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 Test 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

y Siri”或任何其他单 词。在机器学习中，学习（learning）是一个训练模型的过程。通过这个过程，我们可以发现正确的参数集， 从而使模型强制执行所需的行为。换句话说，我们用数据训练（train）模型。如 图1.1.2所示，训练过程通常 包含如下步骤： 1. 从一个随机初始化参数的模型开始，这个模型基本没有“智能”； 2. 获取一些数据样本（例如，音频片段以及对应的是或否标签）； lr, batch_size) # 使用参数的梯度更新参数 with torch.no_grad(): train_l = loss(net(features, w, b), labels) print(f'epoch {epoch + 1}, loss {float(train_l.mean()):f}') 3.2. 线性回归的从零开始实现 99 epoch 1, loss 0.042790 将features和labels作为API的参数传递，并通过数据迭代 器指定batch_size。此外，布尔值is_train表示是否希望数据迭代器对象在每个迭代周期内打乱数据。 def load_array(data_arrays, batch_size, is_train=True): #@save """构造一个PyTorch数据迭代器""" dataset = data.Tenso

deep learning models as well which can learn complex and non-linear decision boundaries. We can train a deep learning model using the animals’ embedding as the input. From the perspective of training for (a piece of text, audio, image, video, or some abstract concept). Here is a quick recipe to train embedding-based models: 1. Embedding Table Generation: Generate the embeddings for the inputs using table. 4 Support Vector Machine - https://en.wikipedia.org/wiki/Support-vector_machine 3. Train the model: Train the model for the task at hand5 with the embeddings as input. Refer to Figure 4-4 that describes

future prediction purpose. If not, then modify or choose new algorithm / model and finally, again train, predict and evaluate the model. Repeat the process until the best algorithm (model) is found. Non-trainable params: 0 _________________________________________________________________ >>> Train and Predict the model Model provides function for training, evaluation and prediction process. They They are as follows:  compile: Configure the learning process of the model  fit: Train the model using the training data  evaluate: Evaluate the model using the test data  predict: Predict

training process requires fewer samples to achieve the same performance, which makes it cheaper to train. Refer to Figure 3-1 for an example of such a model, and note how it achieves accuracy similar to 2. We want the highest possible accuracy with the original training costs: We can let the model train with the new learning techniques. In many cases, this will improve performance. Let’s say that the Spanish to English. This model translates “Estoy muy bien” to “I am fine”. This result can be used to train our original English to Spanish translation model. Let’s dig deeper into each of these categories

，它采样自均 值为 0，标准差为 0.01 的高斯分布： ? = 1. ? + . + ?, ? ∼ ?( , . 12) 通过随机采样? = 1 次，可以获得?个样本的训练数据集?train，代码如下： data = []# 保存样本集的列表 for i in range(100): # 循环采样 100 个点 x = np.random.uniform(-10. 字的手写图片，每种数字有 7000 张图片，它们采集自不同书写风格的真实手写图片，数据 预览版202112 第 3 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 batch_size = 512 # 批大小 # 训练数据集,自动从网络下载 MNIST 数据集，保存至 mnist_data 文件夹 train_db=torchvision.datasets.MNIST('mnist_data', train=True, download=True, # 图片的预处理步骤

algorithm, at least in the worst case2. Furthermore, I do not believe that the algorithm will be able to train enough with a static amount of episodes. 2 Ethics Clearly, solving nim with for example, reinforcement and is taught how to do this, by getting rewards and punishments. It is based upon how one might train an animal with treats and such, to do a trick. 4.1.1 The Principle The logic behind reinforcement the expected output from neuron j. η is the training variable, which is just adjusted as needed to train as one wish. wij ← wij − η(yj − tj) ∗ xi (1) 1 input x output y bias 0 1 2 3 ... i 1 2

4.2.3.4 predict . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 4.2.3.5 train_on_batch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 4.2.3.6 test_on_batch . . 4.3.3.4 predict . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 4.3.3.5 train_on_batch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 4.3.3.6 test_on_batch . . 现在，你可以批量地在训练数据上进行迭代了： # x_train 和 y_train 是 Numpy 数组 -- 就像在 Scikit-Learn API 中一样。 model.fit(x_train, y_train, epochs=5, batch_size=32) 或者，你可以手动地将批次的数据提供给模型： model.train_on_batch(x_batch, y_batch)

upload -n paddlempc -m train_dataA4.csv -i ./train_dataA.csv --ext '{"FileType":"csv","Features":"id,CRIM,ZN,INDUS,CHAS,NOX,RM", "TotalRows":457}' -e '2021-12-10 12:00:00' -d 'train_dataA4' --keyPath 发布训练任务 训练任务由计算需求方发起： $ ./requester-cli task publish -a "linear-vl" -l "MEDV" --keyPath './keys' -t "train" -n "房价预测任务v3" -d "hahahha" -p "id,id" --conf ./testdata/executor/node1/conf/config.toml -f "01e 训练的目标特征 –keyPath: 默认取值’./keys’, 从该文件夹中读取私钥, 计算需求方的私钥, 表 明了计算需求方的身份, 可以用-k 参数直接指定私钥 -t: 任务类型, 可选训练任务’train’ 或预测任务 ‘predict’ -n: 任务名称 -d: 任务描述 -p: PSI求交时使用的标签 –conf: 使用的配置文件 -f: 训练使用的文件ID, 这里是一个列表, 指明了各个任务执行方需要使用

upload -n paddlempc -m train_dataA4.csv -i ./train_dataA.csv --ext '{"FileType":"csv","Features":"id,CRIM,ZN,INDUS,CHAS,NOX,RM", "TotalRows":457}' -e '2021-12-10 12:00:00' -d 'train_dataA4' --keyPath 发布训练任务 训练任务由计算需求方发起： $ ./requester-cli task publish -a "linear-vl" -l "MEDV" --keyPath './keys' -t "train" -n "房价预测任务v3" -d "hahahha" -p "id,id" --conf ./testdata/executor/node1/conf/config.toml -f "01e ‘–ev’、’–evRule’等参数： $ ./requester-cli task publish -a "linear-vl" -l "MEDV" --keyPath './keys' -t "train" -n "房价预测任务v3" -d "hahahha" -p "id,id" --conf ./testdata/executor/node1/conf/config.toml -f "01e