diff --git a/tutorials/source_en/use/data_preparation/converting_datasets.md b/tutorials/source_en/use/data_preparation/converting_datasets.md index 11ea33c866010d2275ede0a1831a1da03e1b1320..795d930d32418dde56c0c22c28b2259ab4542421 100644 --- a/tutorials/source_en/use/data_preparation/converting_datasets.md +++ b/tutorials/source_en/use/data_preparation/converting_datasets.md @@ -64,7 +64,7 @@ MindSpore provides write operation tools to write user-defined raw data in MindS ```python data = [{"file_name": "1.jpg", "label": 0, "data": b"\x10c\xb3w\xa8\xee$o&\xd4\x00\xf8\x129\x15\xd9\xf2q\xc0\xa2\x91YFUO\x1dsE1\x1ep"}, - {"file_name": "3.jpg", "label": 99, "data": b"\xaf\xafU<\xb8|6\xbd}\xc1\x99[\xeaj+\x8f\x84\xd3\xcc\xa0,i\xbb\xb9-\xcdz\xecp{T\xb1\xdb\"}] + {"file_name": "3.jpg", "label": 99, "data": b"\xaf\xafU<\xb8|6\xbd}\xc1\x99[\xeaj+\x8f\x84\xd3\xcc\xa0,i\xbb\xb9-\xcdz\xecp{T\xb1\xdb"}] ``` 4. Prepare index fields. Adding index fields can accelerate data reading. This step is optional. diff --git a/tutorials/source_en/use/data_preparation/loading_the_datasets.md b/tutorials/source_en/use/data_preparation/loading_the_datasets.md index 955183815b638e55e11961f83fe57370ab926e88..5a9462fc2e6506099854f79346532b25b9946eaf 100644 --- a/tutorials/source_en/use/data_preparation/loading_the_datasets.md +++ b/tutorials/source_en/use/data_preparation/loading_the_datasets.md @@ -149,7 +149,7 @@ MindSpore can also read datasets in the `TFRecord` data format through the `TFRe ``` ## Loading a Custom Dataset -In real scenarios, there are virous datasets. For a custom dataset or a dataset that can't be loaded by APIs directly, there are tow ways. +In real scenarios, there are various datasets. For a custom dataset or a dataset that can't be loaded by APIs directly, there are tow ways. One is converting the dataset to MindSpore data format (for details, see [Converting Datasets to the Mindspore Data Format](https://www.mindspore.cn/tutorial/en/master/use/data_preparation/converting_datasets.html)). The other one is using the `GeneratorDataset` object. The following shows how to use `GeneratorDataset`. diff --git a/tutorials/source_zh_cn/use/data_preparation/converting_datasets.md b/tutorials/source_zh_cn/use/data_preparation/converting_datasets.md index 550a58f434faa07e25ad609e167c406d869d4290..5d1c585f8d0813e4e939ed645c068f53c5d1ade6 100644 --- a/tutorials/source_zh_cn/use/data_preparation/converting_datasets.md +++ b/tutorials/source_zh_cn/use/data_preparation/converting_datasets.md @@ -66,7 +66,7 @@ MindSpore提供写操作工具，可将用户定义的原始数据写为MindSpor ```python data = [{"file_name": "1.jpg", "label": 0, "data": b"\x10c\xb3w\xa8\xee$o&\xd4\x00\xf8\x129\x15\xd9\xf2q\xc0\xa2\x91YFUO\x1dsE1\x1ep"}, - {"file_name": "3.jpg", "label": 99, "data": b"\xaf\xafU<\xb8|6\xbd}\xc1\x99[\xeaj+\x8f\x84\xd3\xcc\xa0,i\xbb\xb9-\xcdz\xecp{T\xb1\xdb\"}] + {"file_name": "3.jpg", "label": 99, "data": b"\xaf\xafU<\xb8|6\xbd}\xc1\x99[\xeaj+\x8f\x84\xd3\xcc\xa0,i\xbb\xb9-\xcdz\xecp{T\xb1\xdb"}] ``` 4. 准备索引字段，添加索引字段可以加速数据读取，该步骤非必选。 diff --git a/tutorials/source_zh_cn/use/data_preparation/data_processing_and_augmentation.md b/tutorials/source_zh_cn/use/data_preparation/data_processing_and_augmentation.md index c4ff2c70079775bfe7cd26cf113a61cd591b84cd..6605d9c57b821be5ccc157ebd141ba4a164b19e4 100644 --- a/tutorials/source_zh_cn/use/data_preparation/data_processing_and_augmentation.md +++ b/tutorials/source_zh_cn/use/data_preparation/data_processing_and_augmentation.md @@ -26,7 +26,7 @@ > 从本质上来说，数据增强是通过数据处理中的`map`（映射）进行实现，但是因为数据增强提供丰富的变换操作，所以将其单独提出进行描述。 ## MindSpore支持的数据处理操作 -MindSpore支持多种处理数据操作，包括复制、分批、洗牌、映射等等，详细见下表： +MindSpore支持多种处理数据操作，包括复制、分批、混洗、映射等等，详细见下表： | 数据处理 | 说明 | | -------- | -------------------------------------- | diff --git a/tutorials/source_zh_cn/use/data_preparation/loading_the_datasets.md b/tutorials/source_zh_cn/use/data_preparation/loading_the_datasets.md index 1c034ea0ee2544d0eeb326df84f57444562aebe2..becc74b36061bef2025ca695b815f2aad71956ed 100644 --- a/tutorials/source_zh_cn/use/data_preparation/loading_the_datasets.md +++ b/tutorials/source_zh_cn/use/data_preparation/loading_the_datasets.md @@ -30,7 +30,7 @@ MindSpore可以加载常见的标准数据集。支持的数据集如下表： | --------- | -------------------------------------------------------------------------------------------------------------------------- | | ImageNet | ImageNet是根据WordNet层次结构组织的图像数据库，其中层次结构的每个节点都由成百上千个图像表示。 | | MNIST | 是一个手写数字图像的大型数据库，通常用于训练各种图像处理系统。 | -| CIFAR-10 | 常用于训练图像的采集机器学习和计算机视觉算法。CIFAR-10数据集包含10种不同类别的60,000张32x32彩色图像。 | +| CIFAR-10 | 常用于机器学习和计算机视觉领域训练的图像集合。CIFAR-10数据集包含10种不同类别的60,000张32x32彩色图像。 | | CIFAR-100 | 该数据集类似于CIFAR-10，不同之处在于它有100个类别，每个类别包含600张图像：500张训练图像和100张测试图像。 | | PASCAL-VOC | 数据内容多样，可用于训练计算机视觉模型（分类、定位、检测、分割、动作识别等）。 | | CelebA | CelebA人脸数据集包含上万个名人身份的人脸图片，每张图片有40个特征标记，常用于人脸相关的训练任务。 |