A preprocessing layer which randomly rotates images during training.

This layer will apply random rotations to each image, filling empty space according to fill_mode.

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, pass training = TRUE when calling the layer.

Input pixel values can be of any range (e.g. [0., 1.) or [0, 255]) and of integer or floating point dtype. By default, the layer will output floats.

Note: This layer is safe to use inside a tf.data pipeline (independently of which backend you're using).

Usage

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

Arguments

object

Object to compose the layer with. A tensor, array, or sequential model.

factor

a float represented as fraction of 2 Pi, or a tuple 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% * 360, 30% * 360]. factor=0.2 results in an output rotating by a random amount in the range [-20% * 360, 20% * 360].

fill_mode

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

Interpolation mode. Supported values: "nearest", "bilinear".

seed

Integer. Used to create a random seed.

fill_value

a float represents the value to be filled outside the boundaries when fill_mode="constant".

data_format

string, either "channels_last" or "channels_first". The ordering of the dimensions in the inputs. "channels_last" corresponds to inputs with shape (batch, height, width, channels) while "channels_first" corresponds to inputs with shape (batch, channels, height, width). It defaults to the image_data_format value found in your Keras config file at ~/.keras/keras.json. If you never set it, then it will be "channels_last".

...

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 calling layer(input) is returned.
NULL or missing, then a Layer instance is returned.

Input Shape

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

Output Shape