Graph Network

Represents a neural network using a Graph that usually costains 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

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)
#> torch_tensor
#>  0.3744
#>  0.4958
#>  0.2366
#>  0.4935
#>  0.5576
#>  0.5646
#>  0.4307
#>  0.0599
#>  0.1646
#>  0.4125
#>  0.3545
#>  0.2628
#>  0.5434
#>  0.3028
#>  0.2900
#>  0.4098
#> [ CPUFloatType{16,1} ][ grad_fn = <AddmmBackward0> ]