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Randomly crop the images to target height and width.
Inherits From: PreprocessingLayer, Layer, Module
tf.keras.layers.experimental.preprocessing.RandomCrop(
height, width, seed=None, name=None, **kwargs
)
This layer will crop all the images in the same batch to the same cropping
location.
By default, random cropping is only applied during training. At inference
time, the images will be first rescaled to preserve the shorter side, and
center cropped. If you need to apply random cropping 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, target_height, target_width, channels).
Arguments | |
|---|---|
height
|
Integer, the height of the output shape. |
width
|
Integer, the width of the output shape. |
seed
|
Integer. Used to create a random seed. |
name
|
A string, the name of the layer. |
Methods
adapt
adapt(
data, reset_state=True
)
Fits the state of the preprocessing layer to the data being passed.
| Arguments | |
|---|---|
data
|
The data to train on. It can be passed either as a tf.data Dataset, or as a numpy array. |
reset_state
|
Optional argument specifying whether to clear the state of
the layer at the start of the call to adapt, or whether to start
from the existing state. This argument may not be relevant to all
preprocessing layers: a subclass of PreprocessingLayer may choose to
throw if 'reset_state' is set to False.
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