megengine.functional.nn.softmax#
- softmax(inp, axis=None)[源代码]#
Applies a \(\text{softmax}(x)\) function. \(\text{softmax}(x)\) is defined as:
\[\text{softmax}(x_{i}) = \frac{\exp(x_i)}{\sum_j \exp(x_j)}\]It is applied to all elements along axis, and rescales elements so that they stay in the range [0, 1] and sum to 1.
更多细节见
Softmax
。实际案例
>>> import numpy as np >>> x = Tensor(np.arange(-5, 5, dtype=np.float32)).reshape(2,5) >>> out = F.softmax(x) >>> out.numpy().round(decimals=4) array([[0.0117, 0.0317, 0.0861, 0.2341, 0.6364], [0.0117, 0.0317, 0.0861, 0.2341, 0.6364]], dtype=float32)
- 返回类型: