This layer creates a convolution kernel that is convolved with the layer
input over a 3D spatial (or temporal) dimension (width,height and depth) to
produce a tensor of outputs. If `use_bias`

is `TRUE`

, a bias vector is created
and added to the outputs. Finally, if `activation`

is not `NULL`

, it is
applied to the outputs as well.

## Usage

```
layer_conv_3d(
object,
filters,
kernel_size,
strides = list(1L, 1L, 1L),
padding = "valid",
data_format = NULL,
dilation_rate = list(1L, 1L, 1L),
groups = 1L,
activation = NULL,
use_bias = TRUE,
kernel_initializer = "glorot_uniform",
bias_initializer = "zeros",
kernel_regularizer = NULL,
bias_regularizer = NULL,
activity_regularizer = NULL,
kernel_constraint = NULL,
bias_constraint = NULL,
...
)
```

## Arguments

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

- filters
int, the dimension of the output space (the number of filters in the convolution).

- kernel_size
int or list of 3 integer, specifying the size of the convolution window.

- strides
int or list of 3 integer, specifying the stride length of the convolution.

`strides > 1`

is incompatible with`dilation_rate > 1`

.- padding
string, either

`"valid"`

or`"same"`

(case-insensitive).`"valid"`

means no padding.`"same"`

results in padding evenly to the left/right or up/down of the input. When`padding="same"`

and`strides=1`

, the output has the same size as the input.- data_format
string, either

`"channels_last"`

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"`

.- dilation_rate
int or list of 3 integers, specifying the dilation rate to use for dilated convolution.

- groups
A positive int specifying the number of groups in which the input is split along the channel axis. Each group is convolved separately with

`filters %/% groups`

filters. The output is the concatenation of all the`groups`

results along the channel axis. Input channels and`filters`

must both be divisible by`groups`

.- activation
Activation function. If

`NULL`

, no activation is applied.- use_bias
bool, if

`TRUE`

, bias will be added to the output.- kernel_initializer
Initializer for the convolution kernel. If

`NULL`

, the default initializer (`"glorot_uniform"`

) will be used.- bias_initializer
Initializer for the bias vector. If

`NULL`

, the default initializer (`"zeros"`

) will be used.- kernel_regularizer
Optional regularizer for the convolution kernel.

- bias_regularizer
Optional regularizer for the bias vector.

- activity_regularizer
Optional regularizer function for the output.

- kernel_constraint
Optional projection function to be applied to the kernel after being updated by an

`Optimizer`

(e.g. used to implement norm constraints or value constraints for layer weights). The function must take as input the unprojected variable and must return the projected variable (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training.- bias_constraint
Optional projection function to be applied to the bias after being updated by an

`Optimizer`

.- ...
For forward/backward compatability.

## Input Shape

If

`data_format="channels_last"`

: 5D tensor with shape:`(batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels)`

If

`data_format="channels_first"`

: 5D tensor with shape:`(batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3)`

## Output Shape

If

`data_format="channels_last"`

: 5D tensor with shape:`(batch_size, new_spatial_dim1, new_spatial_dim2, new_spatial_dim3, filters)`

If

`data_format="channels_first"`

: 5D tensor with shape:`(batch_size, filters, new_spatial_dim1, new_spatial_dim2, new_spatial_dim3)`

## Example

```
x <- random_uniform(c(4, 10, 10, 10, 128))
y <- x |> layer_conv_3d(32, 3, activation = 'relu')
shape(y)
```

## See also

Other convolutional layers: `layer_conv_1d()`

`layer_conv_1d_transpose()`

`layer_conv_2d()`

`layer_conv_2d_transpose()`

`layer_conv_3d_transpose()`

`layer_depthwise_conv_1d()`

`layer_depthwise_conv_2d()`

`layer_separable_conv_1d()`

`layer_separable_conv_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_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()`

`layer_zero_padding_3d()`

`rnn_cell_gru()`

`rnn_cell_lstm()`

`rnn_cell_simple()`

`rnn_cells_stack()`