def _export_model(self, estimator: tf.estimator.Estimator, save_location: str) -> None: """ Used to export your model in a format that can be used with Tf.Serving :param estimator: your estimator function """ # this should match the input shape of your model # TODO: update this to your input used in prediction/serving x1 = tf.feature_column.numeric_column("input", shape=[480, 640, 1]) # create a list in case you have more than one input feature_columns = [x1] feature_spec = tf.feature_column.make_parse_example_spec( feature_columns) export_input_fn = tf.estimator.export.build_parsing_serving_input_receiver_fn( feature_spec) # export the saved model estimator.export_savedmodel(save_location, export_input_fn)
def _export_model(self, estimator: tf.estimator.Estimator, save_location: str) -> None: """ Used to export your model in a format that can be used with Tf.Serving :param estimator: your estimator function """ # this should match the input shape of your model x1 = tf.feature_column.numeric_column( "input", shape=[ self.config["train_batch_size"], conf.c_maxnum, conf.c_maxlen ]) # create a list in case you have more than one input ## feature_columns = [x1] feature_columns = x1 feature_spec = tf.feature_column.make_parse_example_spec( feature_columns) export_input_fn = tf.estimator.export.build_parsing_serving_input_receiver_fn( feature_spec) # export the saved model estimator.export_savedmodel(save_location, export_input_fn)