the HyperBand to terminate the runs sooner construction of multiscale networks without needing to tweak the controller for a target child. The early RL based NAS models used the validation accuracy as a primary reward signal which is perfect for the experts. Imagine that we are developing an application to identify a flower from its picture. We have access to a flowers dataset (oxford_flowers102). As an application developer, with no experience with ML

fewer epochs. We could ideally save an epoch’s worth of training time by terminating the training early, if we adopt this hypothetical sample efficient model training. Figure 3-1: The above plot demonstrates used to scan people entering a building. I have yet to come across inverted people trying to gain access! Popular deep learning frameworks provide quick ways to integrate these transformations during the it. The code for this project is available as a Jupyter notebook here. Tensorflow provides easy access to this dataset through the tensorflow-datasets package. Let’s start by loading the training and

样）。也就是说，音频和 文本之间没有1:1的对应关系，因为数千个样本可能对应于一个单独的单词。这也是“序列到序列”的学习问 题，其中输出比输入短得多。 图1.3.5: -D-e-e-p- L-ea-r-ni-ng- 在录音中。 文本到语音。这与自动语音识别相反。换句话说，输入是文本，输出是音频文件。在这种情况下，输出比输 入长得多。虽然人类很容易识判断发音别扭的音频文件，但这对计算机来说并不是那么简单。 模 型是否能很好地泛化取决于很多因素。例如，具有更多参数的模型可能被认为更复杂，参数有更大取值范围 的模型可能更为复杂。通常对于神经网络，我们认为需要更多训练迭代的模型比较复杂，而需要早停（early stopping）的模型（即较少训练迭代周期）就不那么复杂。 我们很难比较本质上不同大类的模型之间（例如，决策树与神经网络）的复杂性。就目前而言，一条简单的经 验法则相当有用：统计学家认为 相等的：内存接口通常为64位或更宽（例如，在 最多384位的GPU上）。因此读取单个字节会导致由于更宽的存取而产生的代价。 其次，第一次存取的额外开销很大，而按序存取（sequential access）或突发读取（burst read）相对开销较 小。有关更深入的讨论，请参阅此维基百科文章131。 130 https://discuss.d2l.ai/t/3838 131 https://en

Outline Supervised Learning https://towardsdatascience.com/supervised-vs-unsupervised-learning-14f68e32ea8d Massive Unlabeled data Unsupervised Learning https://medium.com/intuitionmachine/predictive-l

keep-prob=1(没有dropout) keep-prob=0.5(常用取值，保留一半神经元) 在训练阶段使用，在测试阶段不使用！ Dropout正则化 13 正则化 Early stopping代表提早停止训练神经网络 Early stopping的优点是，只运行 一次梯度下降，你可以找出?的较小 值，中间值和较大值，而无需尝试?2 正则化超级参数?的很多值。 14 正则化 数据增强：随意翻转和裁剪、扭曲变形图片

keep-prob=1(没有dropout) keep-prob=0.5(常用取值，保留一半神经元) 在训练阶段使用，在测试阶段不使用！ Dropout正则化 26 正则化 Early stopping代表提早停止训练神经网络 Early stopping的优点是，只运行 一次梯度下降，你可以找出?的较小 值，中间值和较大值，而无需尝试?2 正则化超级参数?的很多值。 27 正则化 大部分的计算机视觉任务使用很多的数据

data ▪ Constraint model complexity ▪ shallow ▪ regularization ▪ Dropout ▪ Data argumentation ▪ Early Stopping Regularization Enforce Weights close to 0 Weight Decay Intuition How ▪ L1-regularization

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