Added big neural network

Networks.py

@@ -10,10 +10,13 @@ def createNetwork(name, dicts, outputSizes, incremental) :
   historyNb = 5
   suffixSize = 4
   prefixSize = 4
+  hiddenSize = 1600
   columns = ["UPOS", "FORM"]
 
   if name == "base" :
-    return BaseNet(dicts, outputSizes, incremental, featureFunctionAll, historyNb, suffixSize, prefixSize, columns)
+    return BaseNet(dicts, outputSizes, incremental, featureFunctionAll, historyNb, suffixSize, prefixSize, columns, hiddenSize)
+  elif name == "big" :
+    return BaseNet(dicts, outputSizes, incremental, featureFunctionAll, historyNb, suffixSize, prefixSize, columns, hiddenSize*2)
   elif name == "lstm" :
     return LSTMNet(dicts, outputSizes, incremental)
   elif name == "separated" :
@@ -26,7 +29,7 @@ def createNetwork(name, dicts, outputSizes, incremental) :
 
 ################################################################################
 class BaseNet(nn.Module):
-  def __init__(self, dicts, outputSizes, incremental, featureFunction, historyNb, suffixSize, prefixSize, columns) :
+  def __init__(self, dicts, outputSizes, incremental, featureFunction, historyNb, suffixSize, prefixSize, columns, hiddenSize) :
     super().__init__()
     self.dummyParam = nn.Parameter(torch.empty(0), requires_grad=False)
 
@@ -44,9 +47,9 @@ class BaseNet(nn.Module):
     self.outputSizes = outputSizes
     for name in dicts.dicts :
       self.add_module("emb_"+name, nn.Embedding(len(dicts.dicts[name]), self.embSize))
-    self.fc1 = nn.Linear(self.inputSize * self.embSize, 1600)
+    self.fc1 = nn.Linear(self.inputSize * self.embSize, hiddenSize)
     for i in range(len(outputSizes)) :
-      self.add_module("output_"+str(i), nn.Linear(1600, outputSizes[i]))
+      self.add_module("output_"+str(i), nn.Linear(hiddenSize, outputSizes[i]))
     self.dropout = nn.Dropout(0.3)
 
     self.apply(self.initWeights)