X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=README.md;h=2ff5b9966b69eb4031506fd123b2fa2fc32bd961;hb=9dad4fa1118632bfa02c01e4d6a8a5a129061a54;hp=e18ea1f91e70bfdb0ebc372073ea5d74e32128ba;hpb=be353fdfc2a57172064a024f8cec6015c9d908e5;p=dagnn.git

diff --git a/README.md b/README.md
index e18ea1f..2ff5b99 100644
--- a/README.md
+++ b/README.md
@@ -14,11 +14,11 @@ c = nn.Linear(10, 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(c, d)
-model:addEdge(c, nn.Mul(-1), e)
+model:connect(a, b)
+model:connect(b, nn.Linear(10, 15), nn.ReLU(), d)
+model:connect(b, c)
+model:connect(c, d)
+model:connect(c, nn.Mul(-1), e)
 
 model:setInput(a)
 model:setOutput({ d, e })
@@ -40,14 +40,14 @@ which would encode the following graph
 
 and run a forward pass with a random batch of 30 samples.
 
-Note that DAG:addEdge
+Note that DAG:connect allows to add a bunch of edges at once. This is particularly useful to add anonymous modules which have a single predecessor and successor.
 
 #Input and output#
 
-If a node has a single successor, its output is sent unchanged as input to that successor. If it has multiple successors, the outputs are collected into a table, and the table is used as input to the successor node. The indexes of the outputs in that table reflects the order in which they appear in the addEdge commands.
+If a node has a single successor, its output is sent unchanged as input to that successor. If it has multiple successors, the outputs are collected into a table, and the table is used as input to the successor node. The indexes of the outputs in that table reflects the order of the DAG:connect() commands.
 
 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*
+*Francois Fleuret, Jan 13th, 2017*