chainer.functions.softmax¶

chainer.functions.softmax(x, axis=1)[source]¶

Softmax function.

This function computes its softmax along an axis. Let \(c = (c_1, c_2, \dots, c_D)\) be the slice of x along with the axis. For each slice \(c\), it computes the function \(f(c)\) defined as \(f(c)={\exp(c) \over \sum_{d} \exp(c_d)}\).

Parameters:	x (`Variable` or `numpy.ndarray` or `cupy.ndarray`) – Input variable. A \(n\)-dimensional (\(n \geq 2\)) float array. axis (int) – The axis along which the softmax is to be computed.
Returns:	Output variable. A \(n\)-dimensional (\(n \geq 2\)) float array, which is the same shape with x.
Return type:	Variable

Example

>>> x = np.array([[0, 1, 2], [0, 2, 4]], 'f')
>>> x
array([[ 0.,  1.,  2.],
       [ 0.,  2.,  4.]], dtype=float32)
>>> y = F.softmax(x, axis=1)
>>> y.data
array([[ 0.09003057,  0.24472848,  0.66524094],
       [ 0.01587624,  0.11731043,  0.86681336]], dtype=float32)
>>> F.sum(y, axis=1).data
array([ 1.,  1.], dtype=float32)