本文整理汇总了Python中tensorflow.keras.utils.get_file方法的典型用法代码示例。如果您正苦于以下问题：Python utils.get_file方法的具体用法？Python utils.get_file怎么用？Python utils.get_file使用的例子？那么, 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tensorflow.keras.utils的用法示例。
在下文中一共展示了utils.get_file方法的4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: __init__
# 需要导入模块: from tensorflow.keras import utils [as 别名]
# 或者: from tensorflow.keras.utils import get_file [as 别名]
def __init__(self):
proto_url = 'https://download.cvlib.net/config/gender_detection/gender_deploy.prototxt'
model_url = 'https://github.com/arunponnusamy/cvlib-files/releases/download/v0.1/gender_net.caffemodel'
save_dir = os.path.expanduser('~') + os.path.sep + '.cvlib' + os.path.sep + 'pre-trained'
if not os.path.exists(save_dir):
os.makedirs(save_dir)
self.proto = get_file('gender_deploy.prototxt', proto_url,
cache_subdir=save_dir)
self.model = get_file('gender_net.caffemodel', model_url,
cache_subdir=save_dir)
self.labels = ['male', 'female']
self.mean = (78.4263377603, 87.7689143744, 114.895847746)
print('[INFO] Initializing gender detection model ..')
self.net = cv2.dnn.readNetFromCaffe(self.proto, self.model) 示例2: _download_data
# 需要导入模块: from tensorflow.keras import utils [as 别名]
# 或者: from tensorflow.keras.utils import get_file [as 别名]
def _download_data():
_ = get_file(
'qm9.tar.gz', DATASET_URL,
extract=True, cache_dir=DATA_PATH, cache_subdir=DATA_PATH
)
os.rename(DATA_PATH + 'gdb9.sdf', DATA_PATH + 'qm9.sdf')
os.rename(DATA_PATH + 'gdb9.sdf.csv', DATA_PATH + 'qm9.sdf.csv')
os.remove(DATA_PATH + 'qm9.tar.gz') 示例3: _get_data
# 需要导入模块: from tensorflow.keras import utils [as 别名]
# 或者: from tensorflow.keras.utils import get_file [as 别名]
def _get_data(cls, data: Union[str, np.ndarray, Iterable, Dict[str, Union[Iterable, np.ndarray]]], model: Model) \
-> Union[np.ndarray, Iterable, Dict[str, Union[Iterable, np.ndarray]]]:
if not isinstance(data, str):
return data
if data.startswith('http://') or data.startswith('https://'):
filename = '{timestamp}_{filename}'.format(
timestamp=datetime.now().timestamp(), filename=data.split('/')[-1])
data_file = utils.get_file(filename, data)
else:
data_file = os.path.abspath(os.path.expanduser(data))
extensions = [ext for ext in cls._supported_data_file_extensions if data_file.endswith('.' + ext)]
if os.path.isfile(data_file):
assert extensions, 'Unsupported type for file {}. Supported extensions: {}'.format(
data_file, cls._supported_data_file_extensions
)
extension = extensions.pop()
if extension in cls._csv_extensions:
return cls._get_csv_data(data_file)
if extension == 'npy':
return cls._get_numpy_data(data_file)
if extension == 'npz':
return cls._get_numpy_compressed_data(data_file)
if extension in cls._image_extensions:
return cls._get_image(data_file, model)
raise AssertionError('Unsupported file type: {}'.format(data_file))
elif os.path.isdir(data_file):
return cls._get_dir(data_file, model) 示例4: read_data
# 需要导入模块: from tensorflow.keras import utils [as 别名]
# 或者: from tensorflow.keras.utils import get_file [as 别名]
def read_data():
# Get the file
try:
path = get_file(
"babi-tasks-v1-2.tar.gz",
origin="https://s3.amazonaws.com/text-datasets/"
"babi_tasks_1-20_v1-2.tar.gz")
except Exception:
print(
"Error downloading dataset, please download it manually:\n"
"$ wget http://www.thespermwhale.com/jaseweston/babi/tasks_1-20_v1-2" # noqa: E501
".tar.gz\n"
"$ mv tasks_1-20_v1-2.tar.gz ~/.keras/datasets/babi-tasks-v1-2.tar.gz" # noqa: E501
)
raise
# Choose challenge
challenges = {
# QA1 with 10,000 samples
"single_supporting_fact_10k": "tasks_1-20_v1-2/en-10k/qa1_"
"single-supporting-fact_{}.txt",
# QA2 with 10,000 samples
"two_supporting_facts_10k": "tasks_1-20_v1-2/en-10k/qa2_"
"two-supporting-facts_{}.txt",
}
challenge_type = "single_supporting_fact_10k"
challenge = challenges[challenge_type]
with tarfile.open(path) as tar:
train_stories = get_stories(tar.extractfile(challenge.format("train")))
test_stories = get_stories(tar.extractfile(challenge.format("test")))
return train_stories, test_stories