added semiNet

Networks.py

@@ -15,6 +15,8 @@ def createNetwork(name, dicts, outputSizes, incremental) :
 
   if name == "base" :
     return BaseNet(dicts, outputSizes, incremental, featureFunctionAll, historyNb, suffixSize, prefixSize, columns, hiddenSize)
+  elif name == "semi" :
+    return SemiNet(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" :
@@ -94,7 +96,77 @@ class BaseNet(nn.Module):
     prefixValues = Features.extractPrefixFeatures(dicts, config, self.prefixSize)
     suffixValues = Features.extractSuffixFeatures(dicts, config, self.suffixSize)
     return torch.cat([colsValues, historyValues, prefixValues, suffixValues])
+################################################################################
+
+################################################################################
+class SemiNet(nn.Module):
+  def __init__(self, dicts, outputSizes, incremental, featureFunction, historyNb, suffixSize, prefixSize, columns, hiddenSize) :
+    super().__init__()
+    self.dummyParam = nn.Parameter(torch.empty(0), requires_grad=False)
+
+    self.incremental = incremental
+    self.state = 0
+    self.featureFunction = featureFunction
+    self.historyNb = historyNb
+    self.suffixSize = suffixSize
+    self.prefixSize = prefixSize
+    self.columns = columns
+
+    self.embSize = 64
+    self.nbTargets = len(self.featureFunction.split())
+    self.inputSize = len(self.columns)*self.nbTargets+self.historyNb+self.suffixSize+self.prefixSize
+    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, hiddenSize)
+    for i in range(len(outputSizes)) :
+      self.add_module("output_hidden_"+str(i), nn.Linear(hiddenSize, hiddenSize))
+      self.add_module("output_"+str(i), nn.Linear(hiddenSize, outputSizes[i]))
+    self.dropout = nn.Dropout(0.3)
 
+    self.apply(self.initWeights)
+
+  def setState(self, state) :
+    self.state = state
+
+  def forward(self, x) :
+    embeddings = []
+    for i in range(len(self.columns)) :
+      embeddings.append(getattr(self, "emb_"+self.columns[i])(x[...,i*self.nbTargets:(i+1)*self.nbTargets]))
+    y = torch.cat(embeddings,-1).view(x.size(0),-1)
+    curIndex = len(self.columns)*self.nbTargets
+    if self.historyNb > 0 :
+      historyEmb = getattr(self, "emb_HISTORY")(x[...,curIndex:curIndex+self.historyNb]).view(x.size(0),-1)
+      y = torch.cat([y, historyEmb],-1)
+      curIndex = curIndex+self.historyNb
+    if self.prefixSize > 0 :
+      prefixEmb = getattr(self, "emb_LETTER")(x[...,curIndex:curIndex+self.prefixSize]).view(x.size(0),-1)
+      y = torch.cat([y, prefixEmb],-1)
+      curIndex = curIndex+self.prefixSize
+    if self.suffixSize > 0 :
+      suffixEmb = getattr(self, "emb_LETTER")(x[...,curIndex:curIndex+self.suffixSize]).view(x.size(0),-1)
+      y = torch.cat([y, suffixEmb],-1)
+      curIndex = curIndex+self.suffixSize
+    y = self.dropout(y)
+    y = F.relu(self.dropout(self.fc1(y)))
+    y = self.dropout(getattr(self, "output_hidden_"+str(self.state))(y))
+    y = getattr(self, "output_"+str(self.state))(y)
+    return y
+
+  def currentDevice(self) :
+    return self.dummyParam.device
+
+  def initWeights(self,m) :
+    if type(m) == nn.Linear:
+      torch.nn.init.xavier_uniform_(m.weight)
+      m.bias.data.fill_(0.01)
+
+  def extractFeatures(self, dicts, config) :
+    colsValues = Features.extractColsFeatures(dicts, config, self.featureFunction, self.columns, self.incremental)
+    historyValues = Features.extractHistoryFeatures(dicts, config, self.historyNb)
+    prefixValues = Features.extractPrefixFeatures(dicts, config, self.prefixSize)
+    suffixValues = Features.extractSuffixFeatures(dicts, config, self.suffixSize)
+    return torch.cat([colsValues, historyValues, prefixValues, suffixValues])
 ################################################################################
 
 ################################################################################