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Crop the central portion of the images to target height and width.
Inherits From: PreprocessingLayer, Layer, Module
tf.keras.layers.experimental.preprocessing.CenterCrop(
height, width, name=None, **kwargs
)
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).
If the input height/width is even and the target height/width is odd (or inversely), the input image is left-padded by 1 pixel.
Arguments | |
|---|---|
height
|
Integer, the height of the output shape. |
width
|
Integer, the width of the output shape. |
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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