tf.keras.layers.Cropping2D

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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.

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, height, width, channels) while channels_first corresponds to inputs with shape (batch, 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, rows, cols, channels) - If data_format is "channels_first": (batch, channels, rows, cols)

Output shape:

4D tensor with shape: - If data_format is "channels_last": (batch, cropped_rows, cropped_cols, channels) - If data_format is "channels_first": (batch, channels, cropped_rows, cropped_cols)

Examples:

# Crop the input 2D images or feature maps
model = Sequential()
model.add(Cropping2D(cropping=((2, 2), (4, 4)),
                     input_shape=(28, 28, 3)))
# now model.output_shape == (None, 24, 20, 3)
model.add(Conv2D(64, (3, 3), padding='same'))
model.add(Cropping2D(cropping=((2, 2), (2, 2))))
# now model.output_shape == (None, 20, 16. 64)