tf.keras.layers.ZeroPadding3D

TensorFlow 1 version View source on GitHub

Zero-padding layer for 3D data (spatial or spatio-temporal).

Inherits From: Layer

tf.keras.layers.ZeroPadding3D(
    padding=(1, 1, 1), data_format=None, **kwargs
)

Examples:

input_shape = (1, 1, 2, 2, 3) 
x = np.arange(np.prod(input_shape)).reshape(input_shape) 
y = tf.keras.layers.ZeroPadding3D(padding=2)(x) 
print(y.shape) 
(1, 5, 6, 6, 3) 

Arguments:

  • padding: Int, or tuple of 3 ints, or tuple of 3 tuples of 2 ints.
    • If int: the same symmetric padding is applied to height and width.
    • If tuple of 3 ints: interpreted as two different symmetric padding values for height and width: (symmetric_dim1_pad, symmetric_dim2_pad, symmetric_dim3_pad).
    • If tuple of 3 tuples of 2 ints: interpreted as ((left_dim1_pad, right_dim1_pad), (left_dim2_pad, right_dim2_pad), (left_dim3_pad, right_dim3_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, spatial_dim1, spatial_dim2, spatial_dim3, channels) while channels_first corresponds to inputs with shape (batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3). 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:

5D tensor with shape:

  • If data_format is "channels_last": (batch_size, first_axis_to_pad, second_axis_to_pad, third_axis_to_pad, depth)
  • If data_format is "channels_first": (batch_size, depth, first_axis_to_pad, second_axis_to_pad, third_axis_to_pad)

Output shape:

5D tensor with shape:

  • If data_format is "channels_last": (batch_size, first_padded_axis, second_padded_axis, third_axis_to_pad, depth)
  • If data_format is "channels_first": (batch_size, depth, first_padded_axis, second_padded_axis, third_axis_to_pad)