layer_random_translation
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
Arguments | Description |
---|---|
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()