update dropout in a separate PR

Author: Ghostvv

rasa/nlu/classifiers/diet_classifier.py

@@ -1191,19 +1191,17 @@ def _combine_sparse_dense_features(
                     )
                 else:
                     _f = f
-
-                dense_f = self._tf_layers[f"sparse_to_dense.{name}"](_f)
-
-                if dense_dropout:
-                    dense_f = self._tf_layers[f"dense_input_dropout.{name}"](
-                        dense_f, self._training
-                    )
-
-                dense_features.append(dense_f)
+                dense_features.append(self._tf_layers[f"sparse_to_dense.{name}"](_f))
             else:
                 dense_features.append(f)
 
-        return tf.concat(dense_features, axis=-1) * mask
+        outputs = tf.concat(dense_features, axis=-1) * mask
+        if dense_dropout:
+            outputs = self._tf_layers[f"dense_input_dropout.{name}"](
+                outputs, self._training
+            )
+
+        return outputs
 
     def _features_as_seq_ids(
         self, features: List[Union[np.ndarray, tf.Tensor, tf.SparseTensor]], name: Text