demo now works (without random shuffle)

1 files changed, 38 insertions, 21 deletions

diff --git a/nn/layer_dag.lua b/nn/layer_dag.lua
index 1ab18fa..4ee829e 100644
--- a/nn/layer_dag.lua
+++ b/nn/layer_dag.lua
@@ -44,6 +44,7 @@ function nerv.DAGLayer:__init(id, global_conf, layer_conf)
     local outputs = {}
     local dim_in = layer_conf.dim_in
     local dim_out = layer_conf.dim_out
+    local parsed_conn = {}
     for from, to in pairs(layer_conf.connections) do
         local id_from, port_from = parse_id(from)
         local id_to, port_to = parse_id(to)
@@ -76,32 +77,18 @@ function nerv.DAGLayer:__init(id, global_conf, layer_conf)
             if output_dim[port_from] ~= input_dim[port_to] then
                 nerv.error("mismatching data dimension between %s and %s", from, to)
             end
-            local mid = global_conf.mat_type(global_conf.batch_size,
-                                                output_dim[port_from])
-            local err_mid = mid:create() 
-
-            ref_from.outputs[port_from] = mid
-            ref_to.inputs[port_to] = mid
-
-            ref_from.err_inputs[port_from] = err_mid
-            ref_to.err_outputs[port_to] = err_mid
 
+            table.insert(parsed_conn,
+                {{ref_from, port_from}, {ref_to, port_to}})
             table.insert(ref_from.next_layers, ref_to) -- add edge
             ref_to.in_deg = ref_to.in_deg + 1          -- increase the in-degree of the target layer
         end
     end
-    self.layers = layers
-    self.inputs = inputs
-    self.outputs = outputs
-    self.dim_in = dim_in
-    self.dim_out = dim_out
-end
 
-function nerv.DAGLayer:init(id) -- topology sort
     local queue = {}
     local l = 1
     local r = 1
-    for id, ref in pairs(self.layers) do
+    for id, ref in pairs(layers) do
         if ref.in_deg == 0 then
             table.insert(queue, ref)
             nerv.utils.printf("adding source layer: %s\n", id)
@@ -126,20 +113,50 @@ function nerv.DAGLayer:init(id) -- topology sort
     for i = 1, #queue do
         nerv.utils.printf("queued layer: %s\n", queue[i].layer.id)
     end
-    self.queue = queue
-    for id, ref in pairs(self.layers) do
+
+    for id, ref in pairs(layers) do
         -- check wether the graph is connected
         if ref.visited == false then
             nerv.utils.printf("warning: layer %s is ignored\n", id)
         end
+    end
+
+    self.layers = layers
+    self.inputs = inputs
+    self.outputs = outputs
+    self.dim_in = dim_in
+    self.dim_out = dim_out
+    self.parsed_conn = parsed_conn
+    self.queue = queue
+    self.gconf = global_conf
+end
+
+function nerv.DAGLayer:init(batch_size) -- topology sort
+    for i, conn in ipairs(self.parsed_conn) do
+        local _, output_dim
+        local ref_from, port_from, ref_to, port_to
+        ref_from, port_from = unpack(conn[1])
+        ref_to, port_to = unpack(conn[2])
+        _, output_dim = ref_from.layer:get_dim()
+        local mid = self.gconf.cumat_type(batch_size,
+                                        output_dim[port_from])
+        local err_mid = mid:create()
+
+        ref_from.outputs[port_from] = mid
+        ref_to.inputs[port_to] = mid
+
+        ref_from.err_inputs[port_from] = err_mid
+        ref_to.err_outputs[port_to] = err_mid
+    end
+    for id, ref in pairs(self.layers) do
         for i = 1, ref.input_len do
             if ref.inputs[i] == nil then
-                nerv.error("dangling port %d of layer %s", i, id)
+                nerv.error("dangling input port %d of layer %s", i, id)
             end
         end
         for i = 1, ref.output_len do
             if ref.outputs[i] == nil then
-                nerv.error("dangling port %d of layer %s", i, id)
+                nerv.error("dangling output port %d of layer %s", i, id)
             end
         end
         -- initialize sub layers