Parametric Rectified Linear Unit.
It follows: f(x) = alpha * x`` for x < 0, f(x) = xforx >= 0', where
alpha is a learned array with the same shape as x.
layer_activation_parametric_relu( object, alpha_initializer = "zeros", alpha_regularizer = NULL, alpha_constraint = NULL, shared_axes = NULL, input_shape = NULL, batch_input_shape = NULL, batch_size = NULL, dtype = NULL, name = NULL, trainable = NULL, weights = NULL )
object |
Model or layer object |
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, set shared_axes=c(1, 2). |
input_shape |
Input shape (list of integers, does not include the samples axis) which is required when using this layer as the first layer in a model. |
batch_input_shape |
Shapes, including the batch size. For instance,
|
batch_size |
Fixed batch size for layer |
dtype |
The data type expected by the input, as a string ( |
name |
An optional name string for the layer. Should be unique in a model (do not reuse the same name twice). It will be autogenerated if it isn't provided. |
trainable |
Whether the layer weights will be updated during training. |
weights |
Initial weights for layer. |
Other activation layers:
layer_activation_elu(),
layer_activation_leaky_relu(),
layer_activation_relu(),
layer_activation_selu(),
layer_activation_softmax(),
layer_activation_thresholded_relu(),
layer_activation()
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