Zero-padding layer for 3D data (spatial or spatio-temporal).
Inherits From: Layer
, Module
View aliases
Compat aliases for migration
See
Migration guide for
more details.
`tf.compat.v1.keras.layers.ZeroPadding3D`
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)
Args |
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) .
When unspecified, uses
image_data_format value found in your Keras config file at
~/.keras/keras.json (if exists) else 'channels_last'.
Defaults to 'channels_last'.
|
|
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)
|