`Conv3D`

class```
tf.keras.layers.Conv3D(
filters,
kernel_size,
strides=(1, 1, 1),
padding="valid",
data_format=None,
dilation_rate=(1, 1, 1),
groups=1,
activation=None,
use_bias=True,
kernel_initializer="glorot_uniform",
bias_initializer="zeros",
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
**kwargs
)
```

3D convolution layer (e.g. spatial convolution over volumes).

This layer creates a convolution kernel that is convolved
with the layer input 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 `None`

, it is applied to the outputs as well.

When using this layer as the first layer in a model,
provide the keyword argument `input_shape`

(tuple of integers or `None`

, does not include the sample axis),
e.g. `input_shape=(128, 128, 128, 1)`

for 128x128x128 volumes
with a single channel,
in `data_format="channels_last"`

.

**Examples**

```
>>> # The inputs are 28x28x28 volumes with a single channel, and the
>>> # batch size is 4
>>> input_shape =(4, 28, 28, 28, 1)
>>> x = tf.random.normal(input_shape)
>>> y = tf.keras.layers.Conv3D(
... 2, 3, activation='relu', input_shape=input_shape[1:])(x)
>>> print(y.shape)
(4, 26, 26, 26, 2)
```

```
>>> # With extended batch shape [4, 7], e.g. a batch of 4 videos of 3D frames,
>>> # with 7 frames per video.
>>> input_shape = (4, 7, 28, 28, 28, 1)
>>> x = tf.random.normal(input_shape)
>>> y = tf.keras.layers.Conv3D(
... 2, 3, activation='relu', input_shape=input_shape[2:])(x)
>>> print(y.shape)
(4, 7, 26, 26, 26, 2)
```

**Arguments**

**filters**: Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution).**kernel_size**: An integer or tuple/list of 3 integers, specifying the depth, height and width of the 3D convolution window. Can be a single integer to specify the same value for all spatial dimensions.**strides**: An integer or tuple/list of 3 integers, specifying the strides of the convolution along each spatial dimension. Can be a single integer to specify the same value for all spatial dimensions. Specifying any stride value != 1 is incompatible with specifying any`dilation_rate`

value != 1.**padding**: one of`"valid"`

or`"same"`

(case-insensitive).`"valid"`

means no padding.`"same"`

results in padding with zeros evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input.**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_shape + (spatial_dim1, spatial_dim2, spatial_dim3, channels)`

while`channels_first`

corresponds to inputs with shape`batch_shape + (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**: an integer or tuple/list of 3 integers, specifying the dilation rate to use for dilated convolution. Can be a single integer to specify the same value for all spatial dimensions. Currently, specifying any`dilation_rate`

value != 1 is incompatible with specifying any stride value != 1.**groups**: A positive integer 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 to use. If you don't specify anything, no activation is applied (see`keras.activations`

).**use_bias**: Boolean, whether the layer uses a bias vector.**kernel_initializer**: Initializer for the`kernel`

weights matrix (see`keras.initializers`

). Defaults to 'glorot_uniform'.**bias_initializer**: Initializer for the bias vector (see`keras.initializers`

). Defaults to 'zeros'.**kernel_regularizer**: Regularizer function applied to the`kernel`

weights matrix (see`keras.regularizers`

).**bias_regularizer**: Regularizer function applied to the bias vector (see`keras.regularizers`

).**activity_regularizer**: Regularizer function applied to the output of the layer (its "activation") (see`keras.regularizers`

).**kernel_constraint**: Constraint function applied to the kernel matrix (see`keras.constraints`

).**bias_constraint**: Constraint function applied to the bias vector (see`keras.constraints`

).

**Input shape**

5+D tensor with shape: ```
batch_shape + (channels, conv_dim1, conv_dim2,
conv_dim3)
```

if data_format='channels_first'
or 5+D tensor with shape: ```
batch_shape + (conv_dim1, conv_dim2, conv_dim3,
channels)
```

if data_format='channels_last'.

**Output shape**

5+D tensor with shape: ```
batch_shape + (filters, new_conv_dim1,
new_conv_dim2, new_conv_dim3)
```

if data_format='channels_first'
or 5+D tensor with shape: ```
batch_shape + (new_conv_dim1, new_conv_dim2,
new_conv_dim3, filters)
```

if data_format='channels_last'. `new_conv_dim1`

,
`new_conv_dim2`

and `new_conv_dim3`

values might have changed due to
padding.

**Returns**

A tensor of rank 5+ representing
`activation(conv3d(inputs, kernel) + bias)`

.

**Raises**

**ValueError**: if`padding`

is "causal".**ValueError**: when both`strides > 1`

and`dilation_rate > 1`

.