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
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
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 )
What to compose the new
Layer instance with. Typically a
Sequential model or a Tensor (e.g., as returned by
The return value depends on
Layer instance is returned.
Sequential model, the model with an additional layer is returned.
a Tensor, the output tensor from
layer_instance(object) is returned.
Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution).
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.
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.
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
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".
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
dilation_rate value != 1 is incompatible with specifying any stride
value != 1.
A positive integer specifying the number of groups in which the
input is split along the channel axis. Each group is convolved separately
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
Activation function to use. If you don't specify anything,
no activation is applied (ie. "linear" activation:
a(x) = x).
Boolean, whether the layer uses a bias vector.
Initializer for the
kernel weights matrix.
Initializer for the bias vector.
Regularizer function applied to the
Regularizer function applied to the bias vector.
Regularizer function applied to the output of the layer (its "activation")..
Constraint function applied to the kernel matrix.
Constraint function applied to the bias vector.
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.
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.
indicates batches of an arbitrary number of 32-dimensional vectors.
Fixed batch size for layer
The data type expected by the input, as a string (
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.
Whether the layer weights will be updated during training.
Initial weights for layer.
5D tensor with shape:
(samples, channels, conv_dim1, conv_dim2, conv_dim3) if data_format='channels_first' or 5D tensor with
(samples, conv_dim1, conv_dim2, conv_dim3, channels) if
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
new_conv_dim3 values might have changed due to padding.
Other convolutional layers: