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tf.keras.activations.softmax

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

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.

`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`.

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