# tf.keras.activations.softmax

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)).

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` . |