Softmax converts a vector of values to a probability distribution.

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

Each input 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) / sum(exp(x)).

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

Usage

activation_softmax(x, axis = -1L)

Arguments

x

Input tensor.

axis

Integer, axis along which the softmax is applied.

Value

A tensor, the result from applying the activation to the input tensor x.

See also

• https://keras.io/api/layers/activations#softmax-function

Other activations:
activation_elu()
activation_exponential()
activation_gelu()
activation_hard_sigmoid()
activation_leaky_relu()
activation_linear()
activation_log_softmax()
activation_mish()
activation_relu()
activation_relu6()
activation_selu()
activation_sigmoid()
activation_silu()
activation_softplus()
activation_softsign()
activation_tanh()