try normalising weights

toolkit.py

@@ -338,6 +338,8 @@ class ClassificationProject(object):
 
         self._fields = None
 
+        self._mean_train_weight = None
+
 
     @property
     def fields(self):
@@ -741,6 +743,13 @@ class ClassificationProject(object):
             np.random.shuffle(self._scores_train)
 
 
+    @property
+    def mean_train_weight(self):
+        if self._mean_train_weight is None:
+            self._mean_train_weight = np.mean(self.w_train*np.array(self.class_weight)[self.y_train.astype(int)])
+        return self._mean_train_weight
+
+
     @property
     def w_validation(self):
         "class weighted validation data weights"
@@ -749,7 +758,7 @@ class ClassificationProject(object):
             self._w_validation = np.array(self.w_train[split_index:])
             self._w_validation[self.y_train[split_index:]==0] *= self.class_weight[0]
             self._w_validation[self.y_train[split_index:]==1] *= self.class_weight[1]
-        return self._w_validation
+        return self._w_validation/self.mean_train_weight
 
 
     @property
@@ -1501,7 +1510,7 @@ class ClassificationProjectRNN(ClassificationProject):
                 x_input = self.get_input_list(x_batch)
                 yield (x_input,
                        y_train[shuffled_idx[start:start+int(self.batch_size)]],
-                       w_batch*np.array(self.class_weight)[y_batch.astype(int)])
+                       w_batch*np.array(self.class_weight)[y_batch.astype(int)]/self.mean_train_weight)
 
 
     @property