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Python tensorflow.sparse_to_indicator方法代碼示例

本文整理匯總了Python中tensorflow.sparse_to_indicator方法的典型用法代碼示例。如果您正苦於以下問題：Python tensorflow.sparse_to_indicator方法的具體用法？Python tensorflow.sparse_to_indicator怎麽用？Python tensorflow.sparse_to_indicator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tensorflow的用法示例。


在下文中一共展示了tensorflow.sparse_to_indicator方法的8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: prepare_serialized_examples

# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import sparse_to_indicator [as 別名]
def prepare_serialized_examples(self, serialized_examples):
    # set the mapping from the fields to data types in the proto
    num_features = len(self.feature_names)
    assert num_features > 0, "self.feature_names is empty!"
    assert len(self.feature_names) == len(self.feature_sizes), \
    "length of feature_names (={}) != length of feature_sizes (={})".format( \
    len(self.feature_names), len(self.feature_sizes))

    feature_map = {"video_id": tf.FixedLenFeature([], tf.string),
                   "labels": tf.VarLenFeature(tf.int64)}
    for feature_index in range(num_features):
      feature_map[self.feature_names[feature_index]] = tf.FixedLenFeature(
          [self.feature_sizes[feature_index]], tf.float32)

    features = tf.parse_example(serialized_examples, features=feature_map)

    labels = tf.sparse_to_indicator(features["labels"], self.num_classes)
    labels.set_shape([None, self.num_classes])
    concatenated_features = tf.concat([
        features[feature_name] for feature_name in self.feature_names], 1)

    return features["video_id"], concatenated_features, labels, tf.ones([tf.shape(serialized_examples)[0]])
開發者ID:antoine77340，項目名稱:Youtube-8M-WILLOW，代碼行數:24，代碼來源:readers.py

示例2: prepare_reader

# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import sparse_to_indicator [as 別名]
def prepare_reader(self, filename_queue, batch_size=1024):

    reader = tf.TFRecordReader()
    _, serialized_examples = reader.read_up_to(filename_queue, batch_size)

    # set the mapping from the fields to data types in the proto
    num_features = len(self.feature_names)
    assert num_features > 0, "self.feature_names is empty!"
    assert len(self.feature_names) == len(self.feature_sizes), \
    "length of feature_names (={}) != length of feature_sizes (={})".format( \
    len(self.feature_names), len(self.feature_sizes))

    feature_map = {"video_id": tf.FixedLenFeature([], tf.string),
                   "labels": tf.VarLenFeature(tf.int64)}
    for feature_index in range(num_features):
      feature_map[self.feature_names[feature_index]] = tf.FixedLenFeature(
          [self.feature_sizes[feature_index]], tf.float32)

    features = tf.parse_example(serialized_examples, features=feature_map)
    labels = tf.sparse_to_indicator(features["labels"], self.num_classes)
    labels.set_shape([None, self.num_classes])
    concatenated_features = tf.concat([
        features[feature_name] for feature_name in self.feature_names], 1)

    return features["video_id"], concatenated_features, labels, tf.ones([tf.shape(serialized_examples)[0]])
開發者ID:wangheda，項目名稱:youtube-8m，代碼行數:27，代碼來源:readers.py

示例3: prepare_serialized_examples

# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import sparse_to_indicator [as 別名]
def prepare_serialized_examples(self, serialized_examples):
    # set the mapping from the fields to data types in the proto
    num_features = len(self.feature_names)
    assert num_features > 0, "self.feature_names is empty!"
    assert len(self.feature_names) == len(self.feature_sizes), \
    "length of feature_names (={}) != length of feature_sizes (={})".format( \
    len(self.feature_names), len(self.feature_sizes))

    feature_map = {"id": tf.FixedLenFeature([], tf.string),
                   "labels": tf.VarLenFeature(tf.int64)}
    for feature_index in range(num_features):
      feature_map[self.feature_names[feature_index]] = tf.FixedLenFeature(
          [self.feature_sizes[feature_index]], tf.float32)

    features = tf.parse_example(serialized_examples, features=feature_map)
    labels = tf.sparse_to_indicator(features["labels"], self.num_classes)
    labels.set_shape([None, self.num_classes])
    concatenated_features = tf.concat([
        features[feature_name] for feature_name in self.feature_names], 1)

    return features["id"], concatenated_features, labels, tf.ones([tf.shape(serialized_examples)[0]])
開發者ID:miha-skalic，項目名稱:youtube8mchallenge，代碼行數:23，代碼來源:readers.py

示例4: prepare_serialized_examples

# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import sparse_to_indicator [as 別名]
def prepare_serialized_examples(self, serialized_examples):
        # set the mapping from the fields to data types in the proto
        num_features = len(self.feature_names)
        assert num_features > 0, "self.feature_names is empty!"
        assert len(self.feature_names) == len(self.feature_sizes), \
        "length of feature_names (={}) != length of feature_sizes (={})".format( \
        len(self.feature_names), len(self.feature_sizes))

        feature_map = {"id": tf.FixedLenFeature([], tf.string),
                       "labels": tf.VarLenFeature(tf.int64)}
        for feature_index in range(num_features):
            feature_map[self.feature_names[feature_index]] = tf.FixedLenFeature(
                [self.feature_sizes[feature_index]], tf.float32)

        features = tf.parse_example(serialized_examples, features=feature_map)
        labels = tf.sparse_to_indicator(features["labels"], self.num_classes)
        labels.set_shape([None, self.num_classes])
        concatenated_features = tf.concat([
            features[feature_name] for feature_name in self.feature_names], 1)

        return features["id"], concatenated_features, labels, tf.ones([tf.shape(serialized_examples)[0]])
開發者ID:pomonam，項目名稱:AttentionCluster，代碼行數:23，代碼來源:readers.py

示例5: prepare_serialized_examples

# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import sparse_to_indicator [as 別名]
def prepare_serialized_examples(self, serialized_examples):
    # set the mapping from the fields to data types in the proto
    num_features = len(self.feature_names)
    assert num_features > 0, "self.feature_names is empty!"
    assert len(self.feature_names) == len(self.feature_sizes), \
    "length of feature_names (={}) != length of feature_sizes (={})".format( \
    len(self.feature_names), len(self.feature_sizes))

    feature_map = {"video_id": tf.FixedLenFeature([], tf.string),
                   "labels": tf.VarLenFeature(tf.int64)}
    for feature_index in range(num_features):
      feature_map[self.feature_names[feature_index]] = tf.FixedLenFeature(
          [self.feature_sizes[feature_index]], tf.float32)

    features = tf.parse_example(serialized_examples, features=feature_map)
    labels = tf.sparse_to_indicator(features["labels"], self.num_classes)
    labels.set_shape([None, self.num_classes])
    concatenated_features = tf.concat([
        features[feature_name] for feature_name in self.feature_names], 1)

    return features["video_id"], concatenated_features, labels, tf.ones([tf.shape(serialized_examples)[0]])
開發者ID:mpekalski，項目名稱:Y8M，代碼行數:23，代碼來源:readers.py

示例6: prepare_writer

# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import sparse_to_indicator [as 別名]
def prepare_writer(self, filename_queue, batch_size=1024):
    """Creates a single reader thread for pre-aggregated YouTube 8M Examples.

    Args:
      filename_queue: A tensorflow queue of filename locations.

    Returns:
      A tuple of video indexes, features, labels, and padding data.
    """
    reader = tf.TFRecordReader()
    _, serialized_examples = reader.read_up_to(filename_queue, batch_size)

    # set the mapping from the fields to data types in the proto
    num_features = len(self.feature_names)
    assert num_features > 0, "self.feature_names is empty!"
    assert len(self.feature_names) == len(self.feature_sizes), \
    "length of feature_names (={}) != length of feature_sizes (={})".format( \
    len(self.feature_names), len(self.feature_sizes))

    feature_map = {"video_id": tf.FixedLenFeature([], tf.string),
                   "labels": tf.VarLenFeature(tf.int64)}
    for feature_index in range(num_features):
      feature_map[self.feature_names[feature_index]] = tf.FixedLenFeature(
          [self.feature_sizes[feature_index]], tf.float32)

    features = tf.parse_example(serialized_examples, features=feature_map)
    labels = tf.sparse_to_indicator(features["labels"], self.num_classes)
    labels.set_shape([None, self.num_classes])
    concatenated_features = tf.concat([
        features[feature_name] for feature_name in self.feature_names], 1)

    return features["video_id"], concatenated_features, labels, tf.ones([tf.shape(serialized_examples)[0]])
開發者ID:wangheda，項目名稱:youtube-8m，代碼行數:34，代碼來源:writers.py

示例7: prepare_reader

# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import sparse_to_indicator [as 別名]
def prepare_reader(self, filename_queue, batch_size=1024):
    """Creates a single reader thread for pre-aggregated YouTube 8M Examples.

    Args:
      filename_queue: A tensorflow queue of filename locations.

    Returns:
      A tuple of video indexes, features, labels, and padding data.
    """
    reader = tf.TFRecordReader()
    _, serialized_examples = reader.read_up_to(filename_queue, batch_size)

    # set the mapping from the fields to data types in the proto
    num_features = len(self.feature_names)
    assert num_features > 0, "self.feature_names is empty!"
    assert len(self.feature_names) == len(self.feature_sizes), \
    "length of feature_names (={}) != length of feature_sizes (={})".format( \
    len(self.feature_names), len(self.feature_sizes))

    feature_map = {"video_id": tf.FixedLenFeature([], tf.string),
                   "labels": tf.VarLenFeature(tf.int64)}
    for feature_index in range(num_features):
      feature_map[self.feature_names[feature_index]] = tf.FixedLenFeature(
          [self.feature_sizes[feature_index]], tf.float32)

    features = tf.parse_example(serialized_examples, features=feature_map)
    labels = tf.sparse_to_indicator(features["labels"], self.num_classes)
    labels.set_shape([None, self.num_classes])
    concatenated_features = tf.concat([
        features[feature_name] for feature_name in self.feature_names], 1)

    return features["video_id"], concatenated_features, labels, tf.ones([tf.shape(serialized_examples)[0]])
開發者ID:wangheda，項目名稱:youtube-8m，代碼行數:34，代碼來源:readers.py

示例8: prepare_serialized_examples

# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import sparse_to_indicator [as 別名]
def prepare_serialized_examples(self, serialized_examples):
    """Parse a single video-level TF Example."""
    # set the mapping from the fields to data types in the proto
    num_features = len(self.feature_names)
    assert num_features > 0, "self.feature_names is empty!"
    assert len(self.feature_names) == len(self.feature_sizes), \
    "length of feature_names (={}) != length of feature_sizes (={})".format(
        len(self.feature_names), len(self.feature_sizes))

    feature_map = {
        "id": tf.io.FixedLenFeature([], tf.string),
        "labels": tf.io.VarLenFeature(tf.int64)
    }
    for feature_index in range(num_features):
      feature_map[self.feature_names[feature_index]] = tf.FixedLenFeature(
          [self.feature_sizes[feature_index]], tf.float32)

    features = tf.parse_example(serialized_examples, features=feature_map)
    labels = tf.sparse_to_indicator(features["labels"], self.num_classes)
    labels.set_shape([None, self.num_classes])
    concatenated_features = tf.concat(
        [features[feature_name] for feature_name in self.feature_names], 1)

    output_dict = {
        "video_ids": features["id"],
        "video_matrix": concatenated_features,
        "labels": labels,
        "num_frames": tf.ones([tf.shape(serialized_examples)[0]])
    }

    return output_dict
開發者ID:google，項目名稱:youtube-8m，代碼行數:33，代碼來源:readers.py


注：本文中的tensorflow.sparse_to_indicator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。