我们从Python开源项目中，提取了以下18个代码示例，用于说明如何使用tensorflow.sparse_to_indicator()。
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]])
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]) ### Newly raw_labels = features["labels"] raw_coarse = tf.SparseTensor(indices = raw_labels.indices, values = tf.reshape(tf.gather(tf.constant(self.label_belongs, dtype = tf.int64), raw_labels.values), [-1]), dense_shape = raw_labels.dense_shape) coarse_labels = tf.sparse_to_indicator(raw_coarse, self.num_coarse_classes, name = 'coarse_transfer') coarse_labels.set_shape([None, self.num_coarse_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]]) ### Newly return features["video_id"], concatenated_features, labels, coarse_labels, tf.ones([tf.shape(serialized_examples)[0]]) ###
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]])
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), "predictions": tf.FixedLenFeature([self.num_classes], tf.float32), "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]]), features["predictions"]
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]])
def decode(filename_queue): # Create TFRecords reader reader = tf.TFRecordReader() _, serialized_example = reader.read(filename_queue) # Feature keys in TFRecords example features = tf.parse_single_example(serialized_example, features={ 'id': tf.FixedLenFeature([], tf.string), 'vector': tf.FixedLenFeature([], tf.string), 'label': tf.VarLenFeature(tf.int64) }) video_id = features['id'] # Decode vector and pad to fixed size vector = tf.decode_raw(features['vector'], tf.float32) vector = tf.reshape(vector, [-1, 300]) vector = tf.pad(vector, [[0, 40 - tf.shape(vector)[0]], [0, 0]]) vector.set_shape([40, 300]) # Get label index label = tf.sparse_to_indicator(features['label'], 4716) label.set_shape([4716]) label = tf.cast(label, tf.float32) return video_id, vector, label # Creates input pipeline for tensorflow networks
