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Randomly rotate each image.
Inherits From: Layer
tf.keras.layers.experimental.preprocessing.RandomRotation(
factor, fill_mode='reflect', interpolation='bilinear', seed=None, name=None,
**kwargs
)
By default, random rotations are only applied during training.
At inference time, the layer does nothing. If you need to apply random
rotations at inference time, set training to True when calling the layer.
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'.
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 either bound is not between [0, 1], or upper bound is less than lower bound. |
Attributes | |
|---|---|
factor
|
a float represented as fraction of 2pi, or a tuple of size
2 representing lower and upper bound for rotating clockwise and
counter-clockwise. A positive values means rotating counter clock-wise,
while a negative value means clock-wise. When represented as a single
float, this value is used for both the upper and lower bound. For
instance, factor=(-0.2, 0.3) results in an output
rotation by a random amount in the range [-20% * 2pi, 30% * 2pi].
factor=0.2 results in an output rotating by a random amount in the range
[-20% * 2pi, 20% * 2pi].
|
fill_mode
|
Points outside the boundaries of the input are filled according
to the given mode (one of {'constant', 'reflect', 'wrap'}).
|
interpolation
|
Interpolation mode. Supported values: "nearest", "bilinear". |
seed
|
Integer. Used to create a random seed. |
name
|
A string, the name of the layer. |