tf.nn.relu6

Computes Rectified Linear 6: min(max(features, 0), 6).

Compat aliases for migration

tf.compat.v1.nn.relu6

tf.nn.relu6(
    features, name=None
)

In comparison with tf.nn.relu, relu6 activation functions have shown to empirically perform better under low-precision conditions (e.g. fixed point inference) by encouraging the model to learn sparse features earlier. Source: Convolutional Deep Belief Networks on CIFAR-10: Krizhevsky et al., 2010.

x = tf.constant([-3.0, -1.0, 0.0, 6.0, 10.0], dtype=tf.float32)
y = tf.nn.relu6(x)
y.numpy()
array([0., 0., 0., 6., 6.], dtype=float32)

Args
`features`	A `Tensor` with type `float`, `double`, `int32`, `int64`, `uint8`, `int16`, or `int8`.
`name`	A name for the operation (optional).

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

References
Convolutional Deep Belief Networks on CIFAR-10: Krizhevsky et al., 2010 (pdf)

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Last updated 2023-10-06 UTC.

Args