parser.add_argument('--data_path', help='enter path for training data',
type=str)
parser.add_argument('--gpu_id', default="0", help='enter gpu id',
type=str,action=check_size(0,10))
parser.add_argument('--max_para_req', default=100, help='enter the max length of paragraph',
type=int, action=check_size(30,300))
parser.add_argument('--batch_size_squad',default=16, help='enter the batch size',
type=int, action=check_size(1,256))
parser.set_defaults()
args = parser.parse_args()
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu_id
hidden_size = 150
gradient_clip_value = 15
embed_size = 300
params_dict = {}
params_dict['batch_size'] = args.batch_size_squad
params_dict['embed_size'] = 300
params_dict['pad_idx'] = 0
params_dict['hs'] = hidden_size
params_dict['glove_dim'] = 300
params_dict['iter_interval'] = 8000
params_dict['num_iterations'] = 500000
params_dict['ax'] = ax
}
return reduced_results
if __name__ == "__main__":
parser = NgraphArgparser(
description='Train deep residual network on cifar10 dataset')
parser.add_argument(
'--stage_depth',
type=int,
default=2,
help='depth of each stage (network depth will be 9n+2)')
parser.add_argument('--use_aeon',
action='store_true',
help='whether to use aeon dataloader')
args = parser.parse_args()
np.random.seed(args.rng_seed)
# Create the dataloader
if args.use_aeon:
from data import make_aeon_loaders
train_set, valid_set = make_aeon_loaders(args.data_dir,
args.batch_size,
args.num_iterations)
else:
from ngraph.frontends.neon import ArrayIterator # noqa
from ngraph.frontends.neon import CIFAR10 # noqa
train_data, valid_data = CIFAR10(args.data_dir).load_data()
train_set = ArrayIterator(train_data,
args.batch_size,
environment = DimShuffleWrapper(environment)
# todo: perhaps these should be defined in the environment itself
state_axes = ng.make_axes([
ng.make_axis(environment.observation_space.shape[0], name='C'),
ng.make_axis(environment.observation_space.shape[1], name='H'),
ng.make_axis(environment.observation_space.shape[2], name='W'),
])
agent = dqn.Agent(
state_axes,
environment.action_space,
model=model,
epsilon=dqn.linear_generator(start=1.0, end=0.1, steps=1000000),
gamma=0.99,
learning_rate=0.00025,
memory=dqn.Memory(maxlen=1000000),
target_network_update_frequency=1000,
learning_starts=10000,
)
rl_loop.rl_loop_train(environment, agent, episodes=200000)
if __name__ == "__main__":
from ngraph.frontends.neon import NgraphArgparser
parser = NgraphArgparser()
parser.parse_args()
main()