[GitHub] [madlib] khannaekta commented on a change in pull request #522: DL: Remove keras dependency

[GitBox <gi...@apache.org>]

khannaekta commented on a change in pull request #522:
URL: https://github.com/apache/madlib/pull/522#discussion_r532992630



##########
File path: src/ports/postgres/modules/deep_learning/test/unit_tests/test_madlib_keras.py_in
##########
@@ -312,103 +201,129 @@ class MadlibKerasFitTestCase(unittest.TestCase):
             self.model.to_json(), self.compile_params, self.fit_params, 0,
             self.dist_key_mapping, 0, 4, self.total_images_per_seg, False,
             self.accessible_gpus_for_seg, previous_weights.tostring(), True, **k)
+
         image_count = new_state
         self.assertEqual(ending_image_count, image_count)
-        # set_session is only called for the last buffer
-        self.assertEqual(0, self.subject.K.set_session.call_count)
-        # Clear session and sess.close must not get called for the middle buffer
-        self.assertEqual(0, self.subject.K.clear_session.call_count)
-        self.assertTrue('segment_model' not in k['SD'])
-        self.assertTrue('cache_set' not in k['SD'])
-        self.assertTrue(k['SD']['x_train'])
-        self.assertTrue(k['SD']['y_train'])
-
-    def _test_fit_transition_last_buffer_pass(self, is_platform_pg):
-        #TODO should we mock tensorflow's close_session and keras'
-        # clear_session instead of mocking the function `K.clear_session`
-        self.subject.K.set_session = Mock()
-        self.subject.K.clear_session = Mock()
+        self.assertTrue('sess' not in k['GD'])
+        self.assertTrue('segment_model' not in k['GD'])
+        self.assertTrue('cache_set' not in k['GD'])
+        self.assertTrue(k['GD']['x_train'])
+        self.assertTrue(k['GD']['y_train'])
+
+    def _test_fit_transition_last_buffer_pass(self, is_platform_pg, **kwargs):
         self.subject.is_platform_pg = Mock(return_value = is_platform_pg)
 
         starting_image_count = 2*len(self.dependent_var_int)
         ending_image_count = starting_image_count + len(self.dependent_var_int)
 
-        # last iteration Call
-
-        multiplied_weights = mult(self.total_images_per_seg[0],self.model_weights)
-
-        self.subject.compile_and_set_weights(self.model, self.compile_params,
-                                             '/cpu:0', self.serialized_weights)
-        k = {'SD': {'segment_model' :self.model, 'sess': Mock()}}
-
         state = starting_image_count
         new_state = self.subject.fit_transition(
             state, self.dependent_var, self.independent_var,
             self.dependent_var_shape, self.independent_var_shape,
             self.model.to_json(), None, self.fit_params, 0,
             self.dist_key_mapping, 0, 4, self.total_images_per_seg, False,
-            self.accessible_gpus_for_seg, self.dummy_prev_weights, True, **k)
+            self.accessible_gpus_for_seg, self.dummy_prev_weights, "todo-remove",
+            **kwargs)
 
         state = np.fromstring(new_state, dtype=np.float32)
         image_count = state[0]
-        weights = np.rint(state[1:]).astype(np.int)
+        # We need to assert that the weights should be multiplied by final image count.
+        # So we divide and round the weights so that we can compare them with
+        # the original weights. They will only be equal if the learnt weights
+        # only go up or down by 1 which seems to be the case for our unit test.
+        # There might be a better way to assert this but this is good enough for now.
+        weights = np.rint(state[1:]/self.total_images_per_seg[0]).astype(np.int)
+        self.assertTrue(((weights == self.model_weights).all()))

Review comment:
       Finally, got the test to mock the call to the fit. :) 




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