TensorFlow 1 version
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View source on GitHub
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Softmax converts a vector of values to a probability distribution.
tf.keras.activations.softmax(
x, axis=-1
)
The elements of the output vector are in range (0, 1) and sum to 1.
Each 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) / tf.reduce_sum(exp(x)).
The input values in are the log-odds of the resulting probability.
Args | |
|---|---|
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). |
Examples:
Example 1: standalone usage
inputs = tf.random.normal(shape=(32, 10))outputs = tf.keras.activations.softmax(inputs)tf.reduce_sum(outputs[0, :]) # Each sample in the batch now sums to 1<tf.Tensor: shape=(), dtype=float32, numpy=1.0000001>
Example 2: usage in a Dense layer
layer = tf.keras.layers.Dense(32, activation=tf.keras.activations.softmax)