Apply an activation function to an output.

layer_activation(
object,
activation,
input_shape = NULL,
batch_input_shape = NULL,
batch_size = NULL,
dtype = NULL,
name = NULL,
trainable = NULL,
weights = NULL
)

## Arguments

object Model or layer object Name of activation function to use. If you don't specify anything, no activation is applied (ie. "linear" activation: a(x) = x). 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. 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. Fixed batch size for layer The data type expected by the input, as a string (float32, float64, int32...) 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. Whether the layer weights will be updated during training. Initial weights for layer.

Other core layers: layer_activity_regularization(), layer_attention(), layer_dense_features(), layer_dense(), layer_dropout(), layer_flatten(), layer_input(), layer_lambda(), layer_masking(), layer_permute(), layer_repeat_vector(), layer_reshape()
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_thresholded_relu()