def serving_input_fn(self, hparams):
    """For serving/predict, assume that only video frames are provided."""
    video_input_frames = tf.placeholder(
        dtype=tf.float32,
        shape=[
            None, hparams.video_num_input_frames, self.frame_width,
            self.frame_height, self.num_channels
        ])

    # TODO(michalski): add support for passing input_action and input_reward.
    return tf.estimator.export.ServingInputReceiver(
        features={"inputs": video_input_frames},
        receiver_tensors=video_input_frames)
def serving_input_fn ( self , hparams ) : video_input_frames = tf . placeholder ( dtype = tf . float32 , shape = [ None , hparams . video_num_input_frames , self . frame_width , self . frame_height , self . num_channels ] ) return tf . estimator . export . ServingInputReceiver ( features = { "inputs" : video_input_frames } , receiver_tensors = video_input_frames )
For serving/predict, assume that only video frames are provided.