Train-Val-Test划分 主讲人：龙良曲 Recap How to detect Splitting Train Set Test Set For example 60K 10K test while train train test trade-off Overfitt ing For others judge ▪ Kaggle Train Set Test Set Set Val Set Unavailable train-val-test K-fold cross-validation Train Set Test Set Val Set k-fold cross validation ▪ merge train/val sets ▪ randomly sample 1/k as val set 下一课时 减轻Overfitting Thank

framework and comes up with the following advantages:  Larger community support.  Easy to test.  Keras neural networks are written in Python which makes things simpler.  Keras supports the required information from the data.  Split data: Split the data into training and test data set. Test data will be used to evaluate the prediction of the algorithm / Model (once the machine learn) existing training and test data)  Evaluate model: Evaluate the model by predicting the output for test data and cross- comparing the prediction with actual result of the test data.  Freeze

high-school students and census bureau employees. The dataset has 60,000 training examples and 10,000 test examples. Figure 2-11 shows a sample of 100 labeled images from this dataset. Each input example and reshaping of the data is done by the process_x() method which is invoked for both the train and test images. Once we have loaded our data and processed it, we can do some fun stuff with it. import numpy (train_images, train_labels), (test_images, test_labels) = ds.load_data() # Process the images for use. train_images = process_x(train_images) test_images = process_x(test_images) return (train_images

train=True , #用 来 训 练 的 数 据 8 1.2. 导入样本数据 download=True , #如 果 根 目 录 没 有 就 下 载 transform=ToTensor () ) test_data = datasets . FashionMNIST( root=” data ” , train=False , #用 来 测 试 的 数 据 download=True , #如 train_dataloader = DataLoader ( training_data , batch_size =64, s h u f f l e= True ) test_dataloader = DataLoader ( test_data , batch_size =64, s h u f f l e=True ) 我们写点程序检测一下 DataLoader： train_features t+1}\n−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−” ) train_loop ( train_dataloader , model , loss_function , optimizer ) test_loop ( test_dataloader , model , loss_function ) print ( ”Done ! ” ) 然后就是训练和测试的程序，训练一轮的程序如下： def train_loop

dataset，或 称为训练集（training set））。然而，在训练数据上表现良好的模型，并不一定在“新数据集”上有同样的性 能，这里的“新数据集”通常称为测试数据集（test dataset，或称为测试集（test set））。 综上所述，可用数据集通常可以分成两部分：训练数据集用于拟合模型参数，测试数据集用于评估拟合的模 型。然后我们观察模型在这两部分数据集的性能。“一个模型在训练数据集上的性能”可以被想象成“一个学 torchvision.datasets.FashionMNIST( root="../data", train=True, transform=trans, download=True) mnist_test = torchvision.datasets.FashionMNIST( root="../data", train=False, transform=trans, download=True) ，每个类别由训练数据集（train dataset）中的6000张图像和测试数据 集（test dataset）中的1000张图像组成。因此，训练集和测试集分别包含60000和10000张图像。测试数据集 不会用于训练，只用于评估模型性能。 len(mnist_train), len(mnist_test) (60000, 10000) 每个输入图像的高度和宽度均为28像素。数据集由灰度

44 4.2.3.5 train_on_batch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 4.2.3.6 test_on_batch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 4.2.3.7 predict_on_batch 52 4.3.3.5 train_on_batch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 4.3.3.6 test_on_batch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 4.3.3.7 predict_on_batch y_batch) 只需一行代码就能评估模型性能： loss_and_metrics = model.evaluate(x_test, y_test, batch_size=128) 或者对新的数据生成预测： classes = model.predict(x_test, batch_size=128) 构建一个问答系统，一个图像分类模型，一个神经图灵机，或者其他的任何模型，就是这么 的快

章 分类问题 2 集共 70000 张图片。其中 60000 张图片作为训练集?train(Training Set)，用来训练模型，剩 下 10000 张图片作为测试集?test(Test Set)，用来预测或者测试，训练集和测试集共同组成 了整个 MNIST 数据集。 考虑到手写数字图片包含的信息比较简单，每张图片均被缩放到28 × 28的大小，同时 只保留了灰度信息，如图 ? (?) − ?? (?)) 2 10 ?=1 ? ?=1 只需要采用梯度下降算法来优化损失函数得到?和?的最优解，然后再利用求得的模型去 预测未知的手写数字图片? ∈ ?test即可。 3.4 真的解决了吗 按照上述方案，手写数字图片识别问题似乎得到较好地解决？事实果真如此吗？深入 研究的话，就会发现，至少存在两大问题： 预览版202112 第 3 章 分类问题 自动下载、加载、切割 IMDB 数据集 train_data, test_data = datasets.IMDB.splits(TEXT, LABEL) print('len of train data:', len(train_data)) # 打印训练集句子数量 print('len of test data:', len(test_data)) # 打印测试集句子数量 print('example

the structure is as follows. dbpedia_csv/ dbpedia_csv/train.csv dbpedia_csv/readme.txt dbpedia_csv/test.csv dbpedia_csv/classes.txt Let's explore the dataset! First, let's see what classes we have. import " Let's find the number of train and test examples. !wc -l dbpedia_csv/train.csv !wc -l dbpedia_csv/test.csv 560000 dbpedia_csv/train.csv 70000 dbpedia_csv/test.csv It all looks good! Now, it’s time overfitting. We can now vectorize the train and test datasets. x_train_vectorized = vectorization_layer(x_train) x_test_vectorized = vectorization_layer(x_test) Step 3: Initialization of the Embedding Matrix

Scikit-learn主要用法 03 Scikit-learn案例 7 X_train | 训练数据. X_test | 测试数据. X | 完整数据. 符号标记 2.Scikit-learn主要用法 y_train | 训练集标签. y_test | 测试集标签. y | 数据标签. 8 2.Scikit-learn主要用法 导入工具包 from sklearn sklearn import datasets, preprocessing from sklearn.model_selection import train_test_split from sklearn.linear_model import LinearRegression from sklearn.metrics import r2_score 基本建模流程 9 2.Scikit-learn主要用法 Scikit-learn主要用法 from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=12, stratify=y, test_size=0.3) 将完整数据集的70%作为训练集，30%作为测试集，并使得测试集和训练集

Dataset.from_tensor_slices(ds['train']) test_dataset = tf.data.Dataset.from_tensor_slices(ds['test']) As usual, we will start off by creating our training and test datasets. BATCH_SIZE = 256 batched_train batched_train = train_dataset.shuffle(train_dataset.cardinality()).batch(BATCH_SIZE) batched_test = test_dataset.shuffle(test_dataset.cardinality()).batch(BATCH_SIZE) We will import the tensorflow_text library tables in the pre-trained model. We will use this pre-processing layer to tokenize our training and test datasets. # Check out the TF hub website for more preprocessors preprocessor = hub.KerasLayer(