Method post_process_model

tensorpack/contrib/keras.py:56–76

(model)

        update_ops_backup = backup_collection([tf.GraphKeys.UPDATE_OPS])

        def post_process_model(model):
            added_trainable_names = {x.name for x in tf.trainable_variables()}
            restore_collection(trainable_backup)

            for v in model.weights:
                # In Keras, the collection is not respected and could contain non-trainable vars.
                # We put M.weights into the collection instead.
                if v.name not in old_trainable_names and v.name in added_trainable_names:
                    tf.add_to_collection(tf.GraphKeys.TRAINABLE_VARIABLES, v)
            new_trainable_names = {x.name for x in tf.trainable_variables()}

            for n in added_trainable_names:
                if n not in new_trainable_names:
                    logger.warn("Keras created trainable variable '{}' which is actually not trainable. "
                                "This was automatically corrected.".format(n))

            # Keras models might not use this collection at all (in some versions).
            # This is a BC-breaking change of tf.keras: https://github.com/tensorflow/tensorflow/issues/19643
            restore_collection(update_ops_backup)
            for op in model.updates:
                tf.add_to_collection(tf.GraphKeys.UPDATE_OPS, op)

        if self.cached_model is None:
            assert not reuse