Randomly rotate each image

layer_random_rotation(
object,
factor,
fill_mode = "reflect",
interpolation = "bilinear",
seed = NULL,
fill_value = 0,
...
)

## Arguments

object What to call the new Layer instance with. Typically a keras Model, another Layer, or a tf.Tensor/KerasTensor. If object is missing, the Layer instance is returned, otherwise, layer(object) is returned. a float represented as fraction of 2 Pi, or a list of size 2 representing lower and upper bound for rotating clockwise and counter-clockwise. A positive values means rotating counter clock-wise, while a negative value means clock-wise. When represented as a single float, this value is used for both the upper and lower bound. For instance, factor = c(-0.2, 0.3) results in an output rotation by a random amount in the range [-20% * 2pi, 30% * 2pi]. factor = 0.2 results in an output rotating by a random amount in the range [-20% * 2pi, 20% * 2pi]. Points outside the boundaries of the input are filled according to the given mode (one of {"constant", "reflect", "wrap", "nearest"}). reflect: (d c b a | a b c d | d c b a) The input is extended by reflecting about the edge of the last pixel. constant: (k k k k | a b c d | k k k k) The input is extended by filling all values beyond the edge with the same constant value k = 0. wrap: (a b c d | a b c d | a b c d) The input is extended by wrapping around to the opposite edge. nearest: (a a a a | a b c d | d d d d) The input is extended by the nearest pixel. Interpolation mode. Supported values: "nearest", "bilinear". Integer. Used to create a random seed. a float represents the value to be filled outside the boundaries when fill_mode="constant". standard layer arguments.

## Details

By default, random rotations are only applied during training. At inference time, the layer does nothing. If you need to apply random rotations at inference time, set training to TRUE when calling the layer.

Input shape: 3D (unbatched) or 4D (batched) tensor with shape: (..., height, width, channels), in "channels_last" format

Output shape: 3D (unbatched) or 4D (batched) tensor with shape: (..., height, width, channels), in "channels_last" format

Other image augmentation layers: layer_random_contrast(), layer_random_crop(), layer_random_flip(), layer_random_height(), layer_random_translation(), layer_random_width(), layer_random_zoom()
Other preprocessing layers: layer_category_encoding(), layer_center_crop(), layer_discretization(), layer_hashing(), layer_integer_lookup(), layer_normalization(), layer_random_contrast(), layer_random_crop(), layer_random_flip(), layer_random_height(), layer_random_translation(), layer_random_width(), layer_random_zoom(), layer_rescaling(), layer_resizing(), layer_string_lookup(), layer_text_vectorization()