layer_zero_padding_3d {keras}

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

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.

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(), layer_conv_1d_transpose(), layer_conv_2d(), layer_conv_2d_transpose(), layer_conv_3d(), layer_conv_3d_transpose(), 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()