tf.nn.softmax

Computes softmax activations.

View aliases

Main aliases

tf.math.softmax

tf.nn.softmax(
    logits, axis=None, name=None
)

Used in the notebooks

Used in the guide	Used in the tutorials
Multilayer perceptrons for digit recognition with Core APIs TensorFlow basics Introduction to Tensors Training & evaluation with the built-in methods	Uncertainty-aware Deep Learning with SNGP Integrated gradients Custom training: walkthrough Image classification Simple audio recognition: Recognizing keywords

Used for multi-class predictions. The sum of all outputs generated by softmax is 1.

This function performs the equivalent of

softmax = tf.exp(logits) / tf.reduce_sum(tf.exp(logits), axis, keepdims=True)

Example usage:

softmax = tf.nn.softmax([-1, 0., 1.])
softmax
<tf.Tensor: shape=(3,), dtype=float32,
numpy=array([0.09003057, 0.24472848, 0.66524094], dtype=float32)>
sum(softmax)
<tf.Tensor: shape=(), dtype=float32, numpy=1.0>

Args
`logits`	A non-empty `Tensor`. Must be one of the following types: `half`, `float32`, `float64`.
`axis`	The dimension softmax would be performed on. The default is -1 which indicates the last dimension.
`name`	A name for the operation (optional).

Returns
A `Tensor`. Has the same type and shape as `logits`.

Raises
`InvalidArgumentError`	if `logits` is empty or `axis` is beyond the last dimension of `logits`.

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Licensed under the Creative Commons Attribution License 4.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/nn/softmax