tf.compat.v1.math.softmax | TensorFlow v2.16.1 (original) (raw)
Computes softmax activations.
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tf.compat.v1.math.softmax(
logits, axis=None, name=None, dim=None
)
Used in the notebooks
| Used in the tutorials |
|---|
| Fitting Dirichlet Process Mixture Model Using Preconditioned Stochastic Gradient Langevin Dynamics Classify Flowers with Transfer Learning |
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. |