文档反馈

问题文档片段

问题文档片段包含公式时，显示为空格。

提交类型

issue

有点复杂...

找人问问吧。

请选择提交类型

问题类型

规范和低错类

- 规范和低错类：

- 错别字或拼写错误，标点符号使用错误、公式错误或显示异常。

- 链接错误、空单元格、格式错误。

- 英文中包含中文字符。

- 界面和描述不一致，但不影响操作。

- 表述不通顺，但不影响理解。

- 版本号不匹配：如软件包名称、界面版本号。

易用性

- 易用性：

- 关键步骤错误或缺失，无法指导用户完成任务。

- 缺少主要功能描述、关键词解释、必要前提条件、注意事项等。

- 描述内容存在歧义指代不明、上下文矛盾。

- 逻辑不清晰，该分类、分项、分步骤的没有给出。

正确性

- 正确性：

- 技术原理、功能、支持平台、参数类型、异常报错等描述和软件实现不一致。

- 原理图、架构图等存在错误。

- 命令、命令参数等错误。

- 代码片段错误。

- 命令无法完成对应功能。

- 界面错误，无法指导操作。

- 代码样例运行报错、运行结果不符。

风险提示

- 风险提示：

- 对重要数据或系统存在风险的操作，缺少安全提示。

内容合规

- 内容合规：

- 违反法律法规，涉及政治、领土主权等敏感词。

- 内容侵权。

请选择问题类型

问题描述

点击输入详细问题描述，以帮助我们快速定位问题。

提交

文档反馈

mindspore.nn.OneHot

class mindspore.nn.OneHot(axis=-1, depth=1, on_value=1.0, off_value=0.0, dtype=mstype.float32)

Returns a one-hot tensor.

The locations represented by indices in argument indices take value on_value, while all other locations take value off_value.

Note

If the input indices is rank $N$, the output will have rank $N+1$. The new axis is created at dimension axis.

If indices is a scalar, the output shape will be a vector of length depth.

If indices is a vector of length features, the output shape will be:

features * depth if axis == -1

depth * features if axis == 0

If indices is a matrix with shape [batch, features], the output shape will be:

batch * features * depth if axis == -1

batch * depth * features if axis == 1

depth * batch * features if axis == 0

Parameters

• axis (int) – Features x depth if axis is -1, depth x features if axis is 0. Default: -1.

• depth (int) – A scalar defining the depth of the one hot dimension. Default: 1.

• on_value (float) – A scalar defining the value to fill in output[i][j] when indices[j] = i. Default: 1.0.

• off_value (float) – A scalar defining the value to fill in output[i][j] when indices[j] != i. Default: 0.0.

• dtype (mindspore.dtype) – Data type of ‘on_value’ and ‘off_value’, not the data type of indices. Default: mindspore.float32.

Inputs:

• indices (Tensor) - A tensor of indices with data type of int32 or int64. The shape is $(N,\ast )$ where $\ast$ means, any number of additional dimensions.

Outputs:

Tensor, the one-hot tensor of data type dtype with dimension at axis expanded to depth and filled with on_value and off_value. The dimension of the Outputs is equal to the dimension of the indices plus one.

Raises

• TypeError – If axis or depth is not an int.

• TypeError – If dtype of indices is neither int32 nor int64.

• ValueError – If axis is not in range [-1, len(indices_shape)].

• ValueError – If depth is less than 0.

Supported Platforms:

Ascend GPU CPU

Examples

>>> # 1st sample: add new coordinates at axis 1
>>> net = nn.OneHot(depth=4, axis=1)
>>> indices = Tensor([[1, 3], [0, 2]], dtype=mindspore.int32)
>>> output = net(indices)
>>> print(output)
[[[0. 0.]
  [1. 0.]
  [0. 0.]
  [0. 1.]]
 [[1. 0.]
  [0. 0.]
  [0. 1.]
  [0. 0.]]]
>>> # The results are shown below:
>>> print(output.shape)
(2, 4, 2)
>>> # 2nd sample: add new coordinates at axis 0
>>> net = nn.OneHot(depth=4, axis=0)
>>> indices = Tensor([[1, 3], [0, 2]], dtype=mindspore.int32)
>>> output = net(indices)
>>> print(output)
[[[0. 0.]
  [1. 0.]]
 [[1. 0.]
  [0. 0.]]
 [[0. 0.]
  [0. 1.]]
 [[0. 1.]
  [0. 0.]]]
>>> # The results are shown below:
>>> print(output.shape)
(4, 2, 2)
>>> # 3rd sample: add new coordinates at the last dimension.
>>> net = nn.OneHot(depth=4, axis=-1)
>>> indices = Tensor([[1, 3], [0, 2]], dtype=mindspore.int32)
>>> output = net(indices)
>>> # The results are shown below:
>>> print(output)
[[[0. 1. 0. 0.]
  [0. 0. 0. 1.]]
 [[1. 0. 0. 0.]
  [0. 0. 1. 0.]]]
>>> print(output.shape)
(2, 2, 4)
>>> indices = Tensor([1, 3, 0, 2], dtype=mindspore.int32)
>>> output = net(indices)
>>> print(output)
[[0. 1. 0. 0.]
 [0. 0. 0. 1.]
 [1. 0. 0. 0.]
 [0. 0. 1. 0.]]
>>> print(output.shape)
(4, 4)