tf.keras.layers.ZeroPadding2D

Zero-padding layer for 2D input (e.g. picture).

Inherits From: Layer, Module

tf.keras.layers.ZeroPadding2D(
    padding=(1, 1), data_format=None, **kwargs
)

This layer can add rows and columns of zeros at the top, bottom, left and right side of an image tensor.

Examples:

input_shape = (1, 1, 2, 2)
x = np.arange(np.prod(input_shape)).reshape(input_shape)
print(x)
[[[[0 1]
   [2 3]]]]
y = tf.keras.layers.ZeroPadding2D(padding=1)(x)
print(y)
tf.Tensor(
  [[[[0 0]
     [0 0]
     [0 0]
     [0 0]]
    [[0 0]
     [0 1]
     [2 3]
     [0 0]]
    [[0 0]
     [0 0]
     [0 0]
     [0 0]]]], shape=(1, 3, 4, 2), dtype=int64)

padding Int, or tuple of 2 ints, or tuple of 2 tuples of 2 ints.

  • If int: the same symmetric padding is applied to height and width.
  • If tuple of 2 ints: interpreted as two different symmetric padding values for height and width: (symmetric_height_pad, symmetric_width_pad).
  • If tuple of 2 tuples of 2 ints: interpreted as ((top_pad, bottom_pad), (left_pad, right_pad))
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".

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)

4D tensor with shape:

  • If data_format is "channels_last": (batch_size, padded_rows, padded_cols, channels)
  • If data_format is "channels_first": (batch_size, channels, padded_rows, padded_cols)