mindspore.ops.one_hot
- mindspore.ops.one_hot(indices, depth, on_value=1, off_value=0, axis=- 1)[source]
Computes a one-hot tensor.
The locations represented by indices in indices take value on_value, while all other locations take value off_value.
Note
If the input indices has rank N, the output will have rank N+1. The new axis is created at dimension axis. On Ascend, if on_value is int64 dtype, indices must be int64 dtype, and the value for on_value and off_value can only be 1 and 0.
- Parameters
indices (Tensor) – A tensor of indices. Tensor of shape \((X_0, \ldots, X_n)\). Data type must be int32 or int64.
depth (int) – A scalar defining the depth of the one-hot dimension.
on_value (Union[Tensor, int, float], optional) – A value to fill in output when indices[j] = i. Data type must be int32, int64, float16 or float32. Default:
1
.off_value (Union[Tensor, int, float], optional) – A value to fill in output when indices[j] != i. Has the same data type as on_value. Default:
0
.axis (int, optional) – Position to insert the value. e.g. If shape of self is \((N, C)\), and axis is -1, the output shape will be \((N, C, depth)\), If axis is 0, the output shape will be \((depth, N, C)\). Default:
-1
.
- Returns
Tensor, one-hot tensor. Tensor of shape \((X_0, \ldots, X_{axis}, \text{depth} ,X_{axis+1}, \ldots, X_n)\), and it has the same data type as on_value.
- Raises
TypeError – If axis or depth is not an int.
TypeError – If dtype of indices is not int32 or int64.
TypeError – If dtype of on_value is not int32, int64, float16 or float32.
TypeError – If indices, on_value or off_value is not a Tensor.
ValueError – If axis is not in range [-1, ndim].
ValueError – If depth is less than 0.
- Supported Platforms:
Ascend
GPU
CPU
Examples
>>> import mindspore >>> import numpy as np >>> from mindspore import Tensor, ops >>> indices = Tensor(np.array([0, 1, 2]), mindspore.int32) >>> depth, on_value, off_value = 3, Tensor(1.0, mindspore.float32), Tensor(0.0, mindspore.float32) >>> output = ops.one_hot(indices, depth, on_value, off_value, axis=-1) >>> print(output) [[1. 0. 0.] [0. 1. 0.] [0. 0. 1.]]