layer_zero_padding_3d
Zero-padding layer for 3D data (spatial or spatio-temporal).
Description
Zero-padding layer for 3D data (spatial or spatio-temporal).
Usage
layer_zero_padding_3d(
object, padding = c(1L, 1L, 1L),
data_format = NULL,
batch_size = NULL,
name = NULL,
trainable = NULL,
weights = NULL
)
Arguments
Arguments | Description |
---|---|
object | What to compose the new Layer instance with. Typically a Sequential model or a Tensor (e.g., as returned by layer_input() ). The return value depends on object . If object is: - missing or NULL , the Layer instance is returned. - a Sequential model, the model with an additional layer is returned. - a Tensor, the output tensor from layer_instance(object) is returned. |
padding | int, or list of 3 ints, or list of 3 lists of 2 ints. - If int: the same symmetric padding is applied to width and height. - If list of 3 ints: interpreted as three different symmetric padding values: (symmetric_dim1_pad, symmetric_dim2_pad, symmetric_dim3_pad) . - If list of 3 lists 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, spatial_dim1, spatial_dim2, spatial_dim3, channels) while channels_first corresponds to inputs with shape (batch, 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”. |
batch_size | Fixed batch size for layer |
name | An optional name string for the layer. Should be unique in a model (do not reuse the same name twice). It will be autogenerated if it isn’t provided. |
trainable | Whether the layer weights will be updated during training. |
weights | Initial weights for layer. |
Section
Input shape
5D tensor with shape:
If
data_format
is"channels_last"
:(batch, first_axis_to_pad, second_axis_to_pad, third_axis_to_pad, depth)
If
data_format
is"channels_first"
:(batch, 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, first_padded_axis, second_padded_axis, third_axis_to_pad, depth)
If
data_format
is"channels_first"
:(batch, depth, first_padded_axis, second_padded_axis, third_axis_to_pad)
See Also
Other convolutional layers: layer_conv_1d_transpose()
, layer_conv_1d()
, layer_conv_2d_transpose()
, layer_conv_2d()
, layer_conv_3d_transpose()
, layer_conv_3d()
, layer_conv_lstm_2d()
, layer_cropping_1d()
, layer_cropping_2d()
, layer_cropping_3d()
, layer_depthwise_conv_1d()
, layer_depthwise_conv_2d()
, layer_separable_conv_1d()
, layer_separable_conv_2d()
, layer_upsampling_1d()
, layer_upsampling_2d()
, layer_upsampling_3d()
, layer_zero_padding_1d()
, layer_zero_padding_2d()