tf.keras.activations.softmax

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Softmax converts a real vector to a vector of categorical probabilities.

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tf.compat.v1.keras.activations.softmax

tf.keras.activations.softmax(
    x, axis=-1
)

The elements of the output vector are in range (0, 1) and sum to 1.

Each vector is handled independently. The axis argument sets which axis of the input the function is applied along.

Softmax 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 each vector x is computed as exp(x) / tf.reduce_sum(exp(x)).

The input values in are the log-odds of the resulting probability.

Arguments
x	Input tensor.
axis	Integer, axis along which the softmax normalization is applied.

Returns
Tensor, output of softmax transformation (all values are non-negative and sum to 1).

Raises
ValueError	In case dim(x) == 1.