Cropping2D layer
Cropping2D class
keras.layers.Cropping2D(cropping=((0, 0), (0, 0)), data_format=None, **kwargs)
Cropping layer for 2D input (e.g. picture).
It crops along spatial dimensions, i.e. height and width.
Example
>>> input_shape = (2, 28, 28, 3)
>>> x = np.arange(np.prod(input_shape)).reshape(input_shape)
>>> y = keras.layers.Cropping2D(cropping=((2, 2), (4, 4)))(x)
>>> y.shape
(2, 24, 20, 3)
Arguments
- 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, usesimage_data_formatvalue found in your Keras config file at~/.keras/keras.json(if exists). Defaults to"channels_last".
Input shape
4D tensor with shape:
- If data_format is "channels_last":
(batch_size, height, width, channels)
- If data_format is "channels_first":
(batch_size, channels, height, width)
Output shape
4D tensor with shape:
- If data_format is "channels_last":
(batch_size, cropped_height, cropped_width, channels)
- If data_format is "channels_first":
(batch_size, channels, cropped_height, cropped_width)