layer_activation_relu {keras3} | R Documentation |
Rectified Linear Unit activation function layer.
Description
Formula:
f <- function(x, max_value = Inf, negative_slope = 0, threshold = 0) { x <- max(x,0) if (x >= max_value) max_value else if (threshold <= x && x < max_value) x else negative_slope * (x - threshold) }
Usage
layer_activation_relu(
object,
max_value = NULL,
negative_slope = 0,
threshold = 0,
...
)
Arguments
object |
Object to compose the layer with. A tensor, array, or sequential model. |
max_value |
Float >= 0. Maximum activation value. |
negative_slope |
Float >= 0. Negative slope coefficient.
Defaults to |
threshold |
Float >= 0. Threshold value for thresholded activation.
Defaults to |
... |
Base layer keyword arguments, such as |
Value
The return value depends on the value provided for the first argument.
If object
is:
a
keras_model_sequential()
, then the layer is added to the sequential model (which is modified in place). To enable piping, the sequential model is also returned, invisibly.a
keras_input()
, then the output tensor from callinglayer(input)
is returned.-
NULL
or missing, then aLayer
instance is returned.
Examples
relu_layer <- layer_activation_relu(max_value = 10, negative_slope = 0.5, threshold = 0) input <- array(c(-10, -5, 0.0, 5, 10)) result <- relu_layer(input) as.array(result)
## [1] -5.0 -2.5 0.0 5.0 10.0
See Also
Other activation layers:
layer_activation()
layer_activation_elu()
layer_activation_leaky_relu()
layer_activation_parametric_relu()
layer_activation_softmax()
Other layers:
Layer()
layer_activation()
layer_activation_elu()
layer_activation_leaky_relu()
layer_activation_parametric_relu()
layer_activation_softmax()
layer_activity_regularization()
layer_add()
layer_additive_attention()
layer_alpha_dropout()
layer_attention()
layer_average()
layer_average_pooling_1d()
layer_average_pooling_2d()
layer_average_pooling_3d()
layer_batch_normalization()
layer_bidirectional()
layer_category_encoding()
layer_center_crop()
layer_concatenate()
layer_conv_1d()
layer_conv_1d_transpose()
layer_conv_2d()
layer_conv_2d_transpose()
layer_conv_3d()
layer_conv_3d_transpose()
layer_conv_lstm_1d()
layer_conv_lstm_2d()
layer_conv_lstm_3d()
layer_cropping_1d()
layer_cropping_2d()
layer_cropping_3d()
layer_dense()
layer_depthwise_conv_1d()
layer_depthwise_conv_2d()
layer_discretization()
layer_dot()
layer_dropout()
layer_einsum_dense()
layer_embedding()
layer_feature_space()
layer_flatten()
layer_flax_module_wrapper()
layer_gaussian_dropout()
layer_gaussian_noise()
layer_global_average_pooling_1d()
layer_global_average_pooling_2d()
layer_global_average_pooling_3d()
layer_global_max_pooling_1d()
layer_global_max_pooling_2d()
layer_global_max_pooling_3d()
layer_group_normalization()
layer_group_query_attention()
layer_gru()
layer_hashed_crossing()
layer_hashing()
layer_identity()
layer_integer_lookup()
layer_jax_model_wrapper()
layer_lambda()
layer_layer_normalization()
layer_lstm()
layer_masking()
layer_max_pooling_1d()
layer_max_pooling_2d()
layer_max_pooling_3d()
layer_maximum()
layer_mel_spectrogram()
layer_minimum()
layer_multi_head_attention()
layer_multiply()
layer_normalization()
layer_permute()
layer_random_brightness()
layer_random_contrast()
layer_random_crop()
layer_random_flip()
layer_random_rotation()
layer_random_translation()
layer_random_zoom()
layer_repeat_vector()
layer_rescaling()
layer_reshape()
layer_resizing()
layer_rnn()
layer_separable_conv_1d()
layer_separable_conv_2d()
layer_simple_rnn()
layer_spatial_dropout_1d()
layer_spatial_dropout_2d()
layer_spatial_dropout_3d()
layer_spectral_normalization()
layer_string_lookup()
layer_subtract()
layer_text_vectorization()
layer_tfsm()
layer_time_distributed()
layer_torch_module_wrapper()
layer_unit_normalization()
layer_upsampling_1d()
layer_upsampling_2d()
layer_upsampling_3d()
layer_zero_padding_1d()
layer_zero_padding_2d()
layer_zero_padding_3d()
rnn_cell_gru()
rnn_cell_lstm()
rnn_cell_simple()
rnn_cells_stack()