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Python tensorflow.sparse_to_indicator方法代码示例

本文整理汇总了Python中tensorflow.sparse_to_indicator方法的典型用法代码示例。如果您正苦于以下问题:Python tensorflow.sparse_to_indicator方法的具体用法?Python tensorflow.sparse_to_indicator怎么用?Python tensorflow.sparse_to_indicator使用的例子?那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在tensorflow的用法示例。


在下文中一共展示了tensorflow.sparse_to_indicator方法的8个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。

示例1: prepare_serialized_examples

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import sparse_to_indicator [as 别名]
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]]) 
开发者ID:antoine77340,项目名称:Youtube-8M-WILLOW,代码行数:24,代码来源:readers.py

示例2: prepare_reader

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import sparse_to_indicator [as 别名]
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]]) 
开发者ID:wangheda,项目名称:youtube-8m,代码行数:27,代码来源:readers.py

示例3: prepare_serialized_examples

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import sparse_to_indicator [as 别名]
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]]) 
开发者ID:miha-skalic,项目名称:youtube8mchallenge,代码行数:23,代码来源:readers.py

示例4: prepare_serialized_examples

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import sparse_to_indicator [as 别名]
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]]) 
开发者ID:pomonam,项目名称:AttentionCluster,代码行数:23,代码来源:readers.py

示例5: prepare_serialized_examples

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import sparse_to_indicator [as 别名]
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]]) 
开发者ID:mpekalski,项目名称:Y8M,代码行数:23,代码来源:readers.py

示例6: prepare_writer

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import sparse_to_indicator [as 别名]
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]]) 
开发者ID:wangheda,项目名称:youtube-8m,代码行数:34,代码来源:writers.py

示例7: prepare_reader

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import sparse_to_indicator [as 别名]
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]]) 
开发者ID:wangheda,项目名称:youtube-8m,代码行数:34,代码来源:readers.py

示例8: prepare_serialized_examples

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import sparse_to_indicator [as 别名]
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 
开发者ID:google,项目名称:youtube-8m,代码行数:33,代码来源:readers.py


注:本文中的tensorflow.sparse_to_indicator方法示例由纯净天空整理自Github/MSDocs等开源代码及文档管理平台,相关代码片段筛选自各路编程大神贡献的开源项目,源码版权归原作者所有,传播和使用请参考对应项目的License;未经允许,请勿转载。