ZeroPadding3D layer

[source]

ZeroPadding3D class

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

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

Example

>>> input_shape = (1, 1, 2, 2, 3)
>>> x = np.arange(np.prod(input_shape)).reshape(input_shape)
>>> y = keras.layers.ZeroPadding3D(padding=2)(x)
>>> 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 depth, height, and width.
  • If tuple of 3 ints: interpreted as three different symmetric padding values for depth, 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). When unspecified, uses image_data_format value found in your Keras config file at ~/.keras/keras.json (if exists). Defaults to "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)