tf.keras.layers.PReLU

Parametric Rectified Linear Unit activation layer.

Inherits From: Layer, Operation

tf.keras.layers.PReLU(
    alpha_initializer='Zeros',
    alpha_regularizer=None,
    alpha_constraint=None,
    shared_axes=None,
    **kwargs
)

Formula:

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.

Args
`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=[1, 2]`.
`**kwargs`	Base layer keyword arguments, such as `name` and `dtype`.

Attributes
`input`	Retrieves the input tensor(s) of a symbolic operation. Only returns the tensor(s) corresponding to the first time the operation was called.
`output`	Retrieves the output tensor(s) of a layer. Only returns the tensor(s) corresponding to the first time the operation was called.

Attributes

input

Retrieves the input tensor(s) of a symbolic operation.

Only returns the tensor(s) corresponding to the first time the operation was called.

output

Retrieves the output tensor(s) of a layer.

Only returns the tensor(s) corresponding to the first time the operation was called.

Methods

`from_config`

View source

@classmethod
from_config(
    config
)

Creates a layer from its config.

This method is the reverse of get_config, capable of instantiating the same layer from the config dictionary. It does not handle layer connectivity (handled by Network), nor weights (handled by set_weights).

Args
`config`	A Python dictionary, typically the output of get_config.

Returns
A layer instance.

`symbolic_call`