layer_random_translation: Randomly translate each image during training

In keras: R Interface to 'Keras'

Randomly translate each image during training

Description

Randomly translate each image during training

Usage

layer_random_translation(
  object,
  height_factor,
  width_factor,
  fill_mode = "reflect",
  interpolation = "bilinear",
  seed = NULL,
  fill_value = 0,
  ...
)

Arguments

object	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.
height_factor	a float represented as fraction of value, or a list of size 2 representing lower and upper bound for shifting vertically. A negative value means shifting image up, while a positive value means shifting image down. When represented as a single positive float, this value is used for both the upper and lower bound. For instance, height_factor = c(-0.2, 0.3) results in an output shifted by a random amount in the range ⁠[-20%, +30%]⁠. height_factor = 0.2 results in an output height shifted by a random amount in the range ⁠[-20%, +20%]⁠.
width_factor	a float represented as fraction of value, or a list of size 2 representing lower and upper bound for shifting horizontally. A negative value means shifting image left, while a positive value means shifting image right. When represented as a single positive float, this value is used for both the upper and lower bound. For instance, width_factor = c(-0.2, 0.3) results in an output shifted left by 20%, and shifted right by 30%. width_factor = 0.2 results in an output height shifted left or right by 20%.
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".
...	standard layer arguments.

See Also

• https://www.tensorflow.org/api_docs/python/tf/keras/layers/RandomTranslation

• https://keras.io/api/layers/preprocessing_layers/

Other image augmentation layers: layer_random_brightness(), layer_random_contrast(), layer_random_crop(), layer_random_flip(), layer_random_height(), layer_random_rotation(), 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_rotation(), layer_random_width(), layer_random_zoom(), layer_rescaling(), layer_resizing(), layer_string_lookup(), layer_text_vectorization()

keras documentation built on Aug. 16, 2023, 1:07 a.m.