predictions return self.seq_linear(x) ## Dataset ## • Dataset • In PyTorch, a dataset is represented by a regular Python class that inherits from the Dataset class. You can think of it as a kind of a Unless the dataset is huge (cannot fit in memory), you don’t explicitly need to define this class. Use TensorDataset from torch.utils.data import Dataset, TensorDataset class CustomDataset(Dataset): def Dataloader • What happens if we have a huge dataset? Have to train in 'batches' • Use PyTorch's Dataloader class! • We tell it which dataset to use, the desired mini-batch size and if we’d

项目进阶实战》视频课程 快速上手篇：动手训练模型和部署服务 ## 目录 - TensorFlow 2 开发环境搭建 - 使用 tf.keras.datasets 加载数据 - 使用 tf.data.Dataset 加载数据 - 使用 tf.keras.Model 管理模型 - Fashion MNIST 数据集介绍 - 使用 TensorFlow 2 训练分类网络 TensorFlow 2 开发环境搭建 regression dataset. cifar10 module: CIFAR10 small images classification dataset. cifar100 module: CIFAR100 small images classification dataset. fashion_mnist module: Fashion-MNIST dataset. imdb module: module: IMDB sentiment classification dataset. mnist module: MNIST handwritten digits dataset. reuters module: Reuters topic classification dataset. ##### 使用 tf.keras.datasets.mnist 数据集 File Notebook Editor

11.20.1 TDataLink.BOF ..... 372 11.20.1 TDataLink.BufferCount ..... 373 11.20.14 TDataLink.DataSet ..... 373 11.20.15 TDataLink.DataSource ..... 373 11.20.16 TDataLink.DataSourceFixed ..... 373 .... 386 11.21.30 DataSet.FindPrior ..... 386 11.21.31 DataSet.First ..... 387 11.21.32 DataSet.FreeBookmark ..... 387 11.21.33 DataSet.GetBookmark ..... 387 11.21.34 DataSet.GetCurrentRecord 388 11.21.35 DataSet.GetFieldList ..... 388 11.21.36 DataSet.GetFieldNames ..... 388 11.21.37 DataSet.GotoBookmark ..... 388 11.21.38 DataSet.Insert ..... 389 11.21.39 DataSet.InsertRecord .

with data augmentation in the next section. ## Data Augmentation Data Augmentation is a set of dataset manipulation techniques to improve sample and label efficiencies of deep learning models. Over the to overcome dataset shortcomings like: small size, skewed samples, or partial coverage. It is fair to ask: why don't we just get more data? Consider the following examples. MNIST dataset contains 70 pictures of their flukes $ ^{2} $ . The primary challenge with that dataset is the limited number of sample pictures for each whale. The dataset contains over 5000 individuals with more than 2000 having just

^{1} $ where the model needs to predict if a pair of sentences are semantically equivalent. The dataset has only 5800 labeled examples of pairs, which would be incredibly small for this task if we were pre-training and fine-tuning stages. In the figure we demonstrate pre-training with a large unlabeled dataset of animal images. The pre-trained model is then fine-tuned for downstream tasks, for example object Figure 6-1: Pre-training and fine-tuning stages. With an example of a large unlabeled animal images dataset which is used for pre-training. The pre-trained model is then used for fine-tuning for downstream

构建自己的数据集 19 4.1 自定义 Variable 数据与网络训练 19 4.2 准确率的可视化 22 4.3 分类结果的可视化 23 4.4 自定义 Dataset 数据集 25 ### 4.5 总结 Literature 28 ## 前言及简介 ![Image](/uploads/documents/6/f/9/5/6f95eb40bd0db3459 把数据输入这个环节进行模块化和独立化，对于简化模型训练很有好处。pytorch 中有两个模块是用来导入数据的：torch.utils.data.Dataset 以及 torch.utils.data.DataLoader。 Dataset 存储样本以及它们的标签等信息，Dataset 可以使用预加载的数据集（例如 mnist），也可以使用自定义的数据集；而 DataLoader 是把样本进行访问和索引的工具，它实现了迭代器功能，也就是说它可以依次将 包我们就不再重复导入了。 from torch.utils.data import Dataset from torch.utils.data import DataLoader 前面说过，Dataset 可以存储自定义数据，我们可以继承 Dataset 类，在子类中实现一些固定功能的函数，这样就相当于封装了自己的数据为 Dataset 类型。为了方便起见，我们先描述如何使用预加载数据，然后第二章就开始构建

better models faster. Let's say that we are optimizing the validation loss, L, for a given dataset $ D(x,y) $ on a model represented by a function f with a set of hyperparameters $ \lambda $ . winners. Let's start by importing the relevant libraries and creating a random classification dataset with 20 samples, each one assigned to one of the five target classes. import random import tensorflow Source: Hyperband In chapter 3, we trained a model to classify flowers in the oxford_flowers102 dataset. In the next section, we will retrain the same model but with a twist! ## Project: Oxford Flower

—— 古井50度玉液酒450ml | a month ago ## Try it! ## 应用：分类训练集与验证集划分 ## RP2K: A Large-Scale Retail Product Dataset for Fine-Grained Image Classification  ### 1. Abstract We introduce RP2K, a new large-scale retail product dataset for fine-grained image classification. Unlike previous datasets focusing on relatively few products Our dataset aims to advance the research in retail object recognition, which has massive applications such as automatic shelf auditing and image-based product information retrieval. Our dataset enjoys

Background: Prerequisites & What is Pytorch? • Training & Testing Neural Networks in Pytorch • Dataset & Dataloader • Tensors • torch.nn: Models, Loss Functions • torch.optim: Optimization • Save/load Training Load Data Validation Step 1. torch.utils.data.Dataset & torch.utils.data.DataLoader Testing ## Dataset & Dataloader • Dataset: stores data samples and expected values - Dataloader: multiprocessing dataset = MyDataset(file) ● dataloader = DataLoader(dataset, batch_size, shuffle=True) Training: True Testing: False More info about batches and shuffling here. ## Dataset & Dataloader

RL模块采用四层架构设计，如图11.3所示。 图11.3 HelloAgents Agentic RL架构 最底层是数据集层，包含GSM8KDataset类、create_sft_dataset()函数和create_rl_dataset()函数，负责数据加载和格式转换。第二层是奖励函数层，包含MathRewardFunction基类、AccuracyReward准确率奖励、LengthPenaltyRew pper，负责具体的训练逻辑和LoRA支持。最顶层是统一接口层，提供RLTrainingTool统一训练工具，支持四种操作：action=“train”(训练模型)、action=“load_dataset”(加载数据集)、action=“create_reward”(创建奖励函数)、action=“evaluate”(评估模型)。 11.1.5快速上手示例 在深入学习之前，让我们先快速体验一下完 "action": "load_dataset", "format": "sft", "max_samples": 5 # 只加载5个样本查看 }) sft_data = json.loads(sft_result) print(f"数据集大小: {sft_data['dataset_size']}")