tf.keras.layers.experimental.preprocessing.RandomZoom

Randomly zoom each image during training.

Inherits From: Layer

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Compat aliases for migration

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tf.compat.v1.keras.layers.experimental.preprocessing.RandomZoom

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

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

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.