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Showing content from https://keras.io/api/layers/preprocessing_layers/image_augmentation/random_zoom below:

RandomZoom layer

[source]

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

A preprocessing layer which randomly zooms images during training.

This layer will randomly zoom in or out on each axis of an image independently, filling empty space according to fill_mode.

Input pixel values can be of any range (e.g. [0., 1.) or [0, 255]) and of integer or floating point dtype. By default, the layer will output floats.

Input shape

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

Output shape

3D (unbatched) or 4D (batched) tensor with shape: (..., target_height, target_width, channels), or (..., channels, target_height, target_width), in "channels_first" format.

Note: This layer is safe to use inside a tf.data pipeline (independently of which backend you're using).

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%. None means i.e., zooming vertical and horizontal directions by preserving the aspect ratio. Defaults to None.
• fill_mode: Points outside the boundaries of the input are filled according to the given mode. Available methods are "constant", "nearest", "wrap" and "reflect". Defaults to "reflect".
  • "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 specified by fill_value.
  • "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. Note that when using torch backend, "reflect" is redirected to "mirror" (c d c b | a b c d | c b a b) because torch does not support "reflect". Note that torch backend does not support "wrap".
• interpolation: Interpolation mode. Supported values: "nearest", "bilinear".
• seed: Integer. Used to create a random seed.
• fill_value: a float that represents the value to be filled outside the boundaries when fill_mode="constant".
• data_format: string, either "channels_last" or "channels_first". The ordering of the dimensions in the inputs. "channels_last" corresponds to inputs with shape (batch, height, width, channels) while "channels_first" corresponds to inputs with shape (batch, channels, height, width). It defaults to the image_data_format value found in your Keras config file at ~/.keras/keras.json. If you never set it, then it will be "channels_last".
• **kwargs: Base layer keyword arguments, such as name and dtype.

Example

>>> input_img = np.random.random((32, 224, 224, 3))
>>> layer = keras.layers.RandomZoom(.5, .2)
>>> out_img = layer(input_img)

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