tf.nn.relu6 | TensorFlow v2.16.1 (original) (raw)
tf.nn.relu6
Computes Rectified Linear 6: min(max(features, 0), 6).
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.
For example:
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 2024-04-26 UTC.