View source on GitHub
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A preprocessing layer which randomly translates images during training.
tf.keras.layers.RandomTranslation(
height_factor,
width_factor,
fill_mode='reflect',
interpolation='bilinear',
seed=None,
fill_value=0.0,
**kwargs
)
This layer will apply random translations to each image during training,
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.
For an overview and full list of preprocessing layers, see the preprocessing guide.
Args |
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height_factor
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
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
{"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.interpolationInterpolation mode. Supported values: "nearest","bilinear".seedInteger. Used to create a random seed. fill_valuea float represents the value to be filled outside the boundaries when fill_mode="constant".
Input shape |
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3D
unbatched) or 4D (batched) tensor with shape(..., height, width, channels), in "channels_last" format.
Output shape |
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3D
unbatched) or 4D (batched) tensor with shape(..., height, width, channels), in "channels_last" format.
View source on GitHub