|TensorFlow 2.0 version||View source on GitHub|
The softmax activation function transforms the outputs so that all values are in
tf.keras.activations.softmax( x, axis=-1 )
range (0, 1) and sum to 1. It is often used as the activation for the last layer of a classification network because the result could be interpreted as a probability distribution. The softmax of x is calculated by exp(x)/tf.reduce_sum(exp(x)).
x: Input tensor.
axis: Integer, axis along which the softmax normalization is applied.
Tensor, output of softmax transformation (all values are non-negative and sum to 1).
ValueError: In case
dim(x) == 1.