#Example#
-The typical use is:
+A typical use would be:
```Lua
model = nn.DAG()
a = nn.Linear(100, 10)
b = nn.ReLU()
c = nn.Linear(10, 15)
-d = nn.Linear(10, 15)
-e = nn.CMulTable()
-f = nn.Linear(15, 15)
+d = nn.CMulTable()
+e = nn.Linear(15, 15)
model:addEdge(a, b)
+model:addEdge(b, nn.Linear(10, 15), nn.ReLU(), d)
model:addEdge(b, c)
-model:addEdge(b, d)
-model:addEdge(c, e)
-model:addEdge(d, e)
-model:addEdge(d, f)
+model:addEdge(c, d)
+model:addEdge(c, nn.Mul(-1), e)
model:setInput(a)
-model:setOutput({ e, f })
+model:setOutput({ d, e })
-input = torch.Tensor(300, 100):uniform()
-output = model:updateOutput(input):clone()
+input = torch.Tensor(30, 100):uniform()
+output = model:updateOutput(input)
```
which would encode the following graph
- +--> c ----> e -->
- / /
- / /
- input --> a --> b ----> d ---+ output
- \
+ +- Linear(10, 10) -> ReLU ---> d -->
+ / /
+ / /
+ --> a --> b -----------> c --------------+
\
- +--> f -->
+ \
+ +-- Mul(-1) --> e -->
+
+and run a forward pass with a random batch of 30 samples.
+
+Note that DAG:addEdge
#Input and output#
The expected input (respectively the produced output) is a nested table of inputs reflecting the structure of the nested table of modules provided to DAG:setInput (respectively DAG:setOutput)
-So for instance, in the example above, the DAG expects a tensor as input, since it is the input to the module a, and its output will is a table composed of two tensors, corresponding to the outputs of e and f respectively.
+So for instance, in the example above, the model expects a tensor as input, since it is the input to the module a, and its output will is a table composed of two tensors, corresponding to the outputs of d and e respectively.
*Francois Fleuret, Jan 12th, 2017*