def initialize_model(gpu, vocab_size, v_vec, emb_requires_grad, args):
emb_dim = args.emb_dim
h_dim = None
class_num = 2
is_gpu = True
if gpu == -1:
is_gpu = False
if args.emb_type == 'ELMo' or args.emb_type == 'ELMoForManyLangs':
bilstm = BiLSTM(emb_dim,
h_dim,
class_num,
vocab_size,
is_gpu,
v_vec,
emb_type=args.emb_type,
elmo_model_dir=args.emb_path)
elif args.emb_type == 'None':
bilstm = BiLSTM(emb_dim,
h_dim,
class_num,
vocab_size,
is_gpu,
v_vec,
emb_type=args.emb_type)
else:
bilstm = BiLSTM(emb_dim,
h_dim,
class_num,
vocab_size,
is_gpu,
v_vec,
emb_type=args.emb_type)
if is_gpu:
bilstm = bilstm.cuda()
for m in bilstm.modules():
print(m.__class__.__name__)
weights_init(m)
if args.emb_type != 'ELMo' and args.emb_type != 'ELMoForManyLangs' and args.emb_type != 'None':
for param in bilstm.word_embed.parameters():
param.requires_grad = emb_requires_grad
return bilstm
def initialize_model(gpu, vocab_size, v_vec, dropout_ratio, n_layers, model,
statistics_of_each_case_type):
is_gpu = True
if gpu == -1:
is_gpu = False
if model == 'Base' or model == 'FT':
bilstm = BiLSTM(vocab_size, v_vec, dropout_ratio, n_layers, gpu=is_gpu)
elif model == 'OneH':
bilstm = OneHot(vocab_size, v_vec, dropout_ratio, n_layers, gpu=is_gpu)
elif model == 'FA':
bilstm = FeatureAugmentation(vocab_size,
v_vec,
dropout_ratio,
n_layers,
gpu=is_gpu)
elif model == 'CPS':
bilstm = ClassProbabilityShift(
vocab_size,
v_vec,
dropout_ratio,
n_layers,
statistics_of_each_case_type=statistics_of_each_case_type,
gpu=is_gpu)
elif model == 'MIX':
bilstm = Mixture(
vocab_size,
v_vec,
dropout_ratio,
n_layers,
statistics_of_each_case_type=statistics_of_each_case_type,
gpu=is_gpu)
if is_gpu:
bilstm = bilstm.cuda()
for m in bilstm.modules():
print(m.__class__.__name__)
weights_init(m)
return bilstm
