tfmri.layers.Conv3D

class Conv3D(*args, **kwargs)

Bases: keras.layers.convolutional.conv3d.Conv3D

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

Note

This layer can be used as a drop-in replacement for tf.keras.layers.Conv3D. However, this one also supports complex-valued convolutions. Simply pass dtype='complex64' or dtype='complex128' to the layer constructor.

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)

Args:

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.