Cropping layer for 2D input (e.g. picture).
Inherits From: Layer
, Module
View aliases
Compat aliases for migration
See
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more details.
`tf.compat.v1.keras.layers.Cropping2D`
tf.keras.layers.Cropping2D(
cropping=((0, 0), (0, 0)), data_format=None, **kwargs
)
It crops along spatial dimensions, i.e. height and width.
Examples:
input_shape = (2, 28, 28, 3)
x = np.arange(np.prod(input_shape)).reshape(input_shape)
y = tf.keras.layers.Cropping2D(cropping=((2, 2), (4, 4)))(x)
print(y.shape)
(2, 24, 20, 3)
Args |
cropping
|
Int, or tuple of 2 ints, or tuple of 2 tuples of 2 ints.
- If int: the same symmetric cropping
is applied to height and width.
- If tuple of 2 ints:
interpreted as two different
symmetric cropping values for height and width:
(symmetric_height_crop, symmetric_width_crop) .
- If tuple of 2 tuples of 2 ints:
interpreted as
((top_crop, bottom_crop), (left_crop, right_crop))
|
data_format
|
A string,
one of channels_last (default) or channels_first .
The ordering of the dimensions in the inputs.
channels_last corresponds to inputs with shape
(batch_size, height, width, channels) while channels_first
corresponds to inputs with shape
(batch_size, channels, height, width) .
When unspecified, uses
image_data_format value found in your Keras config file at
~/.keras/keras.json (if exists) else 'channels_last'.
Defaults to 'channels_last'.
|
|
4D tensor with shape:
- If
data_format is "channels_last" :
(batch_size, rows, cols, channels)
- If
data_format is "channels_first" :
(batch_size, channels, rows, cols)
|
Output shape |
4D tensor with shape:
- If
data_format is "channels_last" :
(batch_size, cropped_rows, cropped_cols, channels)
- If
data_format is "channels_first" :
(batch_size, channels, cropped_rows, cropped_cols)
|