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tf.keras.layers.Cropping2D

TensorFlow 1 version View source on GitHub

Cropping layer for 2D input (e.g. picture).

Inherits From: Layer

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) 

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). It defaults to the image_data_format value found in your Keras config file at ~/.keras/keras.json. If you never set it, then it will be "channels_last".

Input shape:

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)