def predict(args):
device = torch.device(args.device)
cls_embed = torch.load(args.embedding_save_path)
model = textCNN(args.embedding_size, args.cls_num, args.l1_channels_num)
model.load_state_dict(torch.load(args.model_save_path))
model.eval()
model = model.to(device)
cls_embed = cls_embed.to(device)
cls_embed.eval()
dev_dataset = DataSet(args.predict_data, None, args.batch_size)
dev_dataset.reorderForEval()
towrite = open(args.predict_writeto, "w+")
towrite.write("idx,labels\n")
idx = 0
print("Begin Predict task...")
while (True):
(example, dump), p = dev_dataset.getPredictBatch(False)
example = cls_embed(example, device=device)
# print(example.size())
outs = model(example)
outs = (torch.argmax(outs, -1) + 1).squeeze().tolist()
for out in zip(example[1], outs):
towrite.write("{0},{1}\n".format(out[0], int(out[1])))
idx += 1
if (p): break
towrite.close()
print("Predict task Done!")
def train(args):
device = torch.device(args.device)
cls_embed = ModelEmbeddings.load_from_file(args.pretrain_vector)
train_data = DataSet(args.train_data, args.train_labels, args.batch_size,
int(1e7))
train_data.reorderForEval()
# train_data.padtoMaxLen()
print('loading dev set...')
# devSet,devlabels,devNum=DataSet.load_devSet(args.dev_data,args.dev_labels)
dev_dataset = DataSet(args.dev_data, args.dev_labels, args.batch_size,
int(1e7))
dev_dataset.reorderForEval()
print('Done dev loading.')
model = textCNN(args.embedding_size, args.cls_num, args.l1_channels_num)
model.train()
model = model.to(device)
cls_embed.train()
cls_embed = cls_embed.to(device)
optimizer = torch.optim.Adam([{
'params': model.parameters()
}, {
'params': cls_embed.parameters(),
'lr': 1.75 * args.lr
}],
lr=float(args.lr))
Loss_fun = torch.nn.CrossEntropyLoss(reduction='sum')
print('begin Maximum Likelihood training')
epoch = 0
sum_loss = 0
step = 0
tot_step = 0
pi_2 = np.pi / 2
while (True):
optimizer.zero_grad()
if (args.use_cos_batch and epoch):
for param_group in optimizer.param_groups:
param_group['lr'] = args.lr * np.cos(pi_2 * (step / tot_step))
example, p = train_data.getTestBatch()
t = example[0]
l = example[1]
example = None
l = torch.tensor(l, device=device)
# print("Doing word embed")
# print(type(t))
# print(type(l))
# print(l.requires_grad)
t = cls_embed(t, device=device)
# t=t.detach()
# print("Done word embed")
# print("feed into model...")
outPuts = model(t)
# print("Get answer!")
# print("Caluating Loss")
# print(outPuts.size())
# print(l.size())
# print(l)
# print(outPuts)
loss = Loss_fun(outPuts, l)
loss.backward()
optimizer.step()
loss /= len(outPuts)
sum_loss += loss
print("epoch:{0},step:{1},train loss:{2}".format(epoch, step, loss))
step += 1
# print("Doing backPro")
# print("Done backPro")
# print("Setping...")
# print("Done Step!")
# print('Current Batch Loss:{0}'.format(loss))
if (p):
tot_step = step
epoch += p
print("Epoch mean Loss:{0}".format(sum_loss / step))
step = 0
sum_loss = 0
accuary, F1 = test(args, model, dev_dataset, cls_embed,
args.cls_num, device)
if (model.lastScore < F1):
print("F1 score grow from {0} to {1}, save model...".format(
model.lastScore, F1))
model.lastScore = F1
torch.save(model.state_dict(), args.model_save_path)
torch.save(cls_embed, args.embedding_save_path)
args.lr = args.lr * np.exp(args.lr_decay)
for param_group in optimizer.param_groups:
param_group['lr'] = args.lr
else:
args.lr = args.lr / 2
for param_group in optimizer.param_groups:
param_group['lr'] = args.lr
if (epoch == args.max_epoch): break
