RandomZoom layer

RandomZoom class

tf.keras.layers.experimental.preprocessing.RandomZoom(
    height_factor,
    width_factor=None,
    fill_mode="reflect",
    interpolation="bilinear",
    seed=None,
    fill_value=0.0,
    **kwargs
)

Randomly zoom each image during training.

Arguments

  • height_factor: a float represented as fraction of value, or a tuple of size 2 representing lower and upper bound for zooming vertically. When represented as a single float, this value is used for both the upper and lower bound. A positive value means zooming out, while a negative value means zooming in. For instance, height_factor=(0.2, 0.3) result in an output zoomed out by a random amount in the range [+20%, +30%]. height_factor=(-0.3, -0.2) result in an output zoomed in by a random amount in the range [+20%, +30%].
  • width_factor: a float represented as fraction of value, or a tuple of size 2 representing lower and upper bound for zooming horizontally. When represented as a single float, this value is used for both the upper and lower bound. For instance, width_factor=(0.2, 0.3) result in an output zooming out between 20% to 30%. width_factor=(-0.3, -0.2) result in an output zooming in between 20% to 30%. Defaults to None, i.e., zooming vertical and horizontal directions by preserving the aspect ratio.
  • 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 is "constant".

Example

input_img = np.random.random((32, 224, 224, 3)) >>> layer = tf.keras.layers.experimental.preprocessing.RandomZoom(.5, .2) >>> out_img = layer(input_img) >>> out_img.shape TensorShape([32, 224, 224, 3])

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'.

Raise: ValueError: if lower bound is not between [0, 1], or upper bound is negative.