DAG:addEdge now allows to add a bunch of edges in one shot. This allows to use anonym...

[dagnn.git] / README.md

diff --git a/README.md b/README.md
index 3b8d274..e18ea1f 100644 (file)
--- a/README.md
+++ b/README.md
@@ -3,7 +3,7 @@ This package implements a new module nn.DAG which inherits from nn.Container and
 
 #Example#
 
-The typical use is:
+A typical use would be:
 
 ```Lua
 model = nn.DAG()
@@ -11,34 +11,36 @@ 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#