frame_level_models.py 文件源码

def create_model(self, model_input, vocab_size, num_frames, **unused_params):
    """Creates a model which uses a stack of LSTMs to represent the video.

    Args:
      model_input: A 'batch_size' x 'max_frames' x 'num_features' matrix of
                   input features.
      vocab_size: The number of classes in the dataset.
      num_frames: A vector of length 'batch' which indicates the number of
           frames for each video (before padding).

    Returns:
      A dictionary with a tensor containing the probability predictions of the
      model in the 'predictions' key. The dimensions of the tensor are
      'batch_size' x 'num_classes'.
    """
    lstm_size = FLAGS.lstm_cells
    number_of_layers = FLAGS.lstm_layers

    ## Batch normalize the input
    stacked_lstm = tf.contrib.rnn.MultiRNNCell(
            [
                tf.contrib.rnn.BasicLSTMCell(
                    lstm_size, forget_bias=1.0, state_is_tuple=False)
                for _ in range(number_of_layers)
                ],
            state_is_tuple=False)

    loss = 0.0
    with tf.variable_scope("RNN"):
      outputs, state = tf.nn.dynamic_rnn(stacked_lstm, model_input,
                                         sequence_length=num_frames,
                                         dtype=tf.float32)

    aggregated_model = getattr(video_level_models,
                               FLAGS.video_level_classifier_model)
    return aggregated_model().create_model(
        model_input=state,
        vocab_size=vocab_size,
        **unused_params)