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Python eval_util.EvaluationMetrics方法代碼示例

本文整理匯總了Python中eval_util.EvaluationMetrics方法的典型用法代碼示例。如果您正苦於以下問題：Python eval_util.EvaluationMetrics方法的具體用法？Python eval_util.EvaluationMetrics怎麽用？Python eval_util.EvaluationMetrics使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類eval_util的用法示例。


在下文中一共展示了eval_util.EvaluationMetrics方法的12個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。

示例1: cal_gap

# 需要導入模塊: import eval_util [as 別名]
# 或者: from eval_util import EvaluationMetrics [as 別名]
def cal_gap(self):
        self.predict()
        evl_metrics = eval_util.EvaluationMetrics(4716, self.top_k)
        predictions_val = ensemble(self.pred_list, self.weights)

        print predictions_val.shape
        iteration_info_dict = evl_metrics.accumulate(predictions_val,
                                                         self.labels_val, np.zeros(predictions_val.shape[0]))
        epoch_info_dict = evl_metrics.get()
        print(("[email protected]%d:" %self.top_k) + str(epoch_info_dict['gap']))
開發者ID:forwchen，項目名稱:yt8m，代碼行數:12，代碼來源:valid.py

示例2: evaluate

# 需要導入模塊: import eval_util [as 別名]
# 或者: from eval_util import EvaluationMetrics [as 別名]
def evaluate():
  tf.set_random_seed(0)  # for reproducibility
  with tf.Graph().as_default():
    reader = readers.QuickDrawFeatureReader()

    model = find_class_by_name(FLAGS.model,
        [models])()
    label_loss_fn = find_class_by_name(FLAGS.label_loss, [losses])()

    if FLAGS.eval_data_pattern is "":
      raise IOError("'eval_data_pattern' was not specified. " +
                     "Nothing to evaluate.")

    build_graph(
        reader=reader,
        model=model,
        eval_data_pattern=FLAGS.eval_data_pattern,
        label_loss_fn=label_loss_fn,
        num_readers=FLAGS.num_readers,
        batch_size=FLAGS.batch_size)
    logging.info("built evaluation graph")
    prediction_batch = tf.get_collection("predictions")[0]
    label_batch = tf.get_collection("labels")[0]
    loss = tf.get_collection("loss")[0]
    summary_op = tf.get_collection("summary_op")[0]

    saver = tf.train.Saver(tf.global_variables())
    summary_writer = tf.summary.FileWriter(
        FLAGS.train_dir, graph=tf.get_default_graph())

    evl_metrics = eval_util.EvaluationMetrics(reader.num_classes, 2)

    last_global_step_val = -1
    while True:
      last_global_step_val = evaluation_loop(prediction_batch,
                                             label_batch, loss, summary_op,
                                             saver, summary_writer, evl_metrics,
                                             last_global_step_val)
      if FLAGS.run_once:
        break
開發者ID:machine-learning-challenge，項目名稱:mlc2017-online，代碼行數:42，代碼來源:eval.py

示例3: evaluate

# 需要導入模塊: import eval_util [as 別名]
# 或者: from eval_util import EvaluationMetrics [as 別名]
def evaluate():
  tf.set_random_seed(0)  # for reproducibility
  with tf.Graph().as_default():
    reader = readers.CatsVsDogsFeatureReader()

    model = find_class_by_name(FLAGS.model,
        [cvd_models])()
    label_loss_fn = find_class_by_name(FLAGS.label_loss, [losses])()

    if FLAGS.eval_data_pattern is "":
      raise IOError("'eval_data_pattern' was not specified. " +
                     "Nothing to evaluate.")

    build_graph(
        reader=reader,
        model=model,
        eval_data_pattern=FLAGS.eval_data_pattern,
        label_loss_fn=label_loss_fn,
        num_readers=FLAGS.num_readers,
        batch_size=FLAGS.batch_size)
    logging.info("built evaluation graph")
    image_id_batch = tf.get_collection("id_batch")[0]
    prediction_batch = tf.get_collection("predictions")[0]
    label_batch = tf.get_collection("labels")[0]
    loss = tf.get_collection("loss")[0]
    summary_op = tf.get_collection("summary_op")[0]

    saver = tf.train.Saver(tf.global_variables())
    summary_writer = tf.summary.FileWriter(
        FLAGS.train_dir, graph=tf.get_default_graph())

    evl_metrics = eval_util.EvaluationMetrics(reader.num_classes, 2)

    last_global_step_val = -1
    while True:
      last_global_step_val = evaluation_loop(image_id_batch, prediction_batch,
                                             label_batch, loss, summary_op,
                                             saver, summary_writer, evl_metrics,
                                             last_global_step_val)
      if FLAGS.run_once:
        break
開發者ID:machine-learning-challenge，項目名稱:mlc2017-online，代碼行數:43，代碼來源:eval.py

示例4: evaluate

# 需要導入模塊: import eval_util [as 別名]
# 或者: from eval_util import EvaluationMetrics [as 別名]
def evaluate():
  tf.set_random_seed(0)  # for reproducibility
  with tf.Graph().as_default():
    reader = readers.CatsVsDogsFeatureReader()

    model = find_class_by_name(FLAGS.model,
        [cvd_models])()
    label_loss_fn = find_class_by_name(FLAGS.label_loss, [losses])()

    if FLAGS.eval_data_pattern is "":
      raise IOError("'eval_data_pattern' was not specified. " +
                     "Nothing to evaluate.")

    build_graph(
        reader=reader,
        model=model,
        eval_data_pattern=FLAGS.eval_data_pattern,
        label_loss_fn=label_loss_fn,
        num_readers=FLAGS.num_readers,
        batch_size=FLAGS.batch_size)
    logging.info("built evaluation graph")
    image_id_batch = tf.get_collection("image_id_batch")[0]
    prediction_batch = tf.get_collection("predictions")[0]
    label_batch = tf.get_collection("labels")[0]
    loss = tf.get_collection("loss")[0]
    summary_op = tf.get_collection("summary_op")[0]

    saver = tf.train.Saver(tf.global_variables())
    summary_writer = tf.summary.FileWriter(
        FLAGS.train_dir, graph=tf.get_default_graph())

    evl_metrics = eval_util.EvaluationMetrics(reader.num_classes, 2)

    last_global_step_val = -1
    while True:
      last_global_step_val = evaluation_loop(image_id_batch, prediction_batch,
                                             label_batch, loss, summary_op,
                                             saver, summary_writer, evl_metrics,
                                             last_global_step_val)
      if FLAGS.run_once:
        break
開發者ID:SSUHan，項目名稱:google_ml_challenge，代碼行數:43，代碼來源:eval.py

示例5: evaluate

# 需要導入模塊: import eval_util [as 別名]
# 或者: from eval_util import EvaluationMetrics [as 別名]
def evaluate():
  tf.set_random_seed(0)  # for reproducibility
  with tf.Graph().as_default():
    reader = readers.MnistReader()

    model = find_class_by_name(FLAGS.model,
        [mnist_models])()
    label_loss_fn = find_class_by_name(FLAGS.label_loss, [losses])()

    if FLAGS.eval_data_pattern is "":
      raise IOError("'eval_data_pattern' was not specified. " +
                     "Nothing to evaluate.")

    build_graph(
        reader=reader,
        model=model,
        eval_data_pattern=FLAGS.eval_data_pattern,
        label_loss_fn=label_loss_fn,
        num_readers=FLAGS.num_readers,
        batch_size=FLAGS.batch_size)
    logging.info("built evaluation graph")
    prediction_batch = tf.get_collection("predictions")[0]
    label_batch = tf.get_collection("labels")[0]
    loss = tf.get_collection("loss")[0]
    summary_op = tf.get_collection("summary_op")[0]

    saver = tf.train.Saver(tf.global_variables())
    summary_writer = tf.summary.FileWriter(
        FLAGS.train_dir, graph=tf.get_default_graph())

    evl_metrics = eval_util.EvaluationMetrics(reader.num_classes, 2)

    last_global_step_val = -1
    while True:
      last_global_step_val = evaluation_loop(prediction_batch,
                                             label_batch, loss, summary_op,
                                             saver, summary_writer, evl_metrics,
                                             last_global_step_val)
      if FLAGS.run_once:
        break
開發者ID:machine-learning-challenge，項目名稱:tutorial_mnist，代碼行數:42，代碼來源:eval.py

示例6: evaluate

# 需要導入模塊: import eval_util [as 別名]
# 或者: from eval_util import EvaluationMetrics [as 別名]
def evaluate():
  tf.set_random_seed(0)  # for reproducibility
  with tf.Graph().as_default():
    # 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)

    model = find_class_by_name(FLAGS.model,
        [frame_level_models, video_level_models])()
    label_loss_fn = find_class_by_name(FLAGS.label_loss, [losses])()

    if FLAGS.eval_data_pattern is "":
      raise IOError("'eval_data_pattern' was not specified. " +
                     "Nothing to evaluate.")

    build_graph(
        reader=reader,
        model=model,
        eval_data_pattern=FLAGS.eval_data_pattern,
        label_loss_fn=label_loss_fn,
        num_readers=FLAGS.num_readers,
        batch_size=FLAGS.batch_size)
    logging.info("built evaluation graph")
    video_id_batch = tf.get_collection("video_id_batch")[0]
    prediction_batch = tf.get_collection("predictions")[0]
    label_batch = tf.get_collection("labels")[0]
    loss = tf.get_collection("loss")[0]
    summary_op = tf.get_collection("summary_op")[0]

    saver = tf.train.Saver(tf.global_variables())
    summary_writer = tf.summary.FileWriter(
        FLAGS.train_dir, graph=tf.get_default_graph())

    evl_metrics = eval_util.EvaluationMetrics(reader.num_classes, FLAGS.top_k)

    last_global_step_val = -1
    while True:
      last_global_step_val = evaluation_loop(video_id_batch, prediction_batch,
                                             label_batch, loss, summary_op,
                                             saver, summary_writer, evl_metrics,
                                             last_global_step_val)
      if FLAGS.run_once:
        break
開發者ID:wangheda，項目名稱:youtube-8m，代碼行數:52，代碼來源:eval.py

示例7: evaluate

# 需要導入模塊: import eval_util [as 別名]
# 或者: from eval_util import EvaluationMetrics [as 別名]
def evaluate():
  tf.set_random_seed(0)  # for reproducibility
  with tf.Graph().as_default():
    # 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)

    model = find_class_by_name(FLAGS.model,
        [labels_autoencoder])()
    label_loss_fn = find_class_by_name(FLAGS.label_loss, [losses])()

    if FLAGS.eval_data_pattern is "":
      raise IOError("'eval_data_pattern' was not specified. " +
                     "Nothing to evaluate.")

    build_graph(
        reader=reader,
        model=model,
        eval_data_pattern=FLAGS.eval_data_pattern,
        label_loss_fn=label_loss_fn,
        num_readers=FLAGS.num_readers,
        batch_size=FLAGS.batch_size)
    logging.info("built evaluation graph")
    video_id_batch = tf.get_collection("video_id_batch")[0]
    prediction_batch = tf.get_collection("predictions")[0]
    label_batch = tf.get_collection("labels")[0]
    loss = tf.get_collection("loss")[0]
    summary_op = tf.get_collection("summary_op")[0]

    saver = tf.train.Saver(tf.global_variables())
    summary_writer = tf.summary.FileWriter(
        FLAGS.train_dir, graph=tf.get_default_graph())

    evl_metrics = eval_util.EvaluationMetrics(reader.num_classes, FLAGS.top_k)

    last_global_step_val = -1
    while True:
      last_global_step_val = evaluation_loop(video_id_batch, prediction_batch,
                                             label_batch, loss, summary_op,
                                             saver, summary_writer, evl_metrics,
                                             last_global_step_val)
      if FLAGS.run_once:
        break
開發者ID:wangheda，項目名稱:youtube-8m，代碼行數:52，代碼來源:eval_embedding.py

示例8: evaluate

# 需要導入模塊: import eval_util [as 別名]
# 或者: from eval_util import EvaluationMetrics [as 別名]
def evaluate():
  tf.set_random_seed(0)  # for reproducibility
  with tf.Graph().as_default():
    # convert feature_names and feature_sizes to lists of values
    feature_names, feature_sizes = utils.GetListOfFeatureNamesAndSizes(
        FLAGS.feature_names, FLAGS.feature_sizes)
    distill_names, distill_sizes = utils.GetListOfFeatureNamesAndSizes(
        FLAGS.distill_names, FLAGS.distill_sizes)

    if FLAGS.frame_features:
      reader1 = readers.YT8MFrameFeatureReader(feature_names=feature_names,
                                              feature_sizes=feature_sizes)
    else:
      reader1 = readers.YT8MAggregatedFeatureReader(feature_names=feature_names,
                                                   feature_sizes=feature_sizes)
    reader2 = readers.YT8MAggregatedFeatureReader(
        feature_names=distill_names, feature_sizes=distill_sizes)

    model = find_class_by_name(FLAGS.model,
        [frame_level_models, video_level_models])()
    label_loss_fn = find_class_by_name(FLAGS.label_loss, [losses])()

    if FLAGS.eval_data_pattern is "":
      raise IOError("'eval_data_pattern' was not specified. " +
                     "Nothing to evaluate.")

    build_graph(
        reader1=reader1,
        reader2=reader2,
        model=model,
        eval_data_pattern=FLAGS.eval_data_pattern,
        distill_data_pattern=FLAGS.distill_data_pattern,
        label_loss_fn=label_loss_fn,
        num_readers=FLAGS.num_readers,
        batch_size=FLAGS.batch_size)
    logging.info("built evaluation graph")
    video_id_batch = tf.get_collection("video_id_batch")[0]
    unused_id_batch = tf.get_collection("unused_id_batch")[0]
    prediction_batch = tf.get_collection("predictions")[0]
    label_batch = tf.get_collection("labels")[0]
    loss = tf.get_collection("loss")[0]
    summary_op = tf.get_collection("summary_op")[0]

    saver = tf.train.Saver(tf.global_variables())
    summary_writer = tf.summary.FileWriter(
        FLAGS.train_dir, graph=tf.get_default_graph())

    evl_metrics = eval_util.EvaluationMetrics(reader1.num_classes, FLAGS.top_k)

    last_global_step_val = -1
    while True:
      last_global_step_val = evaluation_loop(video_id_batch, unused_id_batch, prediction_batch,
                                             label_batch, loss, summary_op,
                                             saver, summary_writer, evl_metrics,
                                             last_global_step_val)
      if FLAGS.run_once:
        break
開發者ID:wangheda，項目名稱:youtube-8m，代碼行數:59，代碼來源:eval_distill.py

示例9: evaluate

# 需要導入模塊: import eval_util [as 別名]
# 或者: from eval_util import EvaluationMetrics [as 別名]
def evaluate():
  tf.set_random_seed(0)  # for reproducibility
  with tf.Graph().as_default():
    # 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.distill_data_pattern is not None:
      distill_reader = readers.YT8MAggregatedFeatureReader(feature_names=["predictions"],
                                                   feature_sizes=[4716])
    else:
      distill_reader = None

    model = find_class_by_name(FLAGS.model,
        [frame_level_models, video_level_models])()
    label_loss_fn = find_class_by_name(FLAGS.label_loss, [losses])()
    transformer_class = find_class_by_name(FLAGS.feature_transformer, [feature_transform])

    if FLAGS.eval_data_pattern is "":
      raise IOError("'eval_data_pattern' was not specified. " +
                     "Nothing to evaluate.")

    build_graph(
        reader=reader,
        model=model,
        eval_data_pattern=FLAGS.eval_data_pattern,
        label_loss_fn=label_loss_fn,
        num_readers=FLAGS.num_readers,
        transformer_class=transformer_class,
        distill_reader=distill_reader,
        batch_size=FLAGS.batch_size)

    logging.info("built evaluation graph")
    video_id_batch = tf.get_collection("video_id_batch")[0]
    prediction_batch = tf.get_collection("predictions")[0]
    label_batch = tf.get_collection("labels")[0]
    loss = tf.get_collection("loss")[0]
    summary_op = tf.get_collection("summary_op")[0]

    saver = tf.train.Saver(tf.global_variables())
    summary_writer = tf.summary.FileWriter(
        FLAGS.train_dir, graph=tf.get_default_graph())

    evl_metrics = eval_util.EvaluationMetrics(reader.num_classes, FLAGS.top_k)

    last_global_step_val = -1
    while True:
      last_global_step_val = evaluation_loop(video_id_batch, prediction_batch,
                                             label_batch, loss, summary_op,
                                             saver, summary_writer, evl_metrics,
                                             last_global_step_val)
      if FLAGS.run_once:
        break
開發者ID:wangheda，項目名稱:youtube-8m，代碼行數:62，代碼來源:eval.py

示例10: evaluate

# 需要導入模塊: import eval_util [as 別名]
# 或者: from eval_util import EvaluationMetrics [as 別名]
def evaluate():
  tf.set_random_seed(0)  # for reproducibility
  with tf.Graph().as_default():
    # 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(
        num_classes = FLAGS.truncated_num_classes,
        decode_zlib = FLAGS.decode_zlib,
        feature_names=feature_names, feature_sizes=feature_sizes)
    else:
      reader = readers.YT8MAggregatedFeatureReader(
        num_classes = FLAGS.truncated_num_classes,
        decode_zlib = FLAGS.decode_zlib,
        feature_names=feature_names, feature_sizes=feature_sizes)

    model = find_class_by_name(FLAGS.model,
        [frame_level_models, video_level_models,
         xp_frame_level_models, xp_video_level_models])()
    label_loss_fn = find_class_by_name(FLAGS.label_loss, [losses])()

    if FLAGS.eval_data_pattern is "":
      raise IOError("'eval_data_pattern' was not specified. " +
                     "Nothing to evaluate.")

    build_graph(
        reader=reader,
        model=model,
        eval_data_pattern=FLAGS.eval_data_pattern,
        label_loss_fn=label_loss_fn,
        num_readers=FLAGS.num_readers,
        batch_size=FLAGS.batch_size)
    logging.info("built evaluation graph")
    video_id_batch = tf.get_collection("video_id_batch")[0]
    prediction_batch = tf.get_collection("predictions")[0]
    label_batch = tf.get_collection("labels")[0]
    loss = tf.get_collection("loss")[0]
    summary_op = tf.get_collection("summary_op")[0]

    saver = tf.train.Saver(tf.global_variables())
    summary_writer = tf.summary.FileWriter(
        FLAGS.train_dir, graph=tf.get_default_graph())

    evl_metrics = eval_util.EvaluationMetrics(reader.num_classes, FLAGS.top_k)

    last_global_step_val = -1
    while True:
      last_global_step_val = evaluation_loop(video_id_batch, prediction_batch,
                                             label_batch, loss, summary_op,
                                             saver, summary_writer, evl_metrics,
                                             last_global_step_val)
      if FLAGS.run_once:
        break
開發者ID:mpekalski，項目名稱:Y8M，代碼行數:57，代碼來源:eval.py

示例11: evaluate

# 需要導入模塊: import eval_util [as 別名]
# 或者: from eval_util import EvaluationMetrics [as 別名]
def evaluate():
  tf.set_random_seed(0)  # for reproducibility
  with tf.Graph().as_default():
    # 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(
               num_classes = FLAGS.truncated_num_classes,
               decode_zlib = FLAGS.decode_zlib,
               feature_names = feature_names,
               feature_sizes = feature_sizes)
    else:
      reader = readers.YT8MAggregatedFeatureReader(
               num_classes = FLAGS.truncated_num_classes,
               decode_zlib = FLAGS.decode_zlib,
               feature_names = feature_names,
               feature_sizes = feature_sizes)

    model = find_class_by_name(FLAGS.model,
        [frame_level_models, video_level_models,
         xp_frame_level_models, xp_video_level_models])()
    label_loss_fn = find_class_by_name(FLAGS.label_loss, [losses])()

    if FLAGS.eval_data_pattern is "":
      raise IOError("'eval_data_pattern' was not specified. " +
                     "Nothing to evaluate.")

    build_graph(
        reader=reader,
        model=model,
        eval_data_pattern=FLAGS.eval_data_pattern,
        label_loss_fn=label_loss_fn,
        num_readers=FLAGS.num_readers,
        batch_size=FLAGS.batch_size)
    logging.info("built evaluation graph")
    video_id_batch = tf.get_collection("video_id_batch")[0]
    prediction_batch = tf.get_collection("predictions")[0]
    label_batch = tf.get_collection("labels")[0]
    loss = tf.get_collection("loss")[0]
    summary_op = tf.get_collection("summary_op")[0]

    saver = tf.train.Saver(tf.global_variables())
    summary_writer = tf.summary.FileWriter(
        FLAGS.train_dir, graph=tf.get_default_graph())

    evl_metrics = eval_util.EvaluationMetrics(reader.num_classes, FLAGS.top_k)

    last_global_step_val = -1
    while True:
      last_global_step_val = evaluation_loop(video_id_batch, prediction_batch,
                                             label_batch, loss, summary_op,
                                             saver, summary_writer, evl_metrics,
                                             last_global_step_val)
      if FLAGS.run_once:
        break
開發者ID:mpekalski，項目名稱:Y8M，代碼行數:59，代碼來源:eval.py

示例12: evaluate

# 需要導入模塊: import eval_util [as 別名]
# 或者: from eval_util import EvaluationMetrics [as 別名]
def evaluate():
  tf.set_random_seed(0)  # for reproducibility
  with tf.Graph().as_default():
    # 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)

    model = find_class_by_name(FLAGS.model,
        [frame_level_models, video_level_models])()
    label_loss_fn = find_class_by_name(FLAGS.label_loss, [losses])()

    if FLAGS.eval_data_pattern is "":
      raise IOError("'eval_data_pattern' was not specified. " +
                     "Nothing to evaluate.")

    build_graph(
        reader=reader,
        model=model,
        eval_data_pattern=FLAGS.eval_data_pattern,
        label_loss_fn=label_loss_fn,
        num_readers=FLAGS.num_readers,
        batch_size=FLAGS.batch_size)
    logging.info("built evaluation graph")
    video_id_batch = tf.get_collection("video_id_batch")[0]
    prediction_batch = tf.get_collection("predictions")[0]
    ### Newly
    # coarse_prediction_batch = tf.get_collection("coarse_predictions")[0]
    # coarse_label_batch = tf.get_collection("coarse_labels")[0]
    # coarse_loss = tf.get_collection("coarse_loss")[0]
    ###
    label_batch = tf.get_collection("labels")[0]
    loss = tf.get_collection("loss")[0]
    summary_op = tf.get_collection("summary_op")[0]

    saver = tf.train.Saver(tf.global_variables())
    summary_writer = tf.summary.FileWriter(
        FLAGS.train_dir, graph=tf.get_default_graph())

    evl_metrics = eval_util.EvaluationMetrics(reader.num_classes, FLAGS.top_k)

    last_global_step_val = -1
    while True:
      last_global_step_val = evaluation_loop(video_id_batch, prediction_batch,
                                             label_batch, loss, summary_op,
                                             saver, summary_writer, evl_metrics,
                                             last_global_step_val)
                                             ### Newly
                                             # coarse_prediction_batch, coarse_label_batch, coarse_loss) 
                                             ###
      if FLAGS.run_once:
        break
開發者ID:Tsingularity，項目名稱:youtube-8m，代碼行數:60，代碼來源:eval.py


注：本文中的eval_util.EvaluationMetrics方法示例由純淨天空整理自Github/MSDocs等開源代碼及文檔管理平台，相關代碼片段篩選自各路編程大神貢獻的開源項目，源碼版權歸原作者所有，傳播和使用請參考對應項目的License；未經允許，請勿轉載。