add_argument('--some-number', type=float) In addition to simple builtins like int and float, you can supply your own function to the type parameter to vet the incoming values. def must_be_exactly_ten(value): nu
In addition to simple builtins likeintandfloat, you can supply your own function to thetypeparameter to vet the incoming values. defmust_be_exactly_ten(value):number=int(value)ifnumber==10:returnnumberelse:raiseTypeError("Hey! you need to provide exactly the number 10!")defmain():parser=...
2 """ Binary representation of float value as IEEE-11073:20601 32-bit FLOAT """ 3 return int(value * (10 ** precision)).to_bytes(3, 'little', True) + struct.pack('<b', -precision) We precede the data with a byte containing flags, set to zero (meaning that the temperature is...
parts=lines.map(lambdarow:row.value.split("::"))ratingsRDD=parts.map(lambdap:Row(userId=int(p[0]),movieId=int(p[1]),rating=float(p[2]),timestamp=int(p[3])))ratings=spark.createDataFrame(ratingsRDD)(training,test)=ratings.randomSplit([0.8,0.2])# Build the recommendation mod...
EF[k]=get_bond_features(mol.GetBondBetweenAtoms(int(i),int(j))) EF = torch.tensor(EF, dtype = torch.float) # construct label tensor y_tensor = torch.tensor(np.array([y_val]), dtype = torch.float) # construct Pytorch Geometric data object and append to data list ...
# base_learning_rate: float # target: path to lightning module # params: # key: value # data: # target: main.DataModuleFromConfig # params: # batch_size: int # wrap: bool # train: # target: path to train dataset # params: # key: value # validation: # targe...
Python的基本数据类型均实现了对应输入控件。同时,对基本类型进行了扩展,提供了丰富的语义类型,方便用户输入日期、时间、颜色、文件路径等特殊对象。实现了通用对象输入控件,支持输入Json对象和任意Python字面量对象(包括int、float、bool、str、bytes、list、tuple、set、dict)。
float16-make ‑ roottest-root-io-float16-make roottest-root-io-hadd-compression_settings ‑ roottest-root-io-hadd-compression_settings roottest-root-io-hadd-input_validation ‑ roottest-root-io-hadd-input_validation roottest-root-io-hadd-test_MergeCMSOpenDataRNTuples ‑ roottest-...
python models/transformers_pytorch/bert.py --batch_size 8 would simply run BERT with a batch size of 8 in PyTorch on your CPU. The standardized set of arguments is: General args "batch_size": Arg("batch_size", default=1, type=int), ...
parser.add_argument('--perturbation_mode', type=int, default=1) parser.add_argument('--ngram', type=int, default=3) parser.add_argument('--gamma', type=float, default=0.5) parser.add_argument('--lang', type=str, default='en') parser.add_argument('--ctx', type=int, default=200...