R: A preprocessing layer which randomly rotates images during...

layer_random_rotation {keras3}

R Documentation

A preprocessing layer which randomly rotates images during training.

Description

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,
  value_range = list(0L, 255L),
  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=(-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]⁠`.
`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"`.
`value_range`	see description
`data_format`	see description
`...`	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