elif args.model == "bilstm":
args.datatype = "word"
if args.externaldata != '':
if args.datatype == 'char':
(data, numclass) = pickle.load(open(args.externaldata, 'rb'))
testchar = dataloader.Chardata(data, getidx=True)
test_loader = DataLoader(testchar,
batch_size=args.batchsize,
num_workers=4,
shuffle=False)
alphabet = trainchar.alphabet
elif args.datatype == 'word':
(data, word_index,
numclass) = pickle.load(open(args.externaldata, 'rb'))
testword = dataloader.Worddata(data, getidx=True)
test_loader = DataLoader(testword,
batch_size=args.batchsize,
num_workers=4,
shuffle=False)
else:
if args.datatype == "char":
(train, test, numclass) = loaddata.loaddata(args.data)
trainchar = dataloader.Chardata(train, getidx=True)
testchar = dataloader.Chardata(test, getidx=True)
train_loader = DataLoader(trainchar,
batch_size=args.batchsize,
num_workers=4,
shuffle=True)
test_loader = DataLoader(testchar,
batch_size=args.batchsize,
elif args.model == "bilstm":
args.datatype = "word"
if args.datatype == "char":
(train, test, numclass) = loaddata.loaddata(args.data)
traintext = dataloader.Chardata(train,
backward=args.backward,
length=args.length)
testtext = dataloader.Chardata(test,
backward=args.backward,
length=args.length)
elif args.datatype == "word":
(train, test, tokenizer,
numclass) = loaddata.loaddatawithtokenize(args.data,
nb_words=args.dictionarysize)
traintext = dataloader.Worddata(train)
testtext = dataloader.Worddata(test)
train_loader = DataLoader(traintext,
batch_size=args.batchsize,
num_workers=4,
shuffle=True)
test_loader = DataLoader(testtext,
batch_size=args.batchsize,
num_workers=4,
shuffle=True)
if args.model == "charcnn":
model = model.CharCNN(classes=numclass)
elif args.model == "simplernn":
model = model.smallRNN(classes=numclass)
if args.datatype == "char":
(train, test, numclass) = loaddata.loaddata(args.data)
trainchar = dataloader.Chardata(train, backward=args.backward)
testchar = dataloader.Chardata(test, backward=args.backward)
train_loader = DataLoader(trainchar,
batch_size=args.batchsize,
num_workers=4,
shuffle=True)
test_loader = DataLoader(testchar,
batch_size=args.batchsize,
num_workers=4)
elif args.datatype == "word":
(train, test, tokenizer,
numclass) = loaddata.loaddatawithtokenize(args.data)
trainword = dataloader.Worddata(train, backward=args.backward)
testword = dataloader.Worddata(test, backward=args.backward)
train_loader = DataLoader(trainword,
batch_size=args.batchsize,
num_workers=4,
shuffle=True)
test_loader = DataLoader(testword,
batch_size=args.batchsize,
num_workers=4)
if args.model == "charcnn":
model = model.CharCNN(classes=numclass)
elif args.model == "simplernn":
model = model.smallRNN(classes=numclass)
elif args.model == "bilstm":
model = model.smallRNN(classes=numclass, bidirection=True)
if args.model == "charcnn":
args.datatype = "char"
elif args.model == "simplernn":
args.datatype = "word"
elif args.model == "bilstm":
args.datatype = "word"
if args.externaldata!='':
if args.datatype == 'char':
(data,numclass) = pickle.load(open(args.externaldata,'rb'))
testchar = dataloader.Chardata(data,backward = args.backward, getidx = True)
test_loader = DataLoader(testchar,batch_size=args.batchsize, num_workers=4, shuffle=False)
alphabet = trainchar.alphabet
elif args.datatype == 'word':
(data,word_index,numclass) = pickle.load(open(args.externaldata,'rb'))
testword = dataloader.Worddata(data,backward = args.backward, getidx = True)
test_loader = DataLoader(testword,batch_size=args.batchsize, num_workers=4,shuffle=False)
else:
if args.datatype == "char":
(train,test,numclass) = loaddata.loaddata(args.data)
trainchar = dataloader.Chardata(train,backward = args.backward, getidx = True)
testchar = dataloader.Chardata(test,backward = args.backward, getidx = True)
train_loader = DataLoader(trainchar,batch_size=args.batchsize, num_workers=4, shuffle = True)
test_loader = DataLoader(testchar,batch_size=args.batchsize, num_workers=4, shuffle=True)
alphabet = trainchar.alphabet
maxlength = args.charlength
elif args.datatype == "word":
(train,test,tokenizer,numclass) = loaddata.loaddatawithtokenize(args.data, nb_words = args.dictionarysize, datalen = args.wordlength)
word_index = tokenizer.word_index
trainword = dataloader.Worddata(train,backward = args.backward, getidx = True)
testword = dataloader.Worddata(test,backward = args.backward, getidx = True)
def main():
parser = argparse.ArgumentParser(description='Data')
parser.add_argument('--data',
type=int,
default=0,
metavar='N',
help='data 0 - 6')
parser.add_argument('--charlength',
type=int,
default=1014,
metavar='N',
help='length: default 1014')
parser.add_argument('--wordlength',
type=int,
default=500,
metavar='N',
help='length: default 500')
parser.add_argument('--model',
type=str,
default='charcnn',
metavar='N',
help='model type: LSTM as default')
parser.add_argument('--space',
type=bool,
default=False,
metavar='B',
help='Whether including space in the alphabet')
parser.add_argument(
'--trans',
type=bool,
default=False,
metavar='B',
help='Not implemented yet, add thesausus transformation')
parser.add_argument('--backward',
type=int,
default=-1,
metavar='B',
help='Backward direction')
parser.add_argument('--epochs',
type=int,
default=10,
metavar='B',
help='Number of epochs')
parser.add_argument('--batchsize',
type=int,
default=100,
metavar='B',
help='batch size')
parser.add_argument('--dictionarysize',
type=int,
default=20000,
metavar='B',
help='batch size')
parser.add_argument('--lr',
type=float,
default=0.0005,
metavar='B',
help='learning rate')
parser.add_argument('--maxnorm',
type=float,
default=400,
metavar='B',
help='learning rate')
args = parser.parse_args()
torch.manual_seed(9527)
torch.cuda.manual_seed_all(9527)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if args.model == "charcnn":
args.datatype = "char"
elif args.model == "simplernn":
args.datatype = "word"
elif args.model == "bilstm":
args.datatype = "word"
elif args.model == "smallcharrnn":
args.datatype = "char"
args.charlength = 300
elif args.model == "wordcnn":
args.datatype = "word"
print("Loading data...")
if args.datatype == "char":
(train, test, numclass) = loaddata.loaddata(args.data)
trainchar = dataloader.Chardata(train,
backward=args.backward,
length=args.charlength)
testchar = dataloader.Chardata(test,
backward=args.backward,
length=args.charlength)
train_loader = DataLoader(trainchar,
batch_size=args.batchsize,
num_workers=4,
shuffle=True)
test_loader = DataLoader(testchar,
batch_size=args.batchsize,
num_workers=4)
elif args.datatype == "word":
(train, test, tokenizer, numclass) = loaddata.loaddatawithtokenize(
args.data, nb_words=args.dictionarysize, datalen=args.wordlength)
trainword = dataloader.Worddata(train, backward=args.backward)
testword = dataloader.Worddata(test, backward=args.backward)
train_loader = DataLoader(trainword,
batch_size=args.batchsize,
num_workers=4,
shuffle=True)
test_loader = DataLoader(testword,
batch_size=args.batchsize,
num_workers=4)
if args.model == "charcnn":
model = CharCNN(classes=numclass)
elif args.model == "simplernn":
model = SmallRNN(classes=numclass)
elif args.model == "bilstm":
model = SmallRNN(classes=numclass, bidirection=True)
elif args.model == "smallcharrnn":
model = SmallCharRNN(classes=numclass)
elif args.model == "wordcnn":
model = WordCNN(classes=numclass)
model = model.to(device)
print(model)
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
bestacc = 0
for epoch in range(1, args.epochs + 1):
print('Start epoch %d' % epoch)
model.train()
for dataid, data in enumerate(train_loader):
inputs, target = data
inputs, target = Variable(inputs), Variable(target)
inputs, target = inputs.to(device), target.to(device)
output = model(inputs)
loss = F.nll_loss(output, target)
optimizer.zero_grad()
loss.backward()
optimizer.step()
correct = .0
total_loss = 0
model.eval()
for dataid, data in enumerate(test_loader):
inputs, target = data
inputs, target = inputs.to(device), target.to(device)
output = model(inputs)
loss = F.nll_loss(output, target)
total_loss += loss.item()
pred = output.data.max(1, keepdim=True)[1]
correct += pred.eq(target.data.view_as(pred)).cpu().sum().item()
acc = correct / len(test_loader.dataset)
avg_loss = total_loss / len(test_loader.dataset)
print('Epoch %d : Loss %.4f Accuracy %.5f' % (epoch, avg_loss, acc))
is_best = acc > bestacc
if is_best:
bestacc = acc
if args.dictionarysize != 20000:
fname = "outputs/" + args.model + str(
args.dictionarysize) + "_" + str(args.data)
else:
fname = "outputs/" + args.model + "_" + str(args.data)
save_checkpoint(
{
'epoch': epoch + 1,
'state_dict': model.state_dict(),
'bestacc': bestacc,
'optimizer': optimizer.state_dict(),
},
is_best,
filename=fname)