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.
|
TensorFlow 2 version
View source on GitHub