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The softmax activation function transforms the outputs so that all values are in

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.