forked from mindspore-Ecosystem/mindspore
!44128 modify doc
Merge pull request !44128 from xumengjuan1/code_docs_x12
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@ -44,7 +44,7 @@ mindspore.dataset.Graph
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获取图的所有边。
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参数：
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- **edge_type** (str) - 指定边的类型，Graph初始化未指定 `edge_type` 时，默认值为'0'。详见 `加载图数据集 <https://www.mindspore.cn/tutorials/zh-CN/master/advanced/dataset/augment_graph_data.html>`_ 。
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- **edge_type** (str) - 指定边的类型，Graph初始化未指定 `edge_type` 时，默认值为'0'。
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返回：
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numpy.ndarray，包含边的数组。
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@ -157,7 +157,7 @@ mindspore.dataset.Graph
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获取图中的所有节点。
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参数：
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- **node_type** (str) - 指定节点的类型。Graph初始化未指定 `node_type` 时，默认值为'0'。详见 `加载图数据集 <https://www.mindspore.cn/tutorials/zh-CN/master/advanced/dataset/augment_graph_data.html>`_ 。
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- **node_type** (str) - 指定节点的类型。Graph初始化未指定 `node_type` 时，默认值为'0'。
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返回：
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numpy.ndarray，包含节点的数组。
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@ -6,9 +6,6 @@ mindspore.dataset.GraphData
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从共享文件或数据库中读取用于GNN训练的图数据集。
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支持读取图数据集Cora、Citeseer和PubMed。
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关于如何将源数据集加载到mindspore中请参考 `加载图数据集 <https://www.mindspore.cn/tutorials/zh-CN/
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master/advanced/dataset/augment_graph_data.html>`_。
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参数：
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- **dataset_file** (str) - 数据集文件路径。
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- **num_parallel_workers** (int, 可选) - 读取数据的工作线程数，默认值：None，使用mindspore.dataset.config中配置的线程数。
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@ -36,7 +33,7 @@ mindspore.dataset.GraphData
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获取图的所有边。
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参数：
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- **edge_type** (int) - 指定边的类型，在数据集转换为MindRecord格式时，需要指定 `edge_type` 的值，并在此API中对应使用。详见 `加载图数据集 <https://www.mindspore.cn/tutorials/zh-CN/master/advanced/dataset/augment_graph_data.html>`_ 。
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- **edge_type** (int) - 指定边的类型，在数据集转换为MindRecord格式时，需要指定 `edge_type` 的值，并在此API中对应使用。
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返回：
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numpy.ndarray，包含边的数组。
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@ -149,7 +146,7 @@ mindspore.dataset.GraphData
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获取图中的所有节点。
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参数：
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- **node_type** (int) - 指定节点的类型。在数据集转换为MindRecord格式时，需要指定 `node_type` 的值，并在此API中对应使用。详见 `加载图数据集 <https://www.mindspore.cn/tutorials/zh-CN/master/advanced/dataset/augment_graph_data.html>`_ 。
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- **node_type** (int) - 指定节点的类型。在数据集转换为MindRecord格式时，需要指定 `node_type` 的值，并在此API中对应使用。
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返回：
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numpy.ndarray，包含节点的数组。
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@ -2,4 +2,4 @@
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由于此优化器没有 `loss_scale` 的参数，因此需要通过其他方式处理 `loss_scale` 。
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如何正确处理 `loss_scale` 详见 `LossScale <https://www.mindspore.cn/tutorials/experts/zh-CN/master/others/mixed_precision.html>`_。
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如何正确处理 `loss_scale` 详见 `LossScale <https://www.mindspore.cn/tutorials/zh-CN/master/advanced/mixed_precision.html>`_。
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@ -15,9 +15,6 @@
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"""
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This file contains basic classes that help users do flexible dataset loading.
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You can define your own dataset loading class, and use GeneratorDataset to help load data.
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You can refer to the
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`tutorial <https://www.mindspore.cn/tutorials/zh-CN/master/advanced/dataset/custom.html>`_
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to help define your dataset loading.
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After declaring the dataset object, you can further apply dataset operations
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(e.g. filter, skip, concat, map, batch) on it.
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"""
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@ -78,10 +78,6 @@ class GraphData:
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Reads the graph dataset used for GNN training from the shared file and database.
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Support reading graph datasets like Cora, Citeseer and PubMed.
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About how to load raw graph dataset into MindSpore please
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refer to `Loading Graph Dataset <https://www.mindspore.cn/tutorials/zh-CN/
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master/advanced/dataset/augment_graph_data.html>`_.
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Args:
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dataset_file (str): One of file names in the dataset.
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num_parallel_workers (int, optional): Number of workers to process the dataset in parallel
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@ -853,7 +853,7 @@ class AdamWeightDecay(Optimizer):
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There is usually no connection between a optimizer and mixed precision. But when `FixedLossScaleManager` is used
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and `drop_overflow_update` in `FixedLossScaleManager` is set to False, optimizer needs to set the 'loss_scale'.
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As this optimizer has no argument of `loss_scale`, so `loss_scale` needs to be processed by other means, refer
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document `LossScale <https://www.mindspore.cn/tutorials/experts/en/master/others/mixed_precision.html>`_ to
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document `LossScale <https://www.mindspore.cn/tutorials/zh-CN/master/advanced/mixed_precision.html>`_ to
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process `loss_scale` correctly.
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If parameters are not grouped, the `weight_decay` in optimizer will be applied on the network parameters without
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@ -133,7 +133,7 @@ class Lamb(Optimizer):
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There is usually no connection between a optimizer and mixed precision. But when `FixedLossScaleManager` is used
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and `drop_overflow_update` in `FixedLossScaleManager` is set to False, optimizer needs to set the 'loss_scale'.
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As this optimizer has no argument of `loss_scale`, so `loss_scale` needs to be processed by other means. Refer
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document `LossScale <https://www.mindspore.cn/tutorials/experts/zh-CN/master/others/mixed_precision.html>`_ to
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document `LossScale <https://www.mindspore.cn/tutorials/zh-CN/master/advanced/mixed_precision.html>`_ to
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process `loss_scale` correctly.
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If parameters are not grouped, the `weight_decay` in optimizer will be applied on the network parameters without
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