layer_activation_thresholded_relu
Thresholded Rectified Linear Unit.
Description
It follows: f(x) = x
for x > theta
, f(x) = 0
otherwise.
Usage
layer_activation_thresholded_relu(
object, theta = 1,
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. |
theta | float >= 0. Threshold location of activation. |
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
Zero-bias autoencoders and the benefits of co-adapting features. Other activation layers: layer_activation_elu()
, layer_activation_leaky_relu()
, layer_activation_parametric_relu()
, layer_activation_relu()
, layer_activation_selu()
, layer_activation_softmax()
, layer_activation()