{ }
View source on GitHub |
A preprocessing layer which randomly rotates images during training.
Inherits From: Layer
, Operation
tf.keras.layers.RandomRotation(
factor,
fill_mode='reflect',
interpolation='bilinear',
seed=None,
fill_value=0.0,
value_range=(0, 255),
data_format=None,
**kwargs
)
Used in the notebooks
Used in the guide | Used in the tutorials |
---|---|
This layer will apply random rotations to each image, filling empty space
according to fill_mode
.
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, pass training=True
when calling the layer.
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.
Input shape | |
---|---|
3D
|
unbatched) or 4D (batched) tensor with shape
|
Output shape | |
---|---|
3D
|
unbatched) or 4D (batched) tensor with shape
|
Methods
from_config
@classmethod
from_config( config )
Creates a layer from its config.
This method is the reverse of get_config
,
capable of instantiating the same layer from the config
dictionary. It does not handle layer connectivity
(handled by Network), nor weights (handled by set_weights
).
Args | |
---|---|
config
|
A Python dictionary, typically the output of get_config. |
Returns | |
---|---|
A layer instance. |
symbolic_call
symbolic_call(
*args, **kwargs
)