TensorFlow 1 version | View source on GitHub |
Zero-padding layer for 3D data (spatial or spatio-temporal).
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.
|
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)