python类GetListOfFeatureNamesAndSizes()的实例源码-第2页-面圈网

multires_lstm_memory_deep_combine_chain_model.py 文件源码项目：youtube-8m 作者: wangheda 项目源码文件源码阅读 29 收藏 0 点赞 0 评论 0

def lstm(self, model_input, vocab_size, num_frames, sub_scope="", **unused_params):
    number_of_layers = FLAGS.lstm_layers
    lstm_sizes = map(int, FLAGS.lstm_cells.split(","))
    feature_names, feature_sizes = utils.GetListOfFeatureNamesAndSizes(
        FLAGS.feature_names, FLAGS.feature_sizes)
    sub_inputs = [tf.nn.l2_normalize(x, dim=2) for x in tf.split(model_input, feature_sizes, axis = 2)]

    assert len(lstm_sizes) == len(feature_sizes), \
      "length of lstm_sizes (={}) != length of feature_sizes (={})".format( \
      len(lstm_sizes), len(feature_sizes))

    states = []
    for i in xrange(len(feature_sizes)):
      with tf.variable_scope(sub_scope+"RNN%d" % i):
        sub_input = sub_inputs[i]
        lstm_size = lstm_sizes[i]
        ## Batch normalize the input
        stacked_lstm = tf.contrib.rnn.MultiRNNCell(
                [
                    tf.contrib.rnn.BasicLSTMCell(
                        lstm_size, forget_bias=1.0, state_is_tuple=True)
                    for _ in range(number_of_layers)
                    ],
                state_is_tuple=True)
        output, state = tf.nn.dynamic_rnn(stacked_lstm, sub_input,
                                         sequence_length=num_frames,
                                         swap_memory=FLAGS.rnn_swap_memory,
                                         dtype=tf.float32)
        states.extend(map(lambda x: x.c, state))
    final_state = tf.concat(states, axis = 1)
    return final_state

distillchain_lstm_cnn_deep_combine_chain_model.py 文件源码项目：youtube-8m 作者: wangheda 项目源码文件源码阅读 17 收藏 0 点赞 0 评论 0

def lstmoutput(self, model_input, vocab_size, num_frames):

    number_of_layers = FLAGS.lstm_layers

    lstm_sizes = map(int, FLAGS.lstm_cells.split(","))
    feature_names, feature_sizes = utils.GetListOfFeatureNamesAndSizes(
        FLAGS.feature_names, FLAGS.feature_sizes)
    sub_inputs = [tf.nn.l2_normalize(x, dim=2) for x in tf.split(model_input, feature_sizes, axis = 2)]

    assert len(lstm_sizes) == len(feature_sizes), \
      "length of lstm_sizes (={}) != length of feature_sizes (={})".format( \
      len(lstm_sizes), len(feature_sizes))

    outputs = []
    for i in xrange(len(feature_sizes)):
      with tf.variable_scope("RNN%d" % i):
        sub_input = sub_inputs[i]
        lstm_size = lstm_sizes[i]
        ## Batch normalize the input
        stacked_lstm = tf.contrib.rnn.MultiRNNCell(
                [
                    tf.contrib.rnn.BasicLSTMCell(
                        lstm_size, forget_bias=1.0, state_is_tuple=True)
                    for _ in range(number_of_layers)
                    ],
                state_is_tuple=True)

        output, state = tf.nn.dynamic_rnn(stacked_lstm, sub_input,
                                         sequence_length=num_frames,
                                         swap_memory=FLAGS.rnn_swap_memory,
                                         dtype=tf.float32)
        outputs.append(output)

    # concat
    final_output = tf.concat(outputs, axis=2)
    return final_output

inference-sample-error.py 文件源码项目：youtube-8m 作者: wangheda 项目源码文件源码阅读 27 收藏 0 点赞 0 评论 0

def main(unused_argv):
  logging.set_verbosity(tf.logging.INFO)

  # convert feature_names and feature_sizes to lists of values
  feature_names, feature_sizes = utils.GetListOfFeatureNamesAndSizes(
      FLAGS.feature_names, FLAGS.feature_sizes)

  if FLAGS.frame_features:
    reader = readers.YT8MFrameFeatureReader(feature_names=feature_names,
                                            feature_sizes=feature_sizes)
  else:
    reader = readers.YT8MAggregatedFeatureReader(feature_names=feature_names,
                                                 feature_sizes=feature_sizes)

  if FLAGS.output_file is "":
    raise ValueError("'output_file' was not specified. "
      "Unable to continue with inference.")

  if FLAGS.input_data_pattern is "":
    raise ValueError("'input_data_pattern' was not specified. "
      "Unable to continue with inference.")

  model = find_class_by_name(FLAGS.model,
                             [frame_level_models, video_level_models])()
  transformer_fn = find_class_by_name(FLAGS.feature_transformer, 
                                         [feature_transform])

  build_graph(reader,
              model,
              input_data_pattern=FLAGS.input_data_pattern,
              batch_size=FLAGS.batch_size,
              transformer_class=transformer_fn)

  saver = tf.train.Saver(max_to_keep=3, keep_checkpoint_every_n_hours=10000000000)

  inference(saver, FLAGS.train_dir,
            FLAGS.output_file, FLAGS.batch_size, FLAGS.top_k)

train.py 文件源码项目：yt8m 作者: forwchen 项目源码文件源码阅读 25 收藏 0 点赞 0 评论 0

def get_reader():
  # Convert feature_names and feature_sizes to lists of values.
  feature_names, feature_sizes = utils.GetListOfFeatureNamesAndSizes(
      FLAGS.feature_names, FLAGS.feature_sizes)

  if FLAGS.frame_features:
    reader = readers.YT8MFrameFeatureReader(
        feature_names=feature_names, feature_sizes=feature_sizes)
  else:
    reader = readers.YT8MAggregatedFeatureReader(
        feature_names=feature_names, feature_sizes=feature_sizes)

  return reader

train.py 文件源码项目：youtube-8m 作者: google 项目源码文件源码阅读 23 收藏 0 点赞 0 评论 0

def get_reader():
  # Convert feature_names and feature_sizes to lists of values.
  feature_names, feature_sizes = utils.GetListOfFeatureNamesAndSizes(
      FLAGS.feature_names, FLAGS.feature_sizes)

  if FLAGS.frame_features:
    reader = readers.YT8MFrameFeatureReader(
        feature_names=feature_names, feature_sizes=feature_sizes)
  else:
    reader = readers.YT8MAggregatedFeatureReader(
        feature_names=feature_names, feature_sizes=feature_sizes)

  return reader

train.py 文件源码项目：Video-Classification 作者: boyaolin 项目源码文件源码阅读 32 收藏 0 点赞 0 评论 0

def get_reader():
  # Convert feature_names and feature_sizes to lists of values.
  feature_names, feature_sizes = utils.GetListOfFeatureNamesAndSizes(
      FLAGS.feature_names, FLAGS.feature_sizes)

  if FLAGS.frame_features:
    reader = readers.YT8MFrameFeatureReader(
        feature_names=feature_names, feature_sizes=feature_sizes)
  else:
    reader = readers.YT8MAggregatedFeatureReader(
        feature_names=feature_names, feature_sizes=feature_sizes)

  return reader

train.py 文件源码项目：Youtube-8M-WILLOW 作者: antoine77340 项目源码文件源码阅读 19 收藏 0 点赞 0 评论 0

def get_reader():
  # Convert feature_names and feature_sizes to lists of values.
  feature_names, feature_sizes = utils.GetListOfFeatureNamesAndSizes(
      FLAGS.feature_names, FLAGS.feature_sizes)

  if FLAGS.frame_features:
    reader = readers.YT8MFrameFeatureReader(
        feature_names=feature_names, feature_sizes=feature_sizes)
  else:
    reader = readers.YT8MAggregatedFeatureReader(
        feature_names=feature_names, feature_sizes=feature_sizes)

  return reader

train.py 文件源码项目：Y8M 作者: mpekalski 项目源码文件源码阅读 18 收藏 0 点赞 0 评论 0

def get_reader():
  # Convert feature_names and feature_sizes to lists of values.
  feature_names, feature_sizes = utils.GetListOfFeatureNamesAndSizes(
      FLAGS.feature_names, FLAGS.feature_sizes)

  if FLAGS.frame_features:
    reader = readers.YT8MFrameFeatureReader(
        feature_names=feature_names, feature_sizes=feature_sizes)
  else:
    reader = readers.YT8MAggregatedFeatureReader(
        feature_names=feature_names, feature_sizes=feature_sizes)

  return reader

get_global_moments.py 文件源码项目：Y8M 作者: mpekalski 项目源码文件源码阅读 19 收藏 0 点赞 0 评论 0

def main(unused_argv):
  logging.set_verbosity(tf.logging.INFO)

  # convert feature_names and feature_sizes to lists of values
  feature_names, feature_sizes = utils.GetListOfFeatureNamesAndSizes(
      FLAGS.feature_names, FLAGS.feature_sizes)

  if FLAGS.frame_features:
    reader = readers.YT8MFrameFeatureReader(feature_names=feature_names,
                                            feature_sizes=feature_sizes)
  else:
    reader = readers.YT8MAggregatedFeatureReader(feature_names=feature_names,
                                                 feature_sizes=feature_sizes)

  if FLAGS.output_file is "":
    raise ValueError("'output_file' was not specified. "
      "Unable to continue with inference.")

  if FLAGS.input_data_pattern is "":
    raise ValueError("'input_data_pattern' was not specified. "
      "Unable to continue with inference.")

  calculate_moments(reader, 
                    feature_names,
                    feature_sizes,
                    FLAGS.input_data_pattern, 
                    FLAGS.input_data_pattern2, 
                    FLAGS.input_data_pattern3,
                    FLAGS.output_file,
                    FLAGS.batch_size)

train.py 文件源码项目：Youtube8mdataset_kagglechallenge 作者: jasonlee27 项目源码文件源码阅读 22 收藏 0 点赞 0 评论 0

def get_reader():
  # Convert feature_names and feature_sizes to lists of values.
  feature_names, feature_sizes = utils.GetListOfFeatureNamesAndSizes(
      FLAGS.feature_names, FLAGS.feature_sizes)

  if FLAGS.frame_features:
    reader = readers.YT8MFrameFeatureReader(
        feature_names=feature_names, feature_sizes=feature_sizes)
  else:
    reader = readers.YT8MAggregatedFeatureReader(
        feature_names=feature_names, feature_sizes=feature_sizes)

  return reader

train.py 文件源码项目：youtube 作者: taufikxu 项目源码文件源码阅读 26 收藏 0 点赞 0 评论 0

def get_reader():
  # Convert feature_names and feature_sizes to lists of values.
  feature_names, feature_sizes = utils.GetListOfFeatureNamesAndSizes(
      FLAGS.feature_names, FLAGS.feature_sizes)

  if FLAGS.frame_features:
    reader = readers.YT8MFrameFeatureReader(
        feature_names=feature_names, feature_sizes=feature_sizes)
  else:
    reader = readers.YT8MAggregatedFeatureReader(
        feature_names=feature_names, feature_sizes=feature_sizes)

  return reader

train.py 文件源码项目：kaggle-youtube-8m 作者: liufuyang 项目源码文件源码阅读 25 收藏 0 点赞 0 评论 0

def get_reader():
  # Convert feature_names and feature_sizes to lists of values.
  feature_names, feature_sizes = utils.GetListOfFeatureNamesAndSizes(
      FLAGS.feature_names, FLAGS.feature_sizes)

  if FLAGS.frame_features:
    reader = readers.YT8MFrameFeatureReader(
        feature_names=feature_names, feature_sizes=feature_sizes)
  else:
    reader = readers.YT8MAggregatedFeatureReader(
        feature_names=feature_names, feature_sizes=feature_sizes)

  return reader

train.py 文件源码项目：u8m_test 作者: hxkk 项目源码文件源码阅读 23 收藏 0 点赞 0 评论 0

def get_reader():
  # Convert feature_names and feature_sizes to lists of values.
  feature_names, feature_sizes = utils.GetListOfFeatureNamesAndSizes(
      FLAGS.feature_names, FLAGS.feature_sizes)

  if FLAGS.frame_features:
    reader = readers.YT8MFrameFeatureReader(
        feature_names=feature_names, feature_sizes=feature_sizes)
  else:
    reader = readers.YT8MAggregatedFeatureReader(
        feature_names=feature_names, feature_sizes=feature_sizes)

  return reader

forzhao_oldpool.py 文件源码项目：youtube-8m 作者: Tsingularity 项目源码文件源码阅读 24 收藏 0 点赞 0 评论 0

def get_reader():
  # Convert feature_names and feature_sizes to lists of values.
  feature_names, feature_sizes = utils.GetListOfFeatureNamesAndSizes(
      FLAGS.feature_names, FLAGS.feature_sizes)

  if FLAGS.frame_features:
    reader = readers.YT8MFrameFeatureReader(
        feature_names=feature_names, feature_sizes=feature_sizes)
  else:
    reader = readers.YT8MAggregatedFeatureReader(
        feature_names=feature_names, feature_sizes=feature_sizes)

  return reader

forzhao_train.py 文件源码项目：youtube-8m 作者: Tsingularity 项目源码文件源码阅读 19 收藏 0 点赞 0 评论 0

def get_reader():
  # Convert feature_names and feature_sizes to lists of values.
  feature_names, feature_sizes = utils.GetListOfFeatureNamesAndSizes(
      FLAGS.feature_names, FLAGS.feature_sizes)

  if FLAGS.frame_features:
    reader = readers.YT8MFrameFeatureReader(
        feature_names=feature_names, feature_sizes=feature_sizes)
  else:
    reader = readers.YT8MAggregatedFeatureReader(
        feature_names=feature_names, feature_sizes=feature_sizes)

  return reader

forzhao_infer.py 文件源码项目：youtube-8m 作者: Tsingularity 项目源码文件源码阅读 24 收藏 0 点赞 0 评论 0

def get_reader():
  # Convert feature_names and feature_sizes to lists of values.
  feature_names, feature_sizes = utils.GetListOfFeatureNamesAndSizes(
      FLAGS.feature_names, FLAGS.feature_sizes)

  if FLAGS.frame_features:
    reader = readers.YT8MFrameFeatureReader(
        feature_names=feature_names, feature_sizes=feature_sizes)
  else:
    reader = readers.YT8MAggregatedFeatureReader(
        feature_names=feature_names, feature_sizes=feature_sizes)

  return reader

forzhao_test.py 文件源码项目：youtube-8m 作者: Tsingularity 项目源码文件源码阅读 22 收藏 0 点赞 0 评论 0

def get_reader():
  # Convert feature_names and feature_sizes to lists of values.
  feature_names, feature_sizes = utils.GetListOfFeatureNamesAndSizes(
      FLAGS.feature_names, FLAGS.feature_sizes)

  if FLAGS.frame_features:
    reader = readers.YT8MFrameFeatureReader(
        feature_names=feature_names, feature_sizes=feature_sizes)
  else:
    reader = readers.YT8MAggregatedFeatureReader(
        feature_names=feature_names, feature_sizes=feature_sizes)

  return reader

new_train.py 文件源码项目：youtube-8m 作者: Tsingularity 项目源码文件源码阅读 19 收藏 0 点赞 0 评论 0

def get_reader():
  # Convert feature_names and feature_sizes to lists of values.
  feature_names, feature_sizes = utils.GetListOfFeatureNamesAndSizes(
      FLAGS.feature_names, FLAGS.feature_sizes)

  if FLAGS.frame_features:
    reader = readers.YT8MFrameFeatureReader(
        feature_names=feature_names, feature_sizes=feature_sizes)
  else:
    reader = readers.YT8MAggregatedFeatureReader(
        feature_names=feature_names, feature_sizes=feature_sizes)

  return reader

copy_graph.py 文件源码项目：youtube-8m 作者: Tsingularity 项目源码文件源码阅读 25 收藏 0 点赞 0 评论 0

def get_reader():
  # Convert feature_names and feature_sizes to lists of values.
  feature_names, feature_sizes = utils.GetListOfFeatureNamesAndSizes(
      FLAGS.feature_names, FLAGS.feature_sizes)

  if FLAGS.frame_features:
    reader = readers.YT8MFrameFeatureReader(
        feature_names=feature_names, feature_sizes=feature_sizes)
  else:
    reader = readers.YT8MAggregatedFeatureReader(
        feature_names=feature_names, feature_sizes=feature_sizes)

  return reader

train.py 文件源码项目：youtube-8m 作者: Tsingularity 项目源码文件源码阅读 24 收藏 0 点赞 0 评论 0

def get_reader():
  # Convert feature_names and feature_sizes to lists of values.
  feature_names, feature_sizes = utils.GetListOfFeatureNamesAndSizes(
      FLAGS.feature_names, FLAGS.feature_sizes)

  if FLAGS.frame_features:
    reader = readers.YT8MFrameFeatureReader(
        feature_names=feature_names, feature_sizes=feature_sizes)
  else:
    reader = readers.YT8MAggregatedFeatureReader(
        feature_names=feature_names, feature_sizes=feature_sizes)

  return reader