From 88d230d69d10882c398e3c756d37ba03570e00bc Mon Sep 17 00:00:00 2001 From: chengxiao <2335846400@qq.com> Date: Sat, 29 Aug 2020 10:40:45 +0800 Subject: [PATCH] Modify the official website of the data loading and enhancement of the tutorial. --- .../data_processing_and_augmentation.md | 12 ++++++------ .../data_processing_and_augmentation.md | 16 ++++++++-------- 2 files changed, 14 insertions(+), 14 deletions(-) diff --git a/tutorials/source_en/use/data_preparation/data_processing_and_augmentation.md b/tutorials/source_en/use/data_preparation/data_processing_and_augmentation.md index 47b04127..67852d64 100644 --- a/tutorials/source_en/use/data_preparation/data_processing_and_augmentation.md +++ b/tutorials/source_en/use/data_preparation/data_processing_and_augmentation.md @@ -248,7 +248,7 @@ def zip(self, datasets): ds2 = ds.GeneratorDataset(generator_func2, ["data2"]) ``` -2. Use `zip()` to combine the `data1` column of the dataset `ds1`and the `data2` column of the dataset `ds2` into the dataset `ds3`. +2. Use `zip()` to combine the `data` column of the dataset `ds1`and the `data2` column of the dataset `ds2` into the dataset `ds3`. ```python ds3 = ds.zip((ds1, ds2)) for data in ds3.create_dict_iterator(): @@ -256,11 +256,11 @@ def zip(self, datasets): ``` The output is as follows: ``` - {'data1': array([0, 1, 2], dtype=int64), 'data2': array([-3, -2, -1], dtype=int64)} - {'data1': array([1, 2, 3], dtype=int64), 'data2': array([-2, -1, 0], dtype=int64)} - {'data1': array([2, 3, 4], dtype=int64), 'data2': array([-1, 0, 1], dtype=int64)} - {'data1': array([3, 4, 5], dtype=int64), 'data2': array([0, 1, 2], dtype=int64)} - {'data1': array([4, 5, 6], dtype=int64), 'data2': array([1, 2, 3], dtype=int64)} + {'data': array([0, 1, 2], dtype=int64), 'data2': array([-3, -2, -1], dtype=int64)} + {'data': array([1, 2, 3], dtype=int64), 'data2': array([-2, -1, 0], dtype=int64)} + {'data': array([2, 3, 4], dtype=int64), 'data2': array([-1, 0, 1], dtype=int64)} + {'data': array([3, 4, 5], dtype=int64), 'data2': array([0, 1, 2], dtype=int64)} + {'data': array([4, 5, 6], dtype=int64), 'data2': array([1, 2, 3], dtype=int64)} ``` ## Data Augmentation During image training, especially when the dataset size is relatively small, you can preprocess images by using a series of data augmentation operations, thereby enriching the datasets. 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 60ef10d8..2ef086c6 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 @@ -54,7 +54,7 @@ ds1 = ds.MnistDataset(MNIST_DATASET_PATH, MNIST_SCHEMA) # Create MNIST dataset. ds1 = ds1.shuffle(buffer_size=10000) ds1 = ds1.batch(32, drop_remainder=True) ds1 = ds1.repeat(10) -``` +``` 上面操作先对数据进行混洗，再将每32条数据组成一个batch，最后将数据集重复10次。 下面将构造一个简单数据集`ds1`，并对其进行数据处理操作，来介绍各类数据处理操作的详细使用。 @@ -239,7 +239,7 @@ MindSpore提供`zip`函数，可将多个数据集合并成1个数据集。 ```python def zip(self, datasets): ``` -1. 采用前面构造数据集`ds1`的方法，构造1个数据集`ds2`。 +1. 参考前面`generator_func`函数构造数据集`ds1`的方法，定义`generator_func2`函数用于构造1个数据集`ds2`。 ```python def generator_func2(): for i in range(5): @@ -248,7 +248,7 @@ def zip(self, datasets): ds2 = ds.GeneratorDataset(generator_func2, ["data2"]) ``` -2. 通过`zip`将数据集`ds1`的`data1`列和数据集`ds2`的`data2`列合并成数据集`ds3`。 +2. 通过`zip`将数据集`ds1`的`data`列和数据集`ds2`的`data2`列合并成数据集`ds3`。 ```python ds3 = ds.zip((ds1, ds2)) for data in ds3.create_dict_iterator(): @@ -256,11 +256,11 @@ def zip(self, datasets): ``` 输出如下所示： ``` - {'data1': array([0, 1, 2], dtype=int64), 'data2': array([-3, -2, -1], dtype=int64)} - {'data1': array([1, 2, 3], dtype=int64), 'data2': array([-2, -1, 0], dtype=int64)} - {'data1': array([2, 3, 4], dtype=int64), 'data2': array([-1, 0, 1], dtype=int64)} - {'data1': array([3, 4, 5], dtype=int64), 'data2': array([0, 1, 2], dtype=int64)} - {'data1': array([4, 5, 6], dtype=int64), 'data2': array([1, 2, 3], dtype=int64)} + {'data': array([0, 1, 2], dtype=int64), 'data2': array([-3, -2, -1], dtype=int64)} + {'data': array([1, 2, 3], dtype=int64), 'data2': array([-2, -1, 0], dtype=int64)} + {'data': array([2, 3, 4], dtype=int64), 'data2': array([-1, 0, 1], dtype=int64)} + {'data': array([3, 4, 5], dtype=int64), 'data2': array([0, 1, 2], dtype=int64)} + {'data': array([4, 5, 6], dtype=int64), 'data2': array([1, 2, 3], dtype=int64)} ``` ## 数据增强 在图片训练中，尤其在数据集较小的情况下，用户可以通过一系列的数据增强操作对图片进行预处理，从而丰富了数据集。 -- GitLab