table composed of two tensors, corresponding to the outputs of d and e
respectively.
-#Usage#
+##Usage##
-##nn.DAG()##
+###nn.DAG()###
Create a new empty DAG, which inherits from nn.Container.
-##nn.DAG:connect([module1 [, module2 [, ...]]])##
+###nn.DAG:connect([module1 [, module2 [, ...]]])###
Add new nodes corresponding to the modules passed as arguments if they
are not already existing. Add edges between every two nodes
Calling it with n > 2 arguments is strictly equivalent to calling it
n-1 times on the pairs of successive arguments.
-##nn.DAG:setInput(i)##
+###nn.DAG:setInput(i)###
Defines the content and structure of the input. The argument should be
either a module, or a (nested) table of module. The input to the DAG
should be a (nested) table of inputs with the corresponding structure.
-##nn.DAG:setOutput(o)##
+###nn.DAG:setOutput(o)###
Similar to DAG:setInput().
-##nn.DAG:print()##
+###nn.DAG:print()###
Prints the list of nodes.
-##nn.DAG:saveDot(filename)##
+###nn.DAG:saveDot(filename)###
Save a dot file to be used by the Graphviz set of tools for graph
visualization. This dot file can than be used for instance to produce
dot graph.dot -T pdf -o graph.pdf
```
-##nn.DAG:updateOutput(input)##
+###nn.DAG:updateOutput(input)###
See the torch documentation.
-##nn.DAG:updateGradInput(input, gradOutput)##
+###nn.DAG:updateGradInput(input, gradOutput)###
See the torch documentation.
-##nn.DAG:accGradParameters(input, gradOutput, scale)##
+###nn.DAG:accGradParameters(input, gradOutput, scale)###
See the torch documentation.