tf.keras.layers.experimental.preprocessing.RandomTranslation
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Randomly translate each image during training.
Inherits From: Layer
tf.keras.layers.experimental.preprocessing.RandomTranslation(
height_factor, width_factor, fill_mode='constant', interpolation='bilinear',
seed=None, name=None, **kwargs
)
Arguments |
height_factor
|
a positive float represented as fraction of value, or a tuple
of size 2 representing lower and upper bound for shifting vertically. When
represented as a single float, this value is used for both the upper and
lower bound. For instance, height_factor=(0.2, 0.3) results in an output
height varying in the range [original - 20%, original + 30%] .
height_factor=0.2 results in an output height varying in the range
[original - 20%, original + 20%] .
|
width_factor
|
a positive float represented as fraction of value, or a tuple
of size 2 representing lower and upper bound for shifting horizontally.
When represented as a single float, this value is used for both the upper
and lower bound.
|
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)
The input is extended by wrapping around to the opposite edge.
|
interpolation
|
Interpolation mode. Supported values: "nearest", "bilinear".
|
seed
|
Integer. Used to create a random seed.
|
name
|
A string, the name of the layer.
|
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.
|
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Last updated 2020-10-01 UTC.
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