PReLU
keras.layers.advanced_activations.PReLU(alpha_initializer='zeros', alpha_regularizer=None, alpha_constraint=None, shared_axes=None)
Parametric Rectified Linear Unit.
It follows:
f(x) = alpha * x for x < 0
,
f(x) = x for x >= 0
,
where alpha
is a learned array with the same shape as x.
Input shape
Arbitrary. Use the keyword argument input_shape
(tuple of integers, does not include the samples axis)
when using this layer as the first layer in a model.
Output shape
Same shape as the input.
Arguments
- alpha_initializer: initializer function for the weights.
- alpha_regularizer: regularizer for the weights.
- alpha_constraint: constraint for the weights.
- shared_axes: the axes along which to share learnable
parameters for the activation function.
For example, if the incoming feature maps
are from a 2D convolution
with output shape
(batch, height, width, channels)
, and you wish to share parameters across space so that each filter only has one set of parameters, setshared_axes=[1, 2]
.
References
ELU
keras.layers.advanced_activations.ELU(alpha=1.0)
Exponential Linear Unit.
It follows:
f(x) = alpha * (exp(x) - 1.) for x < 0
,
f(x) = x for x >= 0
.
Input shape
Arbitrary. Use the keyword argument input_shape
(tuple of integers, does not include the samples axis)
when using this layer as the first layer in a model.
Output shape
Same shape as the input.
Arguments
- alpha: scale for the negative factor.
References
ThresholdedReLU
keras.layers.advanced_activations.ThresholdedReLU(theta=1.0)
Thresholded Rectified Linear Unit.
It follows:
f(x) = x for x > theta
,
f(x) = 0 otherwise
.
Input shape
Arbitrary. Use the keyword argument input_shape
(tuple of integers, does not include the samples axis)
when using this layer as the first layer in a model.
Output shape
Same shape as the input.
Arguments
- theta: float >= 0. Threshold location of activation.
References
LeakyReLU
keras.layers.advanced_activations.LeakyReLU(alpha=0.3)
Leaky version of a Rectified Linear Unit.
It allows a small gradient when the unit is not active:
f(x) = alpha * x for x < 0
,
f(x) = x for x >= 0
.
Input shape
Arbitrary. Use the keyword argument input_shape
(tuple of integers, does not include the samples axis)
when using this layer as the first layer in a model.
Output shape
Same shape as the input.
Arguments
- alpha: float >= 0. Negative slope coefficient.
References