Distillation are widely different learning techniques. While data augmentation is concerned with samples and labels, distillation transfers knowledge from a large model or ensemble of models to smaller models. The looking at each example and assigning them a label that they believe describes it best. The assigned labels are subjective to the perception of their labelers. For example, a human labeler might perceive the that can potentially confuse the human labelers to choose a 1 or a 7 as the target label. Obtaining labels in many cases requires significant human involvement, and for that reason can be expensive and slow

Self-Supervised learning helps models to quickly achieve impressive quality with a small number of labels. As we described in chapter 3’s ‘Learning Techniques and Efficiency’ section, labeling of training Factoring in the costs of training human labelers on a given task, and then making sure that the labels are reliable, human labeling gets very expensive very quickly. Even after that it is likely that on labeled data, and hence achieving a high performance on a new task requires a large number of labels. 2. Compute Efficiency: Training for new tasks requires new models to be trained from scratch. For

-3.4]) true_b = 4.2 features, labels = synthetic_data(true_w, true_b, 1000) 47 https://discuss.d2l.ai/t/1775 3.2. 线性回归的从零开始实现 95 注意，features中的每一行都包含一个二维数据样本，labels中的每一行都包含一维标签值（一个标量）。 print('features:' print('features:', features[0],'\nlabel:', labels[0]) features: tensor([1.4632, 0.5511]) label: tensor([5.2498]) 通过生成第二个特征features[:, 1]和labels的散点图，可以直观观察到两者之间的线性关系。 d2l.set_figsize() d2l.plt.scatter(features[: scatter(features[:, (1)].detach().numpy(), labels.detach().numpy(), 1); 3.2.2 读取数据集 回想一下，训练模型时要对数据集进行遍历，每次抽取一小批量样本，并使用它们来更新我们的模型。由于 这个过程是训练机器学习算法的基础，所以有必要定义一个函数，该函数能打乱数据集中的样本并以小批量 方式获取数据。 在下面的代码中，我们定义一个data_i

drop(train_dataset.index) 将 MPG 字段移出为标签数据： # 移动 MPG 油耗效能这一列为真实标签 Y train_labels = train_dataset.pop('MPG') test_labels = test_dataset.pop('MPG') 统计训练集的各个字段数值的均值和标准差，并完成数据的标准化，通过 norm()函数 实现，代码如下： norm(test_dataset) # 标准化测试集 打印出训练集和测试集的大小： print(normed_train_data.shape,train_labels.shape) print(normed_test_data.shape, test_labels.shape) (314, 9) (314,) # 训练集共 314 行，输入特征长度为 9,标签用一个标量表示 预览版202112 第 normed_train_data) np.save('train_labels.npy', train_labels) # 保存测试集的特征和标签 np.save('normed_test_data.npy', normed_test_data) np.save('test_labels.npy', test_labels) 我们可以通过简单地统计数据集中各字段之间的两两分布来观察各个字段对

generate soft labels on the training data, and the student model learns to copy both the ground-truth labels as well as the soft labels generated by the teacher model. While the ground-truth labels simply assign assign a ‘1’ to the correct class, and a ‘0’ to the incorrect class, the soft labels consist of probabilities for each of the possible classes according to the teacher model. Figure 1-10: Distillation ground-truth and the teacher’s outputs on the given training data. The intuition is that the soft labels from the teacher can help the student capture how wrong/right the prediction is. For example, given

#用 来 测 试 的 数 据 download=True , #如 果 根 目 录 没 有 就 下 载 transform=ToTensor () ) #把 数 据 显 示 一 下 labels_map = { 0: ”T−Shirt ” , 1: ” Trouser ” , 2: ” Pullover ” , 3: ” Dress ” , 4: ”Coat” , 5: ” Sandal r e () # 抽 取 索 引 为 100 的 数 据 来 显 示 img , l a b e l = training_data [ 1 0 0 ] plt . t i t l e ( labels_map [ l a b e l ] ) #squeeze 函 数 把 为1 的 维 度 去 掉 plt . imshow ( img . squeeze () , cmap=” gray ” train_features , train_labels = next ( i t e r ( train_dataloader ) ) print ( f ” Feature ␣batch␣shape : ␣{ train_features . s i z e () }” ) print ( f ” Labels ␣batch␣shape : ␣{ train_labels . s i z e () }”

keras.datasets.mnist): """Returns the processed dataset.""" (train_images, train_labels), (test_images, test_labels) = ds.load_data() # Process the images for use. train_images = process_x(train_images) process_x(train_images) test_images = process_x(test_images) return (train_images, train_labels), (test_images, test_labels) (train_x, train_y), (test_x, test_y) = load_data() # You can train on the Fashion MNIST dataset we can use the index of the correct class for each example. The regular function expects one-hot labels which would require us to transform the class label 2, for example, to its one-hot representation

numpy as np data = np.random.random((1000, 100)) labels = np.random.randint(2, size=(1000, 1)) # 训练模型，以 32 个样本为一个 batch 进行迭代 model.fit(data, labels, epochs=10, batch_size=32) # 对于具有 10 个类的单输入模型（多分类分类）： data = np.random.random((1000, 100)) labels = np.random.randint(10, size=(1000, 1)) # 将标签转换为分类的 one-hot 编码 one_hot_labels = keras.utils.to_categorical(labels, num_classes=10) # 训练模型，以 32 个样本为一个 batch batch 进行迭代 model.fit(data, one_hot_labels, epochs=10, batch_size=32) 3.1.5 例子 这里有几个可以帮助你开始的例子！ 在 examples 目录 中，你可以找到真实数据集的示例模型： • CIFAR10 小图片分类：具有实时数据增强的卷积神经网络 (CNN) 快速开始 11 • IMDB 电影评论情感分类：基于词序列的

images are randomly given Random variable X representing the feature vector (and thus the image) The labels are random, since the images are randomly given Random variable Y representing the label Feng Li n-dimensional vector Each feature x(i) j ∈ {0, 1} (j = 1, · · · , n) y (i) ∈ {0, 1} The features and labels can be represented by random variables {Xj}j=1,··· ,n and Y , respectively Feng Li (SDU) GDA, NB The Expectation-Maximization (EM) Algorithm A training set {x(1), x(2), · · · , x(m)} (without labels) The log-likelihood function ℓ(θ) = log m � i=1 p(x(i); θ) = m � i=1 log � z(i)∈Ω p(x(i)

tensorflow as tf fashion_mnist = keras.datasets.fashion_mnist (train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data() Preprocess data import matplotlib.pyplot as plt plt in_labels[i])) plt.show( ) Build the model Train and evaluate Make prediction Visualize prediction i = 6 plt.figure(figsize=(6,3)) plt.subplot(1,2,1) plot_image(i, predictions, test_labels, test_images) test_images) plt.subplot(1,2,2) plot_value_array(i, predictions, test_labels) plt.show() Visualize prediction “Hello TensorFlow” Try it！ 扫码试看/订阅 《TensorFlow 2 项目进阶实战》视频课程