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

Source: R/layers-reshaping.R
layer_zero_padding_3d.Rd

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

Usage

layer_zero_padding_3d(
  object,
  padding = list(list(1L, 1L), list(1L, 1L), list(1L, 1L)),
  data_format = NULL,
  ...
)

Arguments

object

Object to compose the layer with. A tensor, array, or sequential model.

padding

Int, or list of 3 ints, or list of 3 lists of 2 ints.

  • If int: the same symmetric padding is applied to depth, height, and width.

  • If list 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 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_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".

...

For forward/backward compatability.

Value

The return value depends on the value provided for the first argument. If object is:

  • a keras_model_sequential(), then the layer is added to the sequential model (which is modified in place). To enable piping, the sequential model is also returned, invisibly.

  • a keras_input(), then the output tensor from calling layer(input) is returned.

  • NULL or missing, then a Layer instance is returned.

Example

input_shape <- c(1, 1, 2, 2, 3)
x <- op_reshape(seq_len(prod(input_shape)), input_shape)
x

## tf.Tensor(
## [[[[[ 1  2  3]
##     [ 4  5  6]]
##
##    [[ 7  8  9]
##     [10 11 12]]]]], shape=(1, 1, 2, 2, 3), dtype=int32)


y <- layer_zero_padding_3d(x, padding = 2)
shape(y)

## shape(1, 5, 6, 6, 3)

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)

See also

Other reshaping layers:
layer_cropping_1d()
layer_cropping_2d()
layer_cropping_3d()
layer_flatten()
layer_permute()
layer_repeat_vector()
layer_reshape()
layer_upsampling_1d()
layer_upsampling_2d()
layer_upsampling_3d()
layer_zero_padding_1d()
layer_zero_padding_2d()

Other layers:
Layer()
layer_activation()
layer_activation_elu()
layer_activation_leaky_relu()
layer_activation_parametric_relu()
layer_activation_relu()
layer_activation_softmax()
layer_activity_regularization()
layer_add()
layer_additive_attention()
layer_alpha_dropout()
layer_attention()
layer_average()
layer_average_pooling_1d()
layer_average_pooling_2d()
layer_average_pooling_3d()
layer_batch_normalization()
layer_bidirectional()
layer_category_encoding()
layer_center_crop()
layer_concatenate()
layer_conv_1d()
layer_conv_1d_transpose()
layer_conv_2d()
layer_conv_2d_transpose()
layer_conv_3d()
layer_conv_3d_transpose()
layer_conv_lstm_1d()
layer_conv_lstm_2d()
layer_conv_lstm_3d()
layer_cropping_1d()
layer_cropping_2d()
layer_cropping_3d()
layer_dense()
layer_depthwise_conv_1d()
layer_depthwise_conv_2d()
layer_discretization()
layer_dot()
layer_dropout()
layer_einsum_dense()
layer_embedding()
layer_feature_space()
layer_flatten()
layer_flax_module_wrapper()
layer_gaussian_dropout()
layer_gaussian_noise()
layer_global_average_pooling_1d()
layer_global_average_pooling_2d()
layer_global_average_pooling_3d()
layer_global_max_pooling_1d()
layer_global_max_pooling_2d()
layer_global_max_pooling_3d()
layer_group_normalization()
layer_group_query_attention()
layer_gru()
layer_hashed_crossing()
layer_hashing()
layer_identity()
layer_integer_lookup()
layer_jax_model_wrapper()
layer_lambda()
layer_layer_normalization()
layer_lstm()
layer_masking()
layer_max_pooling_1d()
layer_max_pooling_2d()
layer_max_pooling_3d()
layer_maximum()
layer_mel_spectrogram()
layer_minimum()
layer_multi_head_attention()
layer_multiply()
layer_normalization()
layer_permute()
layer_random_brightness()
layer_random_contrast()
layer_random_crop()
layer_random_flip()
layer_random_rotation()
layer_random_translation()
layer_random_zoom()
layer_repeat_vector()
layer_rescaling()
layer_reshape()
layer_resizing()
layer_rnn()
layer_separable_conv_1d()
layer_separable_conv_2d()
layer_simple_rnn()
layer_spatial_dropout_1d()
layer_spatial_dropout_2d()
layer_spatial_dropout_3d()
layer_spectral_normalization()
layer_string_lookup()
layer_subtract()
layer_text_vectorization()
layer_tfsm()
layer_time_distributed()
layer_torch_module_wrapper()
layer_unit_normalization()
layer_upsampling_1d()
layer_upsampling_2d()
layer_upsampling_3d()
layer_zero_padding_1d()
layer_zero_padding_2d()
rnn_cell_gru()
rnn_cell_lstm()
rnn_cell_simple()
rnn_cells_stack()