mindspore.mint.nn.LogSoftmax

class mindspore.mint.nn.LogSoftmax(dim=None)[source]

Applies the Log Softmax function to the input tensor on the specified axis. Supposes a slice in the given axis, \(x\) for each element \(x_i\), the Log Softmax function is shown as follows:

\[\text{output}(x_i) = \log \left(\frac{\exp(x_i)} {\sum_{j = 0}^{N-1}\exp(x_j)}\right),\]

where \(N\) is the length of the Tensor.

Parameters: dim (int, optional) – The axis to perform the Log softmax operation. Default: None .
Returns: Tensor, with the same shape as the input.
Raises: ValueError – If dim is not in range [-len(input.shape), len(input.shape)).

Supported Platforms:: Ascend

Examples

>>> import mindspore
>>> from mindspore import Tensor, mint
>>> import numpy as np
>>> x = Tensor(np.array([[-1.0, 4.0, -8.0], [2.0, -5.0, 9.0]]), mindspore.float32)
>>> log_softmax = mint.nn.LogSoftmax(dim=-1)
>>> output = log_softmax(x)
>>> print(output)
[[-5.00672150e+00 -6.72150636e-03 -1.20067215e+01]
 [-7.00091219e+00 -1.40009127e+01 -9.12250078e-04]]