allow both None and 0 for deactivating dropout

toolkit.py

@@ -623,7 +623,7 @@ class ClassificationProject(object):
             # the (other) hidden layers
             for node_count, dropout_fraction in zip(self.nodes[start_layer:], self.dropout[start_layer:]):
                 self._model.add(Dense(node_count, activation=self.activation_function))
-                if dropout_fraction > 0:
+                if (dropout_fraction is not None) and (dropout_fraction > 0):
                     self._model.add(Dropout(rate=dropout_fraction))
             # last layer is one neuron (binary classification)
             self._model.add(Dense(1, activation=self.activation_function_output))