Cropping layer for 2D input (e.g. picture).
Inherits From: Layer, Module
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`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) orchannels_first.
The ordering of the dimensions in the inputs.channels_lastcorresponds to inputs with shape(batch_size, height, width, channels)whilechannels_firstcorresponds to inputs with shape(batch_size, channels, height, width).
When unspecified, usesimage_data_formatvalue 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_formatis"channels_last":(batch_size, rows, cols, channels)If data_formatis"channels_first":(batch_size, channels, rows, cols) | 
| Output shape | 
|---|
| 4D tensor with shape: 
If data_formatis"channels_last":(batch_size, cropped_rows, cropped_cols, channels)If data_formatis"channels_first":(batch_size, channels, cropped_rows, cropped_cols) |