tf.nn.sampled_softmax

Computes scaled exponential linear: scale * alpha * (exp(features) - 1)

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Compat aliases for migration

tf.compat.v1.nn.selu

tf.nn.selu(
    features, name=None
)

if < 0, scale * features otherwise.

To be used together with initializer = tf.variance_scaling_initializer(factor=1.0, mode='FAN_IN'). For correct dropout, use tf.contrib.nn.alpha_dropout.

See Self-Normalizing Neural Networks

Args
`features`	A `Tensor`. Must be one of the following types: `half`, `bfloat16`, `float32`, `float64`.
`name`	A name for the operation (optional).

Returns
A `Tensor`. Has the same type as `features`.

TensorFlow

tf.nn / sampled_softmax_loss

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Args

Returns

TensorFlow

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tf.nn / sampled_softmax_loss

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Returns