tf.keras.layers.RandomZoom

Randomly zoom each image during training.

Inherits From: Layer, Module

Used in the notebooks

Used in the guide Used in the tutorials

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="constant".

Example:

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

Input shape:

3D (unbatched) or 4D (batched) tensor with shape: (..., height, width, channels), in "channels_last" format.

Output shape:

3D (unbatched) or 4D (batched) tensor with shape: (..., height, width, channels), in "channels_last" format.