def main(**kwargs):
#动态加全职衰减
origin_weight_decay = 1e-5
opt.parse(kwargs, print_=False)
if opt.debug:
import ipdb
ipdb.set_trace()
###################################
# opt.model_names=['MultiCNNTextBNDeep','CNNText_inception',
# #'RCNN',
# 'LSTMText','CNNText_inception']
# opt.model_paths = ['checkpoints/MultiCNNTextBNDeep_word_0.410330780091','checkpoints/CNNText_tmp_word_0.41096749885',
# #'checkpoints/RCNN_word_0.411511574999',
# 'checkpoints/LSTMText_word_0.411994005382','checkpoints/CNNText_tmp_char_0.402429167301']
######################################
# opt.model_names=['MultiCNNTextBNDeep',
# #'CNNText_inception',
# #'RCNN',
# 'LSTMText_boost',
# #'CNNText_inception_boost'
# ]
# opt.model_paths = ['checkpoints/MultiCNNTextBNDeep_word_0.410330780091',
# # 'checkpoints/CNNText_tmp_word_0.41096749885',
# #'checkpoints/RCNN_word_0.411511574999',
# 'checkpoints/LSTMText_word_0.381833388089',
# #'checkpoints/CNNText_tmp_0.376364647145'
# ]
#####################################################3
opt.model_names = [
'MultiCNNTextBNDeep',
#'RCNN',
'LSTMText',
'CNNText_inception',
'CNNText_inception-boost'
]
opt.model_paths = [
'checkpoints/MultiCNNTextBNDeep_word_0.410330780091',
'checkpoints/LSTMText_word_0.381833388089',
'checkpoints/CNNText_tmp_0.380390420742',
#'checkpoints/RCNN_word_0.411511574999',
'checkpoints/CNNText_tmp_0.376364647145'
]
# opt.model_path='checkpoints/BoostModel_word_0.412524727048'
#**************************************
#############################################3
opt.model_names = [
'MultiCNNTextBNDeep', 'LSTMText', 'MultiCNNTextBNDeep-boost'
]
opt.model_paths = [
'checkpoints/MultiCNNTextBNDeep_word_0.410330780091',
'checkpoints/LSTMText_word_0.411994005382', None
]
opt.model_path = 'checkpoints/BoostModel2_word_0.410618920827'
#*********************************************
# opt.model_names=['MultiCNNTextBNDeep','LSTMText','CNNText_inception','RCNN']
# opt.model_paths = ['checkpoints/MultiCNNTextBNDeep_0.37125473788','checkpoints/LSTMText_word_0.381833388089','checkpoints/CNNText_tmp_0.376364647145','checkpoints/RCNN_char_0.3456599248']
model = getattr(models, opt.model)(opt).cuda()
# if opt.model_path:
# model.load(opt.model_path)
print(model)
opt.parse(kwargs, print_=True)
vis.reinit(opt.env)
pre_loss = 1.0
lr, lr2 = opt.lr, opt.lr2
loss_function = getattr(models, opt.loss)()
if opt.all:
dataset = ZhihuALLData(opt.train_data_path,
opt.labels_path,
type_=opt.type_)
else:
dataset = ZhihuData(opt.train_data_path,
opt.labels_path,
type_=opt.type_)
dataloader = data.DataLoader(dataset,
batch_size=opt.batch_size,
shuffle=opt.shuffle,
num_workers=opt.num_workers,
pin_memory=True)
optimizer = model.get_optimizer(opt.lr, opt.lr2, 0)
loss_meter = tnt.meter.AverageValueMeter()
score_meter = tnt.meter.AverageValueMeter()
best_score = 0
# pre_score = 0
for epoch in range(opt.max_epoch):
loss_meter.reset()
score_meter.reset()
for ii, ((title, content), label) in tqdm.tqdm(enumerate(dataloader)):
title, content, label = (Variable(
title[0].cuda()), Variable(title[1].cuda())), (Variable(
content[0].cuda()), Variable(content[1].cuda())), Variable(
label.cuda())
optimizer.zero_grad()
score = model(title, content)
loss = loss_function(score, opt.weight * label.float())
loss_meter.add(loss.data[0])
loss.backward()
optimizer.step()
if ii % opt.plot_every == opt.plot_every - 1:
if os.path.exists(opt.debug_file):
import ipdb
ipdb.set_trace()
predict = score.data.topk(5, dim=1)[1].cpu().tolist(
) #(dim=1,descending=True)[1][:,:5].tolist()
true_target = label.data.float().topk(
5, dim=1) #[1].cpu().tolist()#sort(dim=1,descending=True)
true_index = true_target[1][:, :5]
true_label = true_target[0][:, :5]
predict_label_and_marked_label_list = []
for jj in range(label.size(0)):
true_index_ = true_index[jj]
true_label_ = true_label[jj]
true = true_index_[true_label_ > 0]
predict_label_and_marked_label_list.append(
(predict[jj], true.tolist()))
score_, prec_, recall_, _ss = get_score(
predict_label_and_marked_label_list)
score_meter.add(score_)
vis.vis.text('prec:%s,recall:%s,score:%s,a:%s' %
(prec_, recall_, score_, _ss),
win='tmp')
vis.plot('scores', score_meter.value()[0])
#eval()
vis.plot('loss', loss_meter.value()[0])
# 随机展示一个输出的分布
k = t.randperm(label.size(0))[0]
output = t.nn.functional.sigmoid(score)
# vis.vis.histogram(
# output.data[k].view(-1).cpu(), win=u'output_hist', opts=dict
# (title='output_hist'))
# print "epoch:%4d/%4d,time: %.8f,loss: %.8f " %(epoch,ii,time.time()-start,loss_meter.value()[0])
if ii % opt.decay_every == opt.decay_every - 1:
del loss
scores, prec_, recall_, _ss = val(model, dataset)
if scores > best_score:
best_score = scores
best_path = model.save(name=str(scores), new=True)
vis.log({
' epoch:': epoch,
' lr: ': lr,
'scores': scores,
'prec': prec_,
'recall': recall_,
'ss': _ss,
'scores_train': score_meter.value()[0],
'loss': loss_meter.value()[0]
})
if scores < best_score:
model.load(best_path, change_opt=False)
#lr = lr*opt.lr_decay
#optimizer = model.get_optimizer(lr)
lr = lr * opt.lr_decay
# 第二种降低学习率的方法:不会有moment等的丢失
if lr2 == 0: lr2 = 2e-4
else: lr2 = lr2 * opt.lr_decay
optimizer = model.get_optimizer(lr, lr2, 0)
origin_weight_decay = 5 * origin_weight_decay
# optimizer = model.get_optimizer(lr,lr2,0,weight_decay=origin_weight_decay)
# origin_weight_decay=5*origin_weight_decay
# for param_group in optimizer.param_groups:
# param_group['lr'] *= opt.lr_decay
# if param_group['lr'] ==0:
# param_group['lr'] = 1e-4
pre_loss = loss_meter.value()[0]
# pre_score = score_meter.value()[0]
# pre_score = scores
loss_meter.reset()
score_meter.reset()
if lr < opt.min_lr:
break
def main(**kwargs):
#动态加全职衰减
opt.parse(kwargs, print_=False)
if opt.debug:
import ipdb
ipdb.set_trace()
# opt.model_names=['MultiCNNTextBNDeep','RCNN','LSTMText','CNNText_inception','RCNN','CNNText_inception','LSTMText']
# opt.model_paths = ['checkpoints/MultiCNNTextBNDeep_word_0.41124002492','checkpoints/RCNN_word_0.411511574999','checkpoints/LSTMText_word_0.411994005382','checkpoints/CNNText_tmp_char_0.402429167301','checkpoints/RCNN_char_0.403710422571','checkpoints/CNNText_tmp_word_0.41096749885','checkpoints/LSTMText_char_0.403192339135',]#'checkpoints/FastText_word_0.400391584867']
#############################iMultiModelAll2_word_0.425600838271##################################
# opt.model_names=['MultiCNNTextBNDeep','RCNN','LSTMText','RCNN','CNNText_inception']
# opt.model_paths = ['checkpoints/MultiCNNTextBNDeep_word_0.41124002492','checkpoints/RCNN_word_0.411511574999','checkpoints/LSTMText_word_0.411994005382','checkpoints/RCNN_char_0.403710422571','checkpoints/CNNText_tmp_char_0.402429167301']
###################################################################################################3
#############################################################
# opt.model_names=['MultiCNNTextBNDeep','RCNN','LSTMText','RCNN','CNNText_inception']
# opt.model_paths = ['checkpoints/MultiCNNTextBNDeep_0.37125473788','checkpoints/RCNN_word_0.373609030286','checkpoints/LSTMText_word_0.381833388089','checkpoints/RCNN_char_0.3456599248','checkpoints/CNNText_tmp_0.352036505041']
##################################################################333
# opt.model_names=['LSTMText','MultiCNNTextBNDeep']
# opt.model_paths=['checkpoints/LSTMText_word_0.396765494482','checkpoints/MultiCNNTextBNDeep_word_0.391018392216']
# opt.fold=1
# from data.dataset import ALLFoldData as ZhihuALLData
########################################################################
# opt.model_names=['MultiCNNTextBNDeep','RCNN','LSTMText','RCNN','CNNText_inception']
# opt.model_paths = ['checkpoints/MultiCNNTextBNDeep_0.37125473788','checkpoints/RCNN_word_0.373609030286','checkpoints/LSTMText_word_0.381833388089','checkpoints/RCNN_char_0.3456599248','checkpoints/CNNText_tmp_0.352036505041']
#######################################0.0.41884129858126845-force#####################
# opt.model_names=['MultiCNNTextBNDeep','RCNN','LSTMText','RCNN','MultiCNNTextBNDeep']
# opt.model_paths = ['checkpoints/MultiCNNTextBNDeep_word_0.410011182415','checkpoints/RCNN_word_0.413446202556','checkpoints/LSTMText_word_0.413681107036','checkpoints/RCNN_char_0.398655349075','checkpoints/MultiCNNTextBNDeep_char_0.38666657051']
#######################################################################
############################################################################################
opt.model_names = [
'MultiCNNTextBNDeep', 'FastText3', 'LSTMText', 'CNNText_inception'
]
opt.model_paths = [
'checkpoints/MultiCNNTextBNDeep_word_0.41124002492',
'checkpoints/FastText3_word_0.40810787337',
'checkpoints/LSTMText_word_0.413681107036',
'checkpoints/CNNText_tmp_char_0.402429167301'
]
########################################################################################3
model = getattr(models, opt.model)(opt).cuda()
if opt.model_path:
model.load(opt.model_path)
print(model)
opt.parse(kwargs, print_=True)
vis.reinit(opt.env)
pre_loss = 1.0
lr, lr2 = opt.lr, opt.lr2
loss_function = getattr(models, opt.loss)()
if opt.all:
dataset = ZhihuALLData(opt.train_data_path,
opt.labels_path,
type_=opt.type_,
augument=opt.augument)
# else :dataset = ZhihuData(opt.train_data_path,opt.labels_path,type_=opt.type_)
dataloader = data.DataLoader(dataset,
batch_size=opt.batch_size,
shuffle=opt.shuffle,
num_workers=opt.num_workers,
pin_memory=True)
optimizer = model.get_optimizer(opt.lr, opt.lr2)
loss_meter = tnt.meter.AverageValueMeter()
score_meter = tnt.meter.AverageValueMeter()
best_score = 0
# pre_score = 0
for epoch in range(opt.max_epoch):
loss_meter.reset()
score_meter.reset()
for ii, ((title, content), label) in tqdm.tqdm(enumerate(dataloader)):
title, content, label = (Variable(
title[0].cuda()), Variable(title[1].cuda())), (Variable(
content[0].cuda()), Variable(content[1].cuda())), Variable(
label.cuda())
optimizer.zero_grad()
score = model(title, content)
loss = loss_function(score, label.float())
loss_meter.add(loss.data[0])
loss.backward()
optimizer.step()
if ii % opt.plot_every == opt.plot_every - 1:
if os.path.exists(opt.debug_file):
import ipdb
ipdb.set_trace()
predict = score.data.topk(5, dim=1)[1].cpu().tolist(
) #(dim=1,descending=True)[1][:,:5].tolist()
true_target = label.data.float().topk(
5, dim=1) #[1].cpu().tolist()#sort(dim=1,descending=True)
true_index = true_target[1][:, :5]
true_label = true_target[0][:, :5]
predict_label_and_marked_label_list = []
for jj in range(label.size(0)):
true_index_ = true_index[jj]
true_label_ = true_label[jj]
true = true_index_[true_label_ > 0]
predict_label_and_marked_label_list.append(
(predict[jj], true.tolist()))
score_, prec_, recall_, _ss = get_score(
predict_label_and_marked_label_list)
score_meter.add(score_)
vis.vis.text('prec:%s,recall:%s,score:%s,a:%s' %
(prec_, recall_, score_, _ss),
win='tmp')
vis.plot('scores', score_meter.value()[0])
vis.plot('loss', loss_meter.value()[0])
if ii % opt.decay_every == opt.decay_every - 1:
del loss
scores, prec_, recall_, _ss = val(model, dataset)
vis.log({
' epoch:': epoch,
' lr: ': lr,
'scores': scores,
'prec': prec_,
'recall': recall_,
'ss': _ss,
'scores_train': score_meter.value()[0],
'loss': loss_meter.value()[0]
})
if scores > best_score:
best_score = scores
best_path = model.save(name=str(scores), new=True)
if scores < best_score:
model.load(best_path, change_opt=False)
lr = lr * opt.lr_decay
if lr2 == 0: lr2 = 1e-4
else: lr2 = lr2 * 0.5
optimizer = model.get_optimizer(lr, lr2, 0)
pre_loss = loss_meter.value()[0]
loss_meter.reset()
score_meter.reset()