{ }
View source on GitHub |
A preprocessing layer which randomly zooms images during training.
Inherits From: Layer
, Operation
tf.keras.layers.RandomZoom(
height_factor,
width_factor=None,
fill_mode='reflect',
interpolation='bilinear',
seed=None,
fill_value=0.0,
data_format=None,
**kwargs
)
Used in the notebooks
Used in the guide | Used in the tutorials |
---|---|
This layer will randomly zoom in or out on each axis of an image
independently, 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.
Input shape | |
---|---|
3D
|
unbatched) or 4D (batched) tensor with shape
|
Output shape | |
---|---|
3D
|
unbatched) or 4D (batched) tensor with shape
|
Example:
input_img = np.random.random((32, 224, 224, 3))
layer = keras.layers.RandomZoom(.5, .2)
out_img = layer(input_img)
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
)