mindspore.nn.LogSoftmax
- class mindspore.nn.LogSoftmax(axis=- 1)[source]
LogSoftmax activation function.
Applies the LogSoftmax function to n-dimensional input tensor.
The input is transformed by the Softmax function and then by the log function to lie in range[-inf,0).
Logsoftmax is defined as:
\[\text{logsoftmax}(x_i) = \log \left(\frac{\exp(x_i)}{\sum_{j=0}^{n-1} \exp(x_j)}\right),\]where \(x_{i}\) is the \(i\)-th slice in the given dimension of the input Tensor.
- 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 the input as x with 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
>>> 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]]