tf.keras.layers.experimental.preprocessing.RandomTranslation

Randomly translate each image during training.

Inherits From: Layer

height_factor a float represented as fraction of value, or a tuple 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=(-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 tuple 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=(-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'}).

  • 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.
interpolation Interpolation mode. Supported values: "nearest", "bilinear".
seed Integer. Used to create a random seed.
name A string, the name of the layer.

Input shape:

4D tensor with shape: (samples, height, width, channels), data_format='channels_last'.

Output shape:

4D tensor with shape: (samples, height, width, channels), data_format='channels_last'.