nn_graph: Graph Network

In mlr3torch: Deep Learning with 'mlr3'

Graph Network

Description

Represents a neural network using a Graph that contains mostly PipeOpModules.

Usage

nn_graph(graph, shapes_in, output_map = graph$output$name, list_output = FALSE)

Arguments

graph	(Graph) The Graph to wrap. Is not cloned.
shapes_in	(named integer) Shape info of tensors that go into graph. Names must be graph$input$name, possibly in different order.
output_map	(character) Which of graph's outputs to use. Must be a subset of graph$output$name.
list_output	(logical(1)) Whether output should be a list of tensors. If FALSE (default), then length(output_map) must be 1.

Value

nn_graph

Fields

• graph :: Graph
  The graph (consisting primarily of PipeOpModules) that is wrapped by the network.

• input_map :: character()
  The names of the input arguments of the network.

• shapes_in :: list()
  The shapes of the input tensors of the network.

• output_map :: character()
  Which output elements of the graph are returned by the ⁠$forward()⁠ method.

• list_output :: logical(1)
  Whether the output is a list of tensors.

• module_list :: nn_module_list
  The list of modules in the network.

• list_output :: logical(1)
  Whether the output is a list of tensors.

See Also

Other Graph Network: ModelDescriptor(), TorchIngressToken(), mlr_learners_torch_model, mlr_pipeops_module, mlr_pipeops_torch, mlr_pipeops_torch_ingress, mlr_pipeops_torch_ingress_categ, mlr_pipeops_torch_ingress_ltnsr, mlr_pipeops_torch_ingress_num, model_descriptor_to_learner(), model_descriptor_to_module(), model_descriptor_union()

Examples

graph = mlr3pipelines::Graph$new()
graph$add_pipeop(po("module_1", module = nn_linear(10, 20)), clone = FALSE)
graph$add_pipeop(po("module_2", module = nn_relu()), clone = FALSE)
graph$add_pipeop(po("module_3", module = nn_linear(20, 1)), clone = FALSE)
graph$add_edge("module_1", "module_2")
graph$add_edge("module_2", "module_3")

network = nn_graph(graph, shapes_in = list(module_1.input = c(NA, 10)))

x = torch_randn(16, 10)

network(module_1.input = x)

mlr3torch documentation built on Aug. 26, 2025, 5:09 p.m.