layer_activation_parametric_relu
Parametric Rectified Linear Unit.
Description
It follows: f(x) = alpha * x`` forx < 0,f(x) = xforx >= 0`, where alpha is a learned array with the same shape as x.
Usage
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
| Arguments | Description |
|---|---|
| object | What to compose the new Layer instance with. Typically a Sequential model or a Tensor (e.g., as returned by layer_input()). The return value depends on object. If object is: - missing or NULL, the Layer instance is returned. - a Sequential model, the model with an additional layer is returned. - a Tensor, the output tensor from layer_instance(object) is returned. |
| 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
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. 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()