139 lines
3.7 KiB
Plaintext
139 lines
3.7 KiB
Plaintext
{
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"cells": [
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{
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"metadata": {},
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"cell_type": "markdown",
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"source": [
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"### Dataset类\n",
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"其实这就表示，无论使用自定义的数据集，还是官方为我们封装好的数据集，其本质都是继承了Dataset类。而在继承Dataset类时，至少需要重写以下几个方法：\n",
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"\n",
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"* \\_\\_init\\_\\_()：构造函数，可自定义数据读取方法以及进行数据预处理；\n",
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"* \\_\\_len\\_\\_()：返回数据集大小；\n",
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"* \\_\\_getitem\\_\\_()：索引数据集中的某一个数据。"
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],
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"id": "6f460291da56d507"
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-06-12T12:58:38.694696Z",
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"start_time": "2025-06-12T12:58:38.690734Z"
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}
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},
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"cell_type": "code",
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"source": [
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"import torch\n",
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"from torch.utils.data import Dataset\n",
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"\n",
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"\n",
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"class MyDataset(Dataset):\n",
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" # 构造函数\n",
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" def __init__(self, data_tensor, target_tensor):\n",
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" self.data_tensor = data_tensor\n",
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" self.target_tensor = target_tensor\n",
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"\n",
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" # 返回数据集大小\n",
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" def __len__(self):\n",
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" return self.data_tensor.size(0)\n",
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"\n",
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" # 返回索引的数据与标签\n",
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" def __getitem__(self, idx):\n",
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" return self.data_tensor[idx], self.target_tensor[idx]"
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],
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"id": "1244f4325aad0ac5",
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"outputs": [],
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"execution_count": 64
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-06-12T12:59:08.444392Z",
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"start_time": "2025-06-12T12:59:08.435084Z"
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}
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},
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"cell_type": "code",
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"source": [
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"# 生成数据\n",
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"data_tensor = torch.randn(10, 3)\n",
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"target_tensor = torch.randint(2, (10,)) # 二分类任务\n",
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"\n",
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"print(data_tensor)\n",
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"print(target_tensor)"
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],
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"id": "e2f8ad8dc637791a",
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"tensor([[-1.1016, 0.8803, 1.3645],\n",
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" [ 0.6674, 0.4500, 0.0367],\n",
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" [ 1.3528, 0.2438, 0.4721],\n",
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" [-1.3651, -1.1315, 0.0299],\n",
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" [ 1.4538, 1.3622, -0.5165],\n",
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" [ 0.7515, 0.6168, -0.9036],\n",
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" [ 1.3542, -0.3779, -1.2439],\n",
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" [-0.4588, -0.2233, -0.3531],\n",
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" [-0.0515, 0.8951, 0.3544],\n",
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" [ 0.0181, 0.4075, -2.0888]])\n",
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"tensor([0, 0, 1, 0, 0, 1, 1, 1, 1, 0])\n"
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]
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}
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],
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"execution_count": 66
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-06-12T12:59:25.092189Z",
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"start_time": "2025-06-12T12:59:25.085985Z"
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}
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},
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"cell_type": "code",
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"source": [
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"# 创建数据集\n",
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"my_dataset = MyDataset(data_tensor, target_tensor)\n",
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"\n",
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"# 查看数据集大小\n",
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"print(\"dataset size：\",len(my_dataset))\n",
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"\n",
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"# 使用索引获取数据\n",
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"print(\"data:\", my_dataset[0]) # 获取第一个数据"
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],
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"id": "cd8f86852f54b0c5",
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"dataset size： 10\n",
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"data: (tensor([-1.1016, 0.8803, 1.3645]), tensor(0))\n"
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]
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}
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],
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"execution_count": 67
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 2
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython2",
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"version": "2.7.6"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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