def swish(x): """Swish activation function, `swish(x) = x * sigmoid(x)`. Swish activation function which returns `x*sigmoid(x)`. It is a smooth, non-monotonic function that consistently matches or outperforms ReLU on deep networks, it is unbounded above and bounded below. Example Usage: >>> a = tf.constant([-20, -1.0, 0.0, 1.0, 20], dtype = tf.float32) >>> b = tf.keras.activations.swish(a) >>> b.numpy() array([-4.1223075e-08, -2.6894143e-01, 0.0000000e+00, 7.3105860e-01, 2.0000000e+01], dtype=float32) Args: x: Input tensor. Returns: The swish activation applied to `x` (see reference paper for details). Reference: - [Ramachandran et al., 2017](https://arxiv.org/abs/1710.05941) """ return nn.swish(x)
def swish(x): """Swish activation function. Arguments: x: Input tensor. Returns: The swish activation applied to `x`. """ return nn.swish(x)
def swish(x): """Swish activation function. Arguments: x: Input tensor. Returns: The swish activation applied to `x` (see reference paper for details). Reference: - [Ramachandran et al., 2017](https://arxiv.org/abs/1710.05941) """ return nn.swish(x)