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. |