TensorFlow 1 version | View source on GitHub |
3D convolution layer (e.g. spatial convolution over volumes).
tf.keras.layers.Conv3D(
filters, kernel_size, strides=(1, 1, 1), padding='valid', data_format=None,
dilation_rate=(1, 1, 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
)
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, 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"
.
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).
|
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".
|
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.
|
activation
|
Activation function to use.
If you don't specify anything, no activation is applied
(ie. "linear" activation: a(x) = x ).
|
use_bias
|
Boolean, whether the layer uses a bias vector. |
kernel_initializer
|
Initializer for the kernel weights matrix.
|
bias_initializer
|
Initializer for the bias vector. |
kernel_regularizer
|
Regularizer function applied to
the kernel weights matrix.
|
bias_regularizer
|
Regularizer function applied to the bias vector. |
activity_regularizer
|
Regularizer function applied to the output of the layer (its "activation").. |
kernel_constraint
|
Constraint function applied to the kernel matrix. |
bias_constraint
|
Constraint function applied to the bias vector. |
Input shape:
5D tensor with shape:
(samples, channels, conv_dim1, conv_dim2, conv_dim3)
if
data_format='channels_first'
or 5D tensor with shape:
(samples, conv_dim1, conv_dim2, conv_dim3, channels)
if
data_format='channels_last'.
Output shape:
5D tensor with shape:
(samples, filters, new_conv_dim1, new_conv_dim2, new_conv_dim3)
if
data_format='channels_first'
or 5D tensor with shape:
(samples, 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.