Added big network

Networks.py

@@ -44,17 +44,18 @@ def createNetwork(name, dicts, outputSizes, incremental, pretrained, hasBack) :
   historyPopNb = 5
   suffixSize = 4
   prefixSize = 4
-  hiddenSize = 1600
   columns = ["UPOS", "FORM"]
 
   if name == "base" :
-    return BaseNet(dicts, outputSizes, incremental, featureFunctionAll, historyNb, historyPopNb, suffixSize, prefixSize, columns, hiddenSize, pretrained, hasBack)
+    return BaseNet(dicts, outputSizes, incremental, featureFunctionAll, historyNb, historyPopNb, suffixSize, prefixSize, columns, 1600, 64, pretrained, hasBack)
+  if name == "big" :
+    return BaseNet(dicts, outputSizes, incremental, featureFunctionAll, historyNb, historyPopNb, suffixSize, prefixSize, columns, 3200, 128, pretrained, hasBack)
   elif name == "baseNoLetters" :
-    return BaseNet(dicts, outputSizes, incremental, featureFunctionAll, historyNb, historyPopNb, 0, 0, columns, hiddenSize, pretrained, hasBack)
+    return BaseNet(dicts, outputSizes, incremental, featureFunctionAll, historyNb, historyPopNb, 0, 0, columns, 1600, 64, pretrained, hasBack)
   elif name == "tagger" :
-    return BaseNet(dicts, outputSizes, incremental, featureFunctionNostack, historyNb, historyPopNb, suffixSize, prefixSize, columns, hiddenSize, pretrained, hasBack)
+    return BaseNet(dicts, outputSizes, incremental, featureFunctionNostack, historyNb, historyPopNb, suffixSize, prefixSize, columns, 1600, 64, pretrained, hasBack)
   elif name == "taggerLexicon" :
-    return BaseNet(dicts, outputSizes, incremental, featureFunctionNostack, historyNb, historyPopNb, suffixSize, prefixSize, ["UPOS","FORM","LEXICON"], hiddenSize, pretrained, hasBack)
+    return BaseNet(dicts, outputSizes, incremental, featureFunctionNostack, historyNb, historyPopNb, suffixSize, prefixSize, ["UPOS","FORM","LEXICON"], 1600, 64, pretrained, hasBack)
 
   raise Exception("Unknown network name '%s'"%name)
 ################################################################################
@@ -88,7 +89,7 @@ class LockedEmbeddings(nn.Module) :
 
 ################################################################################
 class BaseNet(nn.Module) :
-  def __init__(self, dicts, outputSizes, incremental, featureFunction, historyNb, historyPopNb, suffixSize, prefixSize, columns, hiddenSize, pretrained, hasBack) :
+  def __init__(self, dicts, outputSizes, incremental, featureFunction, historyNb, historyPopNb, suffixSize, prefixSize, columns, hiddenSize, embSize, pretrained, hasBack) :
     super().__init__()
     self.dummyParam = nn.Parameter(torch.empty(0), requires_grad=False)
 
@@ -102,7 +103,7 @@ class BaseNet(nn.Module) :
     self.columns = columns
     self.hasBack = hasBack
 
-    self.embSize = 64
+    self.embSize = embSize
     embSizes = {}
     self.nbTargets = len(self.featureFunction.split())
     self.outputSizes = outputSizes