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Computes softmax activations.
tf.nn.softmax(
logits, axis=None, name=None
)
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>
Returns | |
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A Tensor . Has the same type and shape as logits .
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Raises | |
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InvalidArgumentError
|
if logits is empty or axis is beyond the last
dimension of logits .
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