discrete_action_predictor.py 文件源码

def predict(self, states):
        """ Returns values for each state
        :param states states as feature -> value dict
        """
        previous_workspace = workspace.CurrentWorkspace()
        workspace.SwitchWorkspace(self._workspace_id)
        for input_blob in states:
            workspace.FeedBlob(
                input_blob,
                np.atleast_1d(states[input_blob]).astype(np.float32)
            )
        workspace.RunNet(self._net)
        result = {
            output: workspace.FetchBlob(output)
            for output in self._output_blobs
        }
        workspace.SwitchWorkspace(previous_workspace)
        return result