tf.keras.layers.PReLU

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Parametric Rectified Linear Unit.

Inherits From: Layer

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

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, set shared_axes=[1, 2].