def main():
rnd_seed = None
if rnd_seed:
torch.manual_seed(rnd_seed)
np.random.seed(rnd_seed)
# ---------------------------------------
# DATA LOADING
# ---------------------------------------
#result_path = "../result_lrn_0p001_rl/"
dict_file = "../dataset/CCGbank/dict_word"
entity_file = "../dataset/CCGbank/dict_tag"
index2word = get_index2word(dict_file)
index2label = get_index2label(entity_file)
vocab_size = len(index2word)
label_size = len(index2label)
#train_X, train_Y = minibatch_of_one_de('train')
val_X, val_Y = minibatch_of_one_de('val')
test_X, test_Y = minibatch_of_one_de('test')
# ---------------------------------------
# HYPER PARAMETERS
# ---------------------------------------
# Using word2vec pre-trained embedding
word_embedding_dim = 300
hidden_dim = 512
label_embedding_dim = 512
max_epoch = 30
# 0.001 is a good value
ner_learning_rate = 0.001
pretrained = None
# ---------------------------------------
# GPU OR NOT?
# ---------------------------------------
gpu = True
if gpu and rnd_seed:
torch.cuda.manual_seed(rnd_seed)
# ---------------------------------------
# MODEL INSTANTIATION
# ---------------------------------------
#attention = None
attention = "fixed"
load_model_dir = "../result_ccg_lrn_0p001_atten/"
load_model_filename = os.path.join(load_model_dir, "ckpt_11.pth")
batch_size = 1
machine = ner(word_embedding_dim, hidden_dim, label_embedding_dim, vocab_size,
label_size, learning_rate=ner_learning_rate,
minibatch_size=batch_size, max_epoch=max_epoch, train_X=None,
train_Y=None, val_X=val_X, val_Y=val_Y, test_X=test_X,
test_Y=test_Y, attention=attention, gpu=gpu,
pretrained=pretrained, load_model_filename=load_model_filename)
if gpu:
machine = machine.cuda()
initial_beam_size = 3
# When you have only one beam, it does not make sense to consider
# max_beam_size larger than the size of your label vocabulary
max_beam_size = 10
# ============ INIT RL =====================
os.environ['OMP_NUM_THREADS'] = '4'
#os.environ['CUDA_VISIBLE_DEVICES'] = ""
parser = argparse.ArgumentParser(description='A3C')
parser.add_argument('--logdir', default='../result_ccg_atten_ckpt_11_rl_lrn_0p001_reward_0p02_beam_3_gpu',
help='name of logging directory')
parser.add_argument('--lr', type=float, default=0.001,
help='learning rate (default: 0.0001)')
parser.add_argument('--gamma', type=float, default=0.99,
help='discount factor for rewards (default: 0.99)')
parser.add_argument('--n_epochs', type=int, default=50,
help='number of epochs for training agent(default: 30)')
parser.add_argument('--entropy-coef', type=float, default=0.01,
help='entropy term coefficient (default: 0.01)')
parser.add_argument('--num-processes', type=int, default=1,
help='how many training processes to use (default: 4)')
parser.add_argument('--num-steps', type=int, default=20,
help='number of forward steps in A3C (default: 20)')
parser.add_argument('--tau', type=float, default=1.00,
help='parameter for GAE (default: 1.00)')
parser.add_argument('--value-loss-coef', type=float, default=0.5,
help='value loss coefficient (default: 0.5)')
parser.add_argument('--max-grad-norm', type=float, default=5,
help='value loss coefficient (default: 5)')
parser.add_argument('--seed', type=int, default=1,
help='random seed (default: 1)')
parser.add_argument('--max-episode-length', type=int, default=1000000,
help='maximum length of an episode (default: 1000000)')
parser.add_argument('--name', default='test',
help='name of the process')
parser.add_argument('--no-shared', default=False,
help='use an optimizer without shared momentum.')
args = parser.parse_args()
if not os.path.exists(args.logdir):
os.mkdir(args.logdir)
shared_model = AdaptiveActorCritic(max_beam_size=max_beam_size,
action_space=3)
#shared_model.share_memory()
shared_model.eval()
if args.no_shared:
shared_optimizer = None
# default here (False)
else:
shared_optimizer = SharedAdam(params=shared_model.parameters(),
lr=args.lr)
# optimizer = optim.Adam(shared_model.parameters(), lr=learning_rate)
shared_optimizer.share_memory()
# --------------------------------------------
# RL TRAINING
# --------------------------------------------
# For German dataset, f_score_index_begin = 5 (because O_INDEX = 4)
# For toy dataset, f_score_index_begin = 4 (because {0: '<s>', 1: '<e>', 2: '<p>', 3: '<u>', ...})
# For CCG dataset, f_score_index_begin = 2 (because {0: _PAD, 1: _SOS, ...})
f_score_index_begin = 2
# RL reward coefficient
reward_coef_fscore = 1
reward_coef_beam_size = 0.02
load_map_location = "cpu"
logfile = open(os.path.join(args.logdir, "eval_test.txt"), "w+")
for epoch in range(0, args.n_epochs):
print("Eval for epoch {}".format(epoch))
load_model_filename = os.path.join(args.logdir, "ckpt_" + str(epoch) + ".pth")
checkpoint = torch.load(load_model_filename, map_location=load_map_location)
shared_model.load_state_dict(checkpoint["state_dict"])
print("\tEval now...")
fscore, total_beam_number_in_dataset, avg_beam_size, time_used = \
eval_adaptive(
machine,
max_beam_size,
shared_model,
test_X, test_Y, index2word, index2label,
"test", args.name, "adaptive", initial_beam_size,
reward_coef_fscore, reward_coef_beam_size,
f_score_index_begin,
args)
log_msg = "%d\t%f\t%d\t%f\t%f" % (epoch, fscore, total_beam_number_in_dataset, avg_beam_size, time_used)
print(log_msg)
logfile.write(log_msg + '\n')
logfile.flush()
logfile.close()
def main():
rnd_seed = None
if rnd_seed:
torch.manual_seed(rnd_seed)
np.random.seed(rnd_seed)
# ---------------------------------------
# DATA LOADING
# ---------------------------------------
#result_path = "../result_lrn_0p001_rl/"
dict_file = "../dataset/German/vocab1.de"
entity_file = "../dataset/German/vocab1.en"
index2word = get_index2word(dict_file)
index2label = get_index2label(entity_file)
vocab_size = len(index2word)
label_size = len(index2label)
train_X, train_Y = minibatch_of_one_de('train')
val_X, val_Y = minibatch_of_one_de('valid')
test_X, test_Y = minibatch_of_one_de('test')
# ---------------------------------------
# HYPER PARAMETERS
# ---------------------------------------
# Using word2vec pre-trained embedding
#word_embedding_dim = 300
hidden_dim = 64
label_embedding_dim = 8
max_epoch = 100
# 0.001 is a good value
ner_learning_rate = 0.001
pretrained = 'de64'
word_embedding_dim = 64
# ---------------------------------------
# GPU OR NOT?
# ---------------------------------------
gpu = False
if gpu and rnd_seed:
torch.cuda.manual_seed(rnd_seed)
# ---------------------------------------
# MODEL INSTANTIATION
# ---------------------------------------
#attention = None
attention = "fixed"
load_model_dir = "../result_lrn_0p001_atten/"
load_model_filename = os.path.join(load_model_dir, "ckpt_46.pth")
batch_size = 1
machine = ner(word_embedding_dim, hidden_dim, label_embedding_dim, vocab_size,
label_size, learning_rate=ner_learning_rate,
minibatch_size=batch_size, max_epoch=max_epoch, train_X=None,
train_Y=None, val_X=val_X, val_Y=val_Y, test_X=test_X,
test_Y=test_Y, attention=attention, gpu=gpu,
pretrained=pretrained, load_model_filename=load_model_filename,
load_map_location="cpu")
if gpu:
machine = machine.cuda()
initial_beam_size = 3
# When you have only one beam, it does not make sense to consider
# max_beam_size larger than the size of your label vocabulary
max_beam_size = label_size
# ============ INIT RL =====================
parser = argparse.ArgumentParser(description='A3C')
parser.add_argument('--logdir', default='../result_lrn_0p001_atten_rl',
help='name of logging directory')
parser.add_argument('--lr', type=float, default=0.0001,
help='learning rate (default: 0.0001)')
parser.add_argument('--gamma', type=float, default=0.99,
help='discount factor for rewards (default: 0.99)')
parser.add_argument('--n_epochs', type=int, default=2,
help='number of epochs for training agent(default: 30)')
parser.add_argument('--entropy-coef', type=float, default=0.01,
help='entropy term coefficient (default: 0.01)')
parser.add_argument('--num-processes', type=int, default=2,
help='how many training processes to use (default: 4)')
parser.add_argument('--num-steps', type=int, default=20,
help='number of forward steps in A3C (default: 20)')
parser.add_argument('--tau', type=float, default=1.00,
help='parameter for GAE (default: 1.00)')
parser.add_argument('--value-loss-coef', type=float, default=0.5,
help='value loss coefficient (default: 0.5)')
parser.add_argument('--max-grad-norm', type=float, default=5,
help='value loss coefficient (default: 5)')
parser.add_argument('--seed', type=int, default=1,
help='random seed (default: 1)')
parser.add_argument('--max-episode-length', type=int, default=1000000,
help='maximum length of an episode (default: 1000000)')
parser.add_argument('--name', default='train',
help='name of the process')
parser.add_argument('--no-shared', default=False,
help='use an optimizer without shared momentum.')
args = parser.parse_args()
if not os.path.exists(args.logdir):
os.mkdir(args.logdir)
# For German dataset, f_score_index_begin = 5 (because O_INDEX = 4)
# For toy dataset, f_score_index_begin = 4 (because {0: '<s>', 1: '<e>', 2: '<p>', 3: '<u>', ...})
f_score_index_begin = 5
# RL reward coefficient
reward_coef_fscore = 1
reward_coef_beam_size = 0.1
logfile = open(os.path.join(args.logdir, "eval_test.txt"), "w+")
model = AdaptiveActorCritic(max_beam_size=max_beam_size, action_space=3)
# Marking as for evaluation
model.eval()
load_map_location = "cpu"
for epoch in range(0, args.n_epochs):
load_model_filename = os.path.join(args.logdir, "ckpt_" + str(epoch) + ".pth")
checkpoint = torch.load(load_model_filename, map_location=load_map_location)
model.load_state_dict(checkpoint["state_dict"])
fscore, total_beam_number_in_dataset, avg_beam_size, time_used = \
eval_adaptive(machine,
max_beam_size,
model,
test_X, test_Y, index2word, index2label,
"test", False, "adaptive", initial_beam_size,
reward_coef_fscore, reward_coef_beam_size,
f_score_index_begin,
args)
log_msg = "%d\t%f\t%d\t%f\t%f" % (epoch, fscore, total_beam_number_in_dataset, avg_beam_size, time_used)
print(log_msg)
print(log_msg, file=logfile, flush=True)
# End for epoch
logfile.close()