## PyTorch ## Early Stop,Dropout 主讲人：龙良曲 ## Tricks Early Stopping Dropout ■ Stochastic Gradient Descent ## Early Stopping ■ Regularization  ## ☐ ☐ ☐ net_dropped = torch.nn.Sequential( torch.nn.Linear(784, 200), torch.nn.Dropout(0.5), #

Reduce Overfitting ## ■ More data ## Constraint model complexity - shallow regularization ## Dropout Data argumentation ## Early Stopping Regularization ## Weight Decay $$ J\left(\theta\right)

jpg)  Output ## Dropout AutoEncoders  (a) [Image](/uploads/documents/4/d/4/8/4d48c77d380b9345c2dae434d12fe818/p13_2.jpg) (b) After applying dropout.  ![Image](/up

footprints. In the first chapter, we briefly introduced learning techniques such as regularization, dropout, data augmentation, and distillation to improve quality. These techniques can boost metrics like augmentation and distillation, to discuss in this chapter. This is because, firstly, regularization and dropout are fairly straightforward to enable in any modern deep learning framework. Secondly, data augmentation tensorflow.keras import applications as apps from tensorflow.keras import layers, optimizers, metrics DROPOUT_RATE = 0.2 LEARNING_RATE = 0.0002 NUM_CLASSES = 102 def create_model(): # Initialize the core

oxford_flowers102 flowers dataset. We used two hyperparameters: LEARNING_RATE and DROPOUT_RATE. The learning rate was set to 0.0002 and the dropout rate was 0.2. The model reached the top accuracy of 70% after training create_model() function here has two additional parameters: learning_rate and dropout_rate which replace the global LEARNING_RATE and DROPOUT_RATE parameters from chapter 3. We have an additional function build_hp_model() 0.01] and dropout_rate in range [0.1, 0.8]. The build_hp_model() is called by the tuner to create a model for each trial with the chosen values for the learning_rate and dropout_rate. DROPOUT_RATE = 0

28 Constraints ..... 33 Regularizers ..... 34 Activations ..... 35 Dense Layer ..... 38 Dropout Layers ..... 42 Flatten Layers ..... 42 Reshape Layers ..... 43 Permute Layers ..... 44 RepeatVector Activation, Dropout model = Sequential() model.add(Dense(512, activation='relu', input_shape=(784,))) model.add(Dropout(0.2)) model.add(Dense(512, activation='relu')) model.add(Dropout(0.2)) 9;)) Where. • Line 1 imports Sequential model from Keras models • Line 2 imports Dense layer, Dropout layer and Activation module • Line 4 create a new sequential model using Sequential API - Line

5.2 核心网络层 ..... 59 5.2.1 Dense [source] ..... 59 5.2.2 Activation [source] ..... 60 5.2.3 Dropout [source] ..... 60 5.2.4 Flatten [source] ..... 60 5.2.5 Input [source] ..... 61 5.2.6 Reshape softmax 多分类： import keras from keras.models import Sequential from keras.layers import Dense, Dropout, Activation from keras.optimizers import SGD # 生成虚拟数据 import numpy as np x_train = np.random.random((1000 add(Dense(64, activation='relu', input_dim=20)) model.add(Dropout(0.5)) model.add(Dense(64, activation='relu')) model.add(Dropout(0.5)) model.add(Dense(10, activation='softmax')) sgd

权重衰减 ..... 149 4.5.1 高维线性回归 ..... 151 4.5.2 从零开始实现 ..... 151 4.5.3 简洁实现 ..... 153 4.6 暂退法 (Dropout) ..... 156 4.6.1 重新审视过拟合 ..... 156 4.6.2 扰动的稳健性 ..... 157 4.6.3 实践中的暂退法 ..... 157 4.6.4 从零开始实现 史上物种飞速进化的时期。事实上，最先进的技术不仅仅是将可用资源应用于几十年前的算法的结果。下面列举了帮助研究人员在过去十年中取得巨大进步的想法（虽然只触及了皮毛）。 - 新的容量控制方法，如dropout (Srivastava et al., 2014)，有助于减轻过拟合的危险。这是通过在整个神经网络中应用噪声注入 (Bishop, 1995) 来实现的，出于训练目的，用随机变量来代替权重。 05347819 0.17096086 0.1863975 -0.09107699 -0.02123026]] 在接下来的章节中，我们将继续讨论过拟合问题和处理这些问题的方法，例如权重衰减和dropout。 ## 小结 · 欠拟合是指模型无法继续减少训练误差。过拟合是指训练误差远小于验证误差。 · 由于不能基于训练误差来估计泛化误差，因此简单地最小化训练误差并不一定意味着泛化误差的减小。机器

used in chapter 3. width_multiplier = 1.0 params = { 'learning_rate': 1e-3, 'dropout_rate': 0.5, } model_wm_10, _ = train_model( width_multiplier, params, epochs=5 ) Model: (BatchNormalization)|(None, 124, 32)|128| |max\_pooling1d\_6 (MaxPooling1D)|(None, 31, 32)|0| |dropout\_10 (Dropout)|(None, 31, 32)|0| |conv1d\_14 (Conv1D)|(None, 31, 64)|18496| |batch\_normalization\_14 ( (BatchNormalization)|(None, 31, 64)|256| |max\_pooling1d\_7 (MaxPooling1D)|(None, 7, 64)|0| |dropout\_11 (Dropout)|(None, 7, 64)|0| |conv1d\_16 (Conv1D)|(None, 7, 128)|73856| |batch\_normalization\_16 (B

8.7 可视化 8.8 参考文献 第9章 过拟合 9.1 模型的容量 9.2 过拟合与欠拟合 9.3 数据集划分 9.4 模型设计 9.5 正则化 9.6 Dropout 9.7 数据增强 9.8 过拟合问题实战 9.9 参考文献 第10章 卷积神经网络 10.1 全连接网络的问题 10.2 卷积神经网络 10.3 卷积层实现 10 Unit，简称 ReLU)激活函数，这是现在使用最为广泛的激活函数之一。2012 年，Alex Krizhevsky 提出了 8 层的深层神经网络 AlexNet，它采用了 ReLU 激活函数，并使用 Dropout 技术来防止过拟合，同时抛弃了逐层预训练的方式，直接在两块 NVIDIA GTX580 GPU 上训练网络。AlexNet 在 ILSVRC-2012 图片识别比赛中获得了第一名的成绩，比第二名在 ### 9.6 Dropout 2012 年，Hinton 等人在其论文《Improving neural networks by preventing co-adaptation of feature detectors》中使用了 Dropout 方法来提高模型性能。Dropout 通过随机断开神经网络的连接，减少每次训练时实际参与计算的模型的参数量；但是在测试时，Dropout 会恢复所有的连接，保证模型测试时获得最好的性能。