mindspore.ops.log_softmax
- mindspore.ops.log_softmax(logits, axis=- 1)[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
- Returns
Tensor, with the same type and shape as the logits.
- Raises
TypeError – If axis is not an int.
TypeError – If dtype of logits is neither float16 nor float32.
ValueError – If axis is not in range [-len(logits.shape), len(logits.shape)).
ValueError – If dimension of logits is less than 1.
- Supported Platforms:
Ascend
GPU
CPU
Examples
>>> import mindspore >>> import numpy as np >>> from mindspore import Tensor, ops >>> logits = Tensor(np.array([1, 2, 3, 4, 5]), mindspore.float32) >>> output = ops.log_softmax(logits) >>> print(output) [-4.4519143 -3.4519143 -2.4519143 -1.4519144 -0.4519144]