mindspore.nn.LogSoftmax

class mindspore.nn.LogSoftmax(axis=- 1)

Applies the LogSoftmax function to n-dimensional input tensor element-wise.

The input is transformed by the Softmax function and then by the log function to lie in range[-inf,0).

Logsoftmax is defined as:

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

Parameters

axis (int) – The axis to apply LogSoftmax operation, -1 means the last dimension. Default: -1 .

Inputs:

• x (Tensor) - The input of LogSoftmax, with float16 or float32 data type.

Outputs:

Tensor, which has the same type and shape as x with output values in the range[-inf,0).

Raises

• TypeError – If axis is not an int.

• TypeError – If dtype of x is neither float16 nor float32.

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

Supported Platforms:

Ascend GPU CPU

Examples

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