layer_random_brightness {keras3} | R Documentation |
A preprocessing layer which randomly adjusts brightness during training.
Description
This layer will randomly increase/reduce the brightness for the input RGB
images. At inference time, the output will be identical to the input.
Call the layer with training=TRUE
to adjust the brightness of the input.
Note: This layer is safe to use inside a tf.data
pipeline
(independently of which backend you're using).
Usage
layer_random_brightness(
object,
factor,
value_range = list(0L, 255L),
seed = NULL,
...
)
Arguments
object |
Object to compose the layer with. A tensor, array, or sequential model. |
factor |
Float or a list of 2 floats between -1.0 and 1.0. The factor is used to determine the lower bound and upper bound of the brightness adjustment. A float value will be chosen randomly between the limits. When -1.0 is chosen, the output image will be black, and when 1.0 is chosen, the image will be fully white. When only one float is provided, eg, 0.2, then -0.2 will be used for lower bound and 0.2 will be used for upper bound. |
value_range |
Optional list of 2 floats
for the lower and upper limit
of the values of the input data.
To make no change, use |
seed |
optional integer, for fixed RNG behavior. |
... |
For forward/backward compatability. |
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.
Inputs
3D (HWC) or 4D (NHWC) tensor, with float or int dtype. Input pixel
values can be of any range (e.g. [0., 1.)
or [0, 255]
)
Output
3D (HWC) or 4D (NHWC) tensor with brightness adjusted based on the
factor
. By default, the layer will output floats.
The output value will be clipped to the range [0, 255]
,
the valid range of RGB colors, and
rescaled based on the value_range
if needed.
Example
random_bright <- layer_random_brightness(factor=0.2, seed = 1) # An image with shape [2, 2, 3] image <- array(1:12, dim=c(2, 2, 3)) # Assume we randomly select the factor to be 0.1, then it will apply # 0.1 * 255 to all the channel output <- random_bright(image, training=TRUE) output
## tf.Tensor( ## [[[39.605797 43.605797 47.605797] ## [41.605797 45.605797 49.605797]] ## ## [[40.605797 44.605797 48.605797] ## [42.605797 46.605797 50.605797]]], shape=(2, 2, 3), dtype=float32)
See Also
Other image augmentation layers:
layer_random_contrast()
layer_random_crop()
layer_random_flip()
layer_random_rotation()
layer_random_translation()
layer_random_zoom()
Other preprocessing layers:
layer_category_encoding()
layer_center_crop()
layer_discretization()
layer_feature_space()
layer_hashed_crossing()
layer_hashing()
layer_integer_lookup()
layer_mel_spectrogram()
layer_normalization()
layer_random_contrast()
layer_random_crop()
layer_random_flip()
layer_random_rotation()
layer_random_translation()
layer_random_zoom()
layer_rescaling()
layer_resizing()
layer_string_lookup()
layer_text_vectorization()
Other layers:
Layer()
layer_activation()
layer_activation_elu()
layer_activation_leaky_relu()
layer_activation_parametric_relu()
layer_activation_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_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()