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tf.keras.activations.softmax

TensorFlow 2.0 version View source on GitHub

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

Aliases:

  • tf.compat.v1.keras.activations.softmax
  • tf.compat.v2.keras.activations.softmax
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)).

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.