Randomly rotate each image
layer_random_rotation(
object,
factor,
fill_mode = "reflect",
interpolation = "bilinear",
seed = NULL,
fill_value = 0,
...
)What to compose the new Layer instance with. Typically a
Sequential model or a Tensor (e.g., as returned by layer_input()).
The return value depends on object. If object is:
missing or NULL, the Layer instance is returned.
a Sequential model, the model with an additional layer is returned.
a Tensor, the output tensor from layer_instance(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.
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_brightness(),
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_brightness(),
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()