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 .
|
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Last updated 2020-10-01 UTC.
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