layer_conv_3d: 3D convolution layer (e.g. spatial convolution over volumes).

In keras: R Interface to 'Keras'

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

Description

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 NULL, 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 (list of integers, does not include the sample axis), e.g. input_shape=c(128L, 128L, 128L, 3L) for 128x128x128 volumes with a single channel, in data_format="channels_last".

Usage

layer_conv_3d(
  object,
  filters,
  kernel_size,
  strides = c(1L, 1L, 1L),
  padding = "valid",
  data_format = NULL,
  dilation_rate = c(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,
  input_shape = NULL,
  batch_input_shape = NULL,
  batch_size = NULL,
  dtype = NULL,
  name = NULL,
  trainable = NULL,
  weights = NULL
)

Arguments

object	What to compose the new Layer instance with. Typically a Sequential model or a Tensor (e.g., as returned by layer_input()). The return value depends on object. If object is: missing or NULL, the Layer instance is returned. a Sequential model, the model with an additional layer is returned. a Tensor, the output tensor from layer_instance(object) is returned.
filters	Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution).
kernel_size	An integer or 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 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 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 (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	Dimensionality of the input (integer) not including the samples axis. This argument is required when using this layer as the first layer in a model.
batch_input_shape	Shapes, including the batch size. For instance, batch_input_shape=c(10, 32) indicates that the expected input will be batches of 10 32-dimensional vectors. batch_input_shape=list(NULL, 32) indicates batches of an arbitrary number of 32-dimensional vectors.
batch_size	Fixed batch size for layer
dtype	The data type expected by the input, as a string (float32, float64, int32...)
name	An optional name string for the layer. Should be unique in a model (do not reuse the same name twice). It will be autogenerated if it isn't provided.
trainable	Whether the layer weights will be updated during training.
weights	Initial weights for layer.

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.

See Also

Other convolutional layers: layer_conv_1d_transpose(), layer_conv_1d(), layer_conv_2d_transpose(), layer_conv_2d(), layer_conv_3d_transpose(), layer_conv_lstm_2d(), layer_cropping_1d(), layer_cropping_2d(), layer_cropping_3d(), layer_depthwise_conv_1d(), layer_depthwise_conv_2d(), layer_separable_conv_1d(), layer_separable_conv_2d(), layer_upsampling_1d(), layer_upsampling_2d(), layer_upsampling_3d(), layer_zero_padding_1d(), layer_zero_padding_2d(), layer_zero_padding_3d()

keras documentation built on Aug. 16, 2023, 1:07 a.m.