本文整理汇总了Python中loader.Loader方法的典型用法代码示例。如果您正苦于以下问题：Python loader.Loader方法的具体用法？Python loader.Loader怎么用？Python loader.Loader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类loader的用法示例。
在下文中一共展示了loader.Loader方法的3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: label
# 需要导入模块: import loader [as 别名]
# 或者: from loader import Loader [as 别名]
def label(args):
"""Label the provided report(s)."""
loader = Loader(args.reports_path, args.extract_impression)
extractor = Extractor(args.mention_phrases_dir,
args.unmention_phrases_dir,
verbose=args.verbose)
classifier = Classifier(args.pre_negation_uncertainty_path,
args.negation_path,
args.post_negation_uncertainty_path,
verbose=args.verbose)
aggregator = Aggregator(CATEGORIES,
verbose=args.verbose)
# Load reports in place.
loader.load()
# Extract observation mentions in place.
extractor.extract(loader.collection)
# Classify mentions in place.
classifier.classify(loader.collection)
# Aggregate mentions to obtain one set of labels for each report.
labels = aggregator.aggregate(loader.collection)
write(loader.reports, labels, args.output_path, args.verbose) 示例2: __init__
# 需要导入模块: import loader [as 别名]
# 或者: from loader import Loader [as 别名]
def __init__(self, sess, image_size=48, num_hid=512,
model_type="dis+cls", batch_size=25, dataset="shape"):
"""Initialize the parameters for an Deep Visual Analogy network.
Args:
image_size: int, The size of width and height of input image
model_type: string, The type of increment function ["add", "deep"]
batch_size: int, The size of a batch [25]
dataset: str, The name of dataset ["shape", ""]
"""
self.sess = sess
self.image_size = image_size
self.model_type = model_type
self.batch_size = batch_size
self.dataset = dataset
self.num_hid = num_hid
#self.loader = Loader(self.dataset, self.batch_size)
self.cards = [2, 4, 3, 6, 2, 2, 2]
num_categorical = 0
for card in self.cards:
num_categorical = num_categorical + card
self.cards[6] = 3;
num_categorical = num_categorical + 1
self.num_categorical = num_categorical
self.id_idxes = range(0, self.num_categorical - 1)
self.pose_idxes = range(self.num_categorical, self.num_hid)
# parameters used to save a checkpoint
self._attrs = ['max_iter', 'batch_size', 'alpha', 'learning_rate']
self.build_model() 示例3: __init__
# 需要导入模块: import loader [as 别名]
# 或者: from loader import Loader [as 别名]
def __init__(self, sess, image_size=48, model_type="deep",
batch_size=25, dataset="shape"):
"""Initialize the parameters for an Deep Visual Analogy network.
Args:
image_size: int, The size of width and height of input image
model_type: string, The type of increment function ["add", "deep"]
batch_size: int, The size of a batch [25]
dataset: str, The name of dataset ["shape", ""]
"""
self.sess = sess
self.image_size = image_size
self.model_type = model_type
self.batch_size = batch_size
self.dataset = dataset
self.loader = Loader(self.dataset, self.batch_size)
self.sample_dir = "samples"
if not os.path.exists(self.sample_dir):
os.makedirs(self.sample_dir)
# parameters used to save a checkpoint
self._attrs = ['batch_size', 'model_type', 'image_size']
self.options = ['rotate', 'scale', 'xpos', 'ypos']
self.build_model()