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.

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)

## Arguments

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_input_shape=c(10, 32)` indicates that the expected input will be
batches of 10 32-dimensional vectors. `batch_input_shape=list(NULL, 32)`
indicates batches of an arbitrary number of 32-dimensional vectors. |

batch_size |
Fixed batch size for layer |

dtype |
The data type expected by the input, as a string (`float32` ,
`float64` , `int32` ...) |

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. |

## See also