Apply an activation function to an output.
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 )
object |
Model or layer object |
activation |
Name of activation function to use. If you don't specify anything, no activation is applied (ie. "linear" activation: a(x) = x). |
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_size |
Fixed batch size for layer |
dtype |
The data type expected by the input, as a string ( |
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. |
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()
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