mindspore.ops.OneHot

class mindspore.ops.OneHot(*args, **kwargs)[source]

Computes a one-hot tensor.

Makes a new tensor, whose locations represented by indices in 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.

Parameters

axis (int) – Position to insert the value. e.g. If indices shape 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.

Inputs:

  • 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 (Tensor) - A value to fill in output when indices[j] = i. With data type of float16 or float32.

  • off_value (Tensor) - A value to fill in output when indices[j] != i. Has the same data type with as on_value.

Outputs:

Tensor, one-hot tensor. Tensor of shape \((X_0, \ldots, X_{axis}, \text{depth} ,X_{axis+1}, \ldots, X_n)\).

Raises

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

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

  • TypeError – If indices, on_value or off_value is not a Tensor.

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

  • ValueError – If depth is less than 0.

Supported Platforms:

Ascend GPU CPU

Examples

>>> 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)
>>> onehot = ops.OneHot()
>>> output = onehot(indices, depth, on_value, off_value)
>>> print(output)
[[1. 0. 0.]
 [0. 1. 0.]
 [0. 0. 1.]]