def __init__(self, resnet_size_choices=None): super(ResnetArgParser, self).__init__(parents=[ parsers.BaseParser(), parsers.PerformanceParser(), parsers.ImageModelParser(), parsers.ExportParser(), parsers.BenchmarkParser(), ]) self.add_argument( '--version', '-v', type=int, choices=[1, 2], default=resnet_model.DEFAULT_VERSION, help='Version of ResNet. (1 or 2) See README.md for details.') self.add_argument( '--resnet_size', '-rs', type=int, default=50, choices=resnet_size_choices, help='[default: %(default)s] The size of the ResNet model to use.', metavar='<RS>' if resnet_size_choices is None else None)
def __init__(self): super(DANArgParser, self).__init__(parents=[ parsers.BaseParser(), parsers.PerformanceParser(), parsers.ImageModelParser(), ]) self.add_argument( "--data_dir_test", "-ddt", default=None, help="[default: %(default)s] The location of the test data.", metavar="<DD>", ) self.add_argument( '--dan_stage', '-ds', type=int, default=1, choices=[1, 2], help='[default: %(default)s] The stage of the DAN model.') self.add_argument('--mode', '-mode', type=str, default='train', choices=['train', 'eval', 'predict']) self.add_argument('--num_lmark', '-nlm', type=int, default=68)
def __init__(self): super(SimpNetArgParser, self).__init__(parents=[ parsers.BaseParser(), parsers.PerformanceParser(), parsers.ImageModelParser(), parsers.BenchmarkParser(), ])
def __init__(self): super(TestParser, self).__init__(parents=[ parsers.BaseParser(), parsers.PerformanceParser(num_parallel_calls=True, inter_op=True, intra_op=True, use_synthetic_data=True), parsers.ImageModelParser(data_format=True), parsers.BenchmarkParser(benchmark_log_dir=True) ])
def __init__(self): super(MNISTArgParser, self).__init__(parents=[ parsers.BaseParser(), parsers.ImageModelParser(), ]) self.set_defaults(data_dir='/tmp/mnist_data', model_dir='/tmp/mnist_model', batch_size=100, train_epochs=40)
def __init__(self): super(MNISTArgParser, self).__init__(parents=[ parsers.BaseParser(), parsers.ImageModelParser(), parsers.ExportParser(), ]) self.set_defaults( data_dir='/home/jcf/models-master/official/mnist/mnist_data', model_dir='/tmp/mnist_model', batch_size=100, train_epochs=40)
def __init__(self): super(WideDeepArgParser, self).__init__(parents=[parsers.BaseParser()]) self.add_argument( '--model_type', '-mt', type=str, default='wide_deep', choices=['wide', 'deep', 'wide_deep'], help='[default %(default)s] Valid model types: wide, deep, wide_deep.', metavar='<MT>') self.set_defaults( data_dir='/tmp/census_data', model_dir='/tmp/census_model', train_epochs=40, epochs_between_evals=2, batch_size=40)
def __init__(self, resnet_size_choices=None): super(ResnetArgParser, self).__init__(parents=[ parsers.BaseParser(), parsers.PerformanceParser(), parsers.ImageModelParser(), ]) self.add_argument( '--resnet_size', '-rs', type=int, default=50, choices=resnet_size_choices, help='[default: %(default)s]The size of the ResNet model to use.', metavar='<RS>' )
def __init__(self): super(WideDeepArgParser, self).__init__(parents=[ parsers.BaseParser(multi_gpu=False, num_gpu=False)]) self.add_argument( '--model_type', '-mt', type=str, default='wide', choices=['wide', 'deep', 'wide_deep'], help='[default %(default)s] Valid model types: wide, deep, wide_deep.', metavar='<MT>') self.set_defaults( data_dir='/home/vivek/Work/kaggle/DontGetKicked/data/train', model_dir='/home/vivek/Work/kaggle/DontGetKicked/model', train_epochs=400, epochs_between_evals=2, batch_size=80)
def __init__(self, resnet_size_choices=None): super(ResnetArgParser, self).__init__(parents=[ parsers.BaseParser(), parsers.PerformanceParser(), parsers.ImageModelParser(), parsers.ExportParser(), parsers.BenchmarkParser(), ]) self.add_argument('--dataset','-d',default="cifar10", help='Which dataset to use (currently cifar10/cifar100)' ) self.add_argument( '--version', '-v', type=int, choices=[1, 2], default=rncm.RESNET_DEFAULT_VERSION, help='Version of ResNet. (1 or 2) See README.md for details.' ) self.add_argument( '--resnet_size', '-rs', type=int, default=50, choices=resnet_size_choices, help='[default: %(default)s] The size of the ResNet model to use.', metavar='<RS>' if resnet_size_choices is None else None ) self.add_argument( '--continu',type=int,default=0, help='Continue with an existing model, or start from scratch' ) self.add_argument( '--scratch',type=int,default=0, help='Start from scratch even if model exist' ) self.add_argument( '--ncmmethod', default=rncm.NCM_DEFAULT_METHOD, help='[default: %(default)s] Which NCM method to use', ) self.add_argument( '--ncmparam', default=rncm.NCM_DEFAULT_PARAMETER, type=float, help='[default: %(default)s] additional NCM parameter to use', ) self.add_argument( '--initial_learning_scale', '-l', default=0.1, type=float, help='Intial Learning Scale (default: %(default)s)', )
def __init__(self): super(WideDeepArgParser, self).__init__(parents=[parsers.BaseParser()]) self.add_argument( '--mode', '-m', type=str, default='ndcg', choices=['train', 'retrain', 'ndcg'], help='[default: %(default)s] Model types: train, retrain, ndcg.', metavar='<M>') self.add_argument( '--params', '-p', type=str, default='configs/std_params.json', help= '[default: %(default)s] Hyper-parameter setting: a json object.', metavar='<P>') self.add_argument('--name', '-n', type=str, default='tmp_model', help='[default: %(default)s] Model name.', metavar='<N>') self.add_argument('--loss', '-l', type=str, default='focal', choices=['focal', 'xent', 'mse'], help='[default: %(default)s] Model loss.', metavar='<L>') self.add_argument('--device', '-d', type=str, default='gpu', choices=['cpu', 'gpu'], help='[default: %(default)s] Select device.', metavar='<d>') self.set_defaults(data_dir='./data/', model_dir='./model_saved/', train_epochs=100, epochs_between_evals=1, batch_size=64)
def __init__(self): super(MNISTEagerArgParser, self).__init__(parents=[ parsers.BaseParser( epochs_between_evals=False, multi_gpu=False, hooks=False), parsers.ImageModelParser() ]) self.add_argument( '--log_interval', '-li', type=int, default=10, metavar='N', help= '[default: %(default)s] batches between logging training status') self.add_argument( '--output_dir', '-od', type=str, default=None, metavar='<OD>', help= '[default: %(default)s] Directory to write TensorBoard summaries') self.add_argument('--lr', '-lr', type=float, default=0.01, metavar='<LR>', help='[default: %(default)s] learning rate') self.add_argument('--momentum', '-m', type=float, default=0.5, metavar='<M>', help='[default: %(default)s] SGD momentum') self.add_argument('--no_gpu', '-nogpu', action='store_true', default=False, help='disables GPU usage even if a GPU is available') self.set_defaults( data_dir='/tmp/tensorflow/mnist/input_data', model_dir='/tmp/tensorflow/mnist/checkpoints/', batch_size=100, train_epochs=10, )
def __init__(self): super(MNISTArgParser, self).__init__( parents=[parsers.BaseParser(), parsers.ImageModelParser()]) self.add_argument( '--export_dir', type=str, help= '[default: %(default)s] If set, a SavedModel serialization of the ' 'model will be exported to this directory at the end of training. ' 'See the README for more details and relevant links.') self.set_defaults(data_dir='/tmp/mnist_data', model_dir='/tmp/mnist_model', batch_size=100, train_epochs=40)
def __init__(self): super(WideDeepArgParser, self).__init__(parents=[parsers.BaseParser()]) self.add_argument( '--model_type', '-mt', type=str, default='wide_deep', choices=['wide', 'deep', 'wide_deep'], help= '[default %(default)s] Valid model types: wide, deep, wide_deep.', metavar='<MT>') self.set_defaults( data_dir= 'Users/Smith/AppData/Local/Programs/Python/Python36/Python testing/', model_dir= 'Users/Smith/AppData/Local/Programs/Python/Python36/Python testing/', train_epochs=40, epochs_between_evals=2, batch_size=40)
def __init__(self, resnet_size_choices=None): super(ResnetArgParser, self).__init__(parents=[ parsers.BaseParser(), parsers.PerformanceParser(), parsers.ImageModelParser(), parsers.ExportParser(), parsers.BenchmarkParser(), ]) self.add_argument( '--version', '-v', type=int, choices=[1, 2], default=resnet_model.DEFAULT_VERSION, help='Version of ResNet. (1 or 2) See README.md for details.') self.add_argument( '--resnet_size', '-rs', type=int, default=50, choices=resnet_size_choices, help='[default: %(default)s] The size of the ResNet model to use.', metavar='<RS>' if resnet_size_choices is None else None) self.add_argument( '--enable_ml_comm', '-mc', type=int, choices=[0, 1], default=1, help= '[default: %(default)s] Whether to use Cray ML-Comm Distributed Training Plugin' ) self.add_argument( '--global_perf_log_freq', '-pf', type=int, default=50, help= '[default: %(default)s] Number of steps after which to report global (all process averages) training loss and performance' ) self.add_argument( '--warmup_epochs', '-we', type=int, default=0, help= '[default: %(default)s] Number of warmup epochs when using LARS') self.add_argument( '--base_lr', '-blr', type=float, default=1.0, help= '[default: %(default)s] Learning rate to start after warmup epochs finish when using LARS' ) self.add_argument( '--init_lr', '-ilr', type=float, default=0.1, help= '[default: %(default)s] Learning rate to start warmup with when using LARS' ) self.add_argument( '--weight_decay', '-wd', type=float, default=1e-4, help='[default: %(default)s] Weight decay to use during training')