def leaky_relu(features, alpha=0.2, name=None, **kwargs):
r"""Apply the leaky rectified linear unit.
The **LeakyReLU** function is defined as:
.. math::
\text{LeakyReLU}(x) =
\begin{cases}
x, & \text{ if } x \geq 0 \\
\alpha * x, & \text{ otherwise }
\end{cases}
Parameters
----------
features : dragon.Tensor
The input tensor.
alpha : number, optional, default=0.2
The value to :math:`\alpha`.
name : str, optional
The operation name.
Returns
-------
dragon.Tensor
The output tensor.
"""
return activation_ops.leaky_relu(features,
alpha=alpha,
name=name,
**kwargs)
def leaky_relu(x, alpha=0.2, name="leaky_relu", **kwargs):
r"""Apply the leaky rectified linear unit.
The **LeakyReLU** function is defined as:
.. math::
\text{LeakyReLU}(x) =
\begin{cases}
x, & \text{ if } x \geq 0 \\
\alpha * x, & \text{ otherwise }
\end{cases}
Examples:
```python
x = tl.layers.Input([10, 200])
y = tl.layers.Dense(
n_units=100,
act=tl.act.lrelu(x, 0.2),
name='dense',
)(x)
```
Parameters
----------
x : dragon.Tensor
The input tensor.
alpha : float, optional, default=0.2
The value to :math:`\alpha`.
name : str, optional
The operator name.
Returns
-------
dragon.Tensor
The output tensor.
"""
if not (0 <= alpha <= 1):
raise ValueError("`alpha` value must be in [0, 1]`")
return activation_ops.leaky_relu(x, alpha=alpha, name=name, **kwargs)
def relu(x, alpha=0, max_value=None, **kwargs):
r"""Apply the rectified linear unit to input.
`[Nair & Hinton, 2010] <http://www.csri.utoronto.ca/~hinton/absps/reluICML.pdf>`_.
The **ReLU** function is defined as:
.. math::
\text{ReLU}(x) =
\begin{cases}
\min(x, v_{max}), & \text{ if } x \geq 0 \\
\alpha * x, & \text{ otherwise }
\end{cases}
Examples:
```python
x = tf.constant([-1, 0, 1], 'float32')
print(tf.keras.activations.relu(x, inplace=False))
```
Parameters
----------
x : dragon.Tensor
The input tensor.
alpha : number, optional, default=0
The value to :math:`\alpha`.
max_value : number, optional
The value to :math:`v_{max}`.
"""
if max_value is not None:
if alpha != 0:
raise ValueError('Set either <alpha> or <max_value>.')
if max_value != 6:
raise ValueError('<max_value> can only be set to 6.')
return activation_ops.relu6(x, **kwargs)
return activation_ops.leaky_relu(x, alpha=alpha, **kwargs)
def __call__(self, bottom):
if self.negative_slope > 0:
return activation_ops.leaky_relu(bottom, self.negative_slope)
return activation_ops.relu(bottom)
def call(self, inputs):
return activation_ops.leaky_relu(inputs,
alpha=self.alpha,
inplace=self.inplace)