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def build_parser():
# Build this benchmark"s cli parser on top of the keras_cli parser.
parser = build_base_parser()
# Benchmark specific hyperparameters.
parser.add_argument("--embedding_dims", action="store", dest="embedding_dims",
nargs="?", const=2, type = int, default = 50,
help="how many embedding dims will be added to the model")
parser.add_argument("--filters", action="store", dest="filters",
nargs="?", const=2, type = int, default = 250,
help="the number of output filters in the convolution")
parser.add_argument("--hidden_dims", action="store", dest="hidden_dims",
nargs="?", const=2, type = int, default = 250,
help="hidden dims of a vanilla hidden layer")
parser.add_argument("--kernel_size", action="store", dest="kernel_size",
nargs="?", const=2, type = int, default = 3,
def build_parser():
# Build this benchmark's cli parser on top of the base parser.
parser = build_base_parser()
# Benchmark specific hyperparameters.
parser.add_argument("--base_lr", action="store", dest="base_lr",
nargs="?", const=2, type=int, default=1e-3,)
parser.add_argument("--lr80", action="store", dest="lr80",
nargs="?", const=2, type=int, default=1e-1)
parser.add_argument("--lr120", action="store", dest="lr120",
nargs="?", const=2, type=int, default=1e-2)
parser.add_argument("--lr160", action="store", dest="lr160",
nargs="?", const=2, type=int, default=1e-3)
parser.add_argument("--lr180", action="store", dest="lr180",
nargs="?", const=2, type=int, default=0.5e-3)
def build_parser():
# Build this benchmark"s cli parser on top of the base parser.
parser = build_base_parser()
# Benchmark specific hyperparameters.
parser.add_argument("--f1_size", action="store", dest="f1_size",
nargs="?", const=2, type=int, default=3,
help="Filter 1 dim")
parser.add_argument("--f2_size", action="store", dest="f2_size",
nargs="?", const=2, type=int, default=3,
help="Filter 2 dim")
parser.add_argument("--f1_units", action="store", dest="f1_units",
nargs="?", const=2, type=int, default=32,
help="Filter 1 units")
parser.add_argument("--f2_units", action="store", dest="f2_units",
nargs="?", const=2, type=int, default=64,
def build_parser():
parser = build_base_parser()
return parser
def build_parser():
# Build this benchmark"s cli parser on top of the keras_cli parser.
parser = build_base_parser()
# Benchmark specific hyperparameters.
parser.add_argument("--nunits", action="store", dest="nunits",
nargs="?", const=1, type=int, default=512,
help="Dense units")
parser.add_argument("--max_words", action="store", dest="max_words",
nargs="?", const=2, type=int, default=1000)
parser.add_argument("--skip_top", action="store", dest="skip_top",
nargs="?", const=2, type=int, default=0)
return parser
def build_parser():
parser = build_base_parser()
parser.add_argument('--max_features', action='store', dest='max_features',
nargs='?', const=2, type = int, default='20000',
help='max_features when loading data')
parser.add_argument('--maxlen', action='store', dest='maxlen',
nargs='?', const=2, type = int, default='400',
help='the max length of the sequence of x_train and x_test')
parser.add_argument('--embedding_dims', action='store', dest='embedding_dims',
nargs='?', const=2, type = int, default = '50',
help='how many embedding dims will be added to the model')
return parser
def build_parser():
# Build this benchmark"s cli parser on top of the keras_cli parser.
parser = build_base_parser()
# Benchmark specific hyperparameters.
parser.add_argument("--units", action="store", dest="units",
nargs="?", const=1, type=int, default=64,
help="units for LSTM")
parser.add_argument("--max_features", action="store", dest="max_features",
nargs="?", const=2, type = int, default=20000,
help="max_features when loading data")
parser.add_argument("--maxlen", action="store", dest="maxlen",
nargs="?", const=2, type = int, default=100,
help="the max length of the sequence of x_train and x_test")
parser.add_argument("--embedding_dims", action="store", dest="embedding_dims",
nargs="?", const=2, type = int, default = 128,
def build_parser():
# Build this benchmark"s cli parser on top of the keras_cli parser.
parser = build_base_parser()
# Benchmark specific hyperparameters.
parser.add_argument('--nunits', action='store', dest='nunits',
nargs='?', const=2, type=int, default='512',
help='number of units/layer in MLP')
parser.add_argument('--nhidden', action='store', dest='nhidden',
nargs='?', const=2, type=int, default='2',
help='number of hidden layers in MLP')
return parser