feat(tf): prevent out-of-memory errors and unpredictable timing of ga…

…rbage collection, in some cases

src/artificial_artwork/nst_tf_algorithm.py

@@ -244,6 +244,10 @@ def perform_nst(self, style_network):
 
         print(" --- Finished Learning Algorithm :) ---")
 
+        # prevent out-of-memory errors or other unexpected errors (due to the
+        # unpredictable timing of garbage collection) in some cases
+        self.session_runner.close()  # trigger garbage collection / memory release
+
     def iterate(self, image_model: t.Dict[str, Layer]):
         # Run the session on the train_step to minimize the total cost
         # This is our typical iterative learning loop / iteration, where the

src/artificial_artwork/tf_session_runner.py

@@ -46,6 +46,21 @@ def run(self, *args, **kwargs):
     @property
     def session(self):
         return self._proxy_subject.interactive_session
+
+    def close(self):
+        """Close the session and trigger garbage collection to free up resources.
+        
+        Closes the Tensorflow Interactive Session, which triggers garbage
+        collection to free up resources from memory.
+        
+        Calling this method can prevent Tensorflow Error: "An interactive
+        session is already active. This can cause out-of-memory errors or some
+        other unexpected errors (due to the unpredictable timing of garbage
+        collection) in some cases. You must explicitly call
+        `InteractiveSession.close()` to release resources held by the other
+        session(s). Please use `tf.Session()` if you intend to productionize.
+        """
+        self._proxy_subject.interactive_session.close()
 
     @classmethod
     def with_default_graph_reset(cls):