network predicts 2 by 2 classes, refactored threshold to main

models.py

@@ -45,9 +45,9 @@ def get_top_cnn(cnn, numFeatures, maxLen, windowSize, domainFeatures, filters, k
     maxPool = GlobalMaxPooling1D()(cnn)
     cnnDropout = Dropout(cnnDropout)(maxPool)
     cnnDense = Dense(cnnHiddenDims, activation='relu')(cnnDropout)
-    cnnOutput = Dense(2, activation='softmax')(cnnDense)
+    cnnOutput1 = Dense(2, activation='softmax')(cnnDense)
+    cnnOutput2 = Dense(2, activation='softmax')(cnnDense)
 
     # We define a trainable model linking the
     # tweet inputs to the predictions
-    model = Model(inputs=inputList, outputs=cnnOutput)
-    return model
+    return Model(inputs=inputList, outputs=(cnnOutput1, cnnOutput2))