View source on GitHub |
Randomly rotate each image.
Inherits From: Layer
tf.keras.layers.experimental.preprocessing.RandomRotation(
factor, fill_mode='constant', 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 lower bound is not between [0, 1], or upper bound is negative. |
Attributes | |
---|---|
factor
|
a positive float represented as fraction of 2pi, or a tuple of size 2 representing lower and upper bound for rotating clockwise and counter-clockwise. When represented as a single float, lower = upper. |
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. |