def get_param_weight_decay_dict(param_group_name_list):
return [{
'params': [
p for n, p in model.named_parameters()
if not any(nd in n for nd in param_group_name_list)
],
"weight_decay":
args.weight_decay
}, {
'params': [
p for n, p in model.named_parameters()
if any(nd in n for nd in param_group_name_list)
],
'weight_decay':
0.0
}]
def save_layer_fig(model,exam_id, org_input, org_target, prediction,
slice_level_performance, result_dir):
result_exam_dir = os.path.join(result_dir, exam_id)
if not os.path.exists(result_exam_dir):
os.makedirs(result_exam_dir)
for name, param in model.named_parameters():
print(name, '\t\t', param.shape)
def model_save(fn, all_model=1, model_para=0):
if all_model:
with open(fn, 'wb') as f:
torch.save([model, optimizer], f)
if model_para:
para_dic = {}
for name, para in model.named_parameters():
para_dic[name] = para.clone().cpu().data.numpy()
with open(fn, 'wb') as f:
pickle.dump(para_dic, f)
def train():
# Turn on training mode which enables dropout.
model.train()
total_loss = 0
start_time = time.time()
ntokens = len(corpus.dictionary)
hidden = model.init_hidden(args.batch_size)
criterion = nn.CrossEntropyLoss()
for batch, i in enumerate(range(0, train_data.size(0) - 1, args.bptt)):
data, targets = get_batch(train_data, i)
# Starting each batch, we detach the hidden state from how it was previously produced.
# If we didn't, the model would try backpropagating all the way to start of the dataset.
hidden = repackage_hidden(hidden)
model.zero_grad()
logits, hidden = model(data, hidden)
loss_values = model.calculate_loss_values(logits, targets)
loss_values_data = tuple(map(lambda x: x.data[0], loss_values))
cross_entropy_val, center_loss_val = loss_values_data
cross_entropy_loss = loss_values[0]
center_loss = loss_values[1]
if center_loss_factor > 0:
train_loss = cross_entropy_loss + center_loss_factor*center_loss
else:
train_loss = cross_entropy_loss
train_loss.backward()
train_loss_val = train_loss.data[0]
perplexity_val = math.exp(cross_entropy_val)
train_metrics['train_loss'].append(train_loss_val)
train_metrics['center_loss'].append(center_loss_val)
train_metrics['cross_entropy'].append(cross_entropy_val)
train_metrics['perplexity'].append(perplexity_val)
# `clip_grad_norm` helps prevent the exploding gradient problem in RNNs / LSTMs.
torch.nn.utils.clip_grad_norm(model.parameters(), args.clip)
for name,p in model.named_parameters():
if p.requires_grad:
p.data.add_(-lr, p.grad.data)
total_loss += cross_entropy_val
if batch % args.log_interval == 0 and batch > 0:
cur_loss = total_loss / args.log_interval
elapsed = time.time() - start_time
print('| epoch {:3d} | {:5d}/{:5d} batches | lr {:02.2f} | ms/batch {:5.2f} | '
'ce loss {:5.2f} | ppl {:8.2f}'.format(
epoch, batch, len(train_data) // args.bptt, lr,
elapsed * 1000 / args.log_interval, cur_loss, math.exp(cur_loss)))
print('| train loss: {:.3f} | center loss: {:.3f} | cross entropy: {:.3f} | '
'perplexity: {:.3f}'.format(train_loss_val, center_loss_val,
cross_entropy_val, perplexity_val))
total_loss = 0
start_time = time.time()
def print_model_param_info(model):
print('[Model parameters to train]')
for name, param in model.named_parameters():
if param.requires_grad:
print(name, param.data) # param.data
# for param in model.parameters():
# if param.requires_grad:
# print(param.name, param.size())
# print(param.data)
return
def trainable_params(model, feature_extract):
"""
Prints and returns all the trainable parameters in model.
:param model: the model instance
:param feature_extract: if True, only params with *requires_grad* will be returned.
:return: a list containing the model's trainable params.
"""
params_to_update = model.parameters()
print("Params to learn:")
if feature_extract:
params_to_update = []
for name, param in model.named_parameters():
if param.requires_grad == True:
params_to_update.append(param)
print("\t", name)
else:
for name, param in model.named_parameters():
if param.requires_grad == True:
print("\t", name)
return params_to_update
def finetune(cur_iter, cur_learning_rate):
# Turn on training mode which enables dropout.
model.train()
total_loss = 0
start_time = time.time()
ntokens = len(corpus.dictionary)
hidden = model.init_hidden(args.batch_size)
print("cur learning rate = ", cur_learning_rate)
print("iteration", cur_iter)
for batch, i in enumerate(range(0, train_data.size(0) - 1, args.bptt)):
data, targets = get_batch(train_data, i)
# print(data.size())
# Starting each batch, we detach the hidden state from how it was previously produced.
# If we didn't, the model would try backpropagating all the way to start of the dataset
# (previous batches).
hidden = repackage_hidden(hidden)
model.zero_grad()
output, hidden = model(data, hidden)
loss = criterion(output.view(-1, ntokens), targets)
loss.backward()
# `clip_grad_norm` helps prevent the exploding gradient problem in RNNs / LSTMs.
torch.nn.utils.clip_grad_norm_(model.parameters(), args.clip)
for name, p in model.named_parameters():
if p.requires_grad:
p.data.add_(
-get_discriminative_lr(name, cur_learning_rate),
p.grad.data) # p.data = p.data += -lr * p.grad.data
total_loss += loss.item()
cur_learning_rate = scheduled_slanted_lr(cur_iter, T, args.cut_frac,
args.ratio, args.lr)
cur_iter += 1
#print(batch)
if batch % args.log_interval == 0 and batch > 0:
cur_loss = total_loss / args.log_interval
elapsed = time.time() - start_time
print(
'| epoch {:3d} | {:5d}/{:5d} batches | lr {:04.4f} | ms/batch {:5.2f} | '
'loss {:5.2f} | ppl {:8.2f}'.format(
epoch, batch,
len(train_data) // args.bptt, cur_learning_rate,
elapsed * 1000 / args.log_interval, cur_loss,
math.exp(cur_loss)))
total_loss = 0
start_time = time.time()
return cur_iter, cur_learning_rate
def train():
# Turn on training mode which enables dropout.
model.train()
total_loss = 0.
start_time = time.time()
ntokens = len(corpus.dictionary)
hidden = model.init_hidden(args.batch_size)
for batch, i in enumerate(range(0, train_data.size(0) - 1, args.bptt)):
data, targets = get_batch(train_data, i)
# Starting each batch, we detach the hidden state from how it was previously produced.
# If we didn't, the model would try backpropagating all the way to start of the dataset.
hidden = repackage_hidden(hidden)
model.zero_grad()
output, hidden = model(data, hidden)
loss = criterion(output.view(-1, ntokens), targets)
orth_reg = 0.0
for name, param in model.named_parameters():
if param.requires_grad and 'U' in name:
d = min(list(param.shape))
diff = torch.matmul(param.t(), param) - torch.eye(d).cuda()
orth_reg += torch.sum(diff**2) / (d * d)
elif param.requires_grad and 'V' in name:
d = min(list(param.shape))
diff = torch.matmul(param, param.t()) - torch.eye(d).cuda()
orth_reg += torch.sum(diff**2) / (d * d)
total = loss + args.od * orth_reg
total.backward()
model.rnn.svd_grad()
# `clip_grad_norm` helps prevent the exploding gradient problem in RNNs / LSTMs.
torch.nn.utils.clip_grad_norm_(model.parameters(), args.clip)
for p in model.parameters():
p.data.add_(-lr, p.grad.data)
total_loss += loss.item()
if batch % args.log_interval == 0 and batch > 0:
cur_loss = total_loss / args.log_interval
elapsed = time.time() - start_time
print(
'| epoch {:3d} | {:5d}/{:5d} batches | lr {:02.4f} | ms/batch {:5.2f} | '
'loss {:5.2f} | ppl {:8.2f}'.format(
epoch, batch,
len(train_data) // args.bptt, lr,
elapsed * 1000 / args.log_interval, cur_loss,
math.exp(cur_loss)))
total_loss = 0
start_time = time.time()
def unfreeze_rln(self):
"""
Method to freeze RLN parameters
"""
if isinstance(self.net, nn.DataParallel):
model = self.net.module.model
else:
model = self.net.model
for name, param in model.named_parameters():
param.learn = True
for name, param in model.bert.named_parameters():
param.learn = True
for name, param in model.bert.named_parameters():
param.learn = True
def test(epoch):
global best_acc
model.eval()
test_loss = 0
correct = 0
total = 0
with torch.no_grad():
for batch_index, (inputs, targets) in enumerate(testloader):
inputs, targets = inputs.to(device), targets.to(device)
inputs = Variable(inputs)
targets = Variable(targets)
# Forward
outputs = model(inputs)
loss = criterion(outputs, targets)
# Results
test_loss += loss.item()
_, predicted = outputs.max(1)
total += targets.size(0)
correct += predicted.eq(targets).sum().item()
print('Loss: %.3f | Accuracy: %.3f' %
(test_loss / 100, 100. * correct / total))
# Save the model
acc = 100. * correct / total
if acc > best_acc:
print('Saving..')
state = {
'net': model.state_dict(),
'acc': acc,
'epoch': epoch,
}
if not os.path.isdir('checkpoint'):
os.mkdir('checkpoint')
torch.save(state, './checkpoint/Baseline.ckpt')
best_acc = acc
# Plot the model
info = {'loss': test_loss, 'accuracy': acc}
for tag, value in info.items():
logger.scalar_summary(tag, value, epoch + 1)
for tag, value in model.named_parameters():
tag = tag.replace('.', '/')
logger.histo_summary(tag, value.data.cpu().numpy(), epoch + 1)
logger.histo_summary(tag + '/grad',
value.grad.data.cpu().numpy(), epoch + 1)
def train(lr, epoch = 0):
# Turn on training mode which enables dropout.
model.train()
total_loss = 0.
start_time = time.time()
ntokens = len(corpus.dictionary)
hidden = model.init_hidden(args.batch_size)
for batch, i in enumerate(range(0, train_data.size(0) - 1, args.bptt)):
data, targets = get_batch(train_data, i)
# Starting each batch, we detach the hidden state from how it was previously produced.
# If we didn't, the model would try backpropagating all the way to start of the dataset.
hidden = repackage_hidden(hidden)
model.zero_grad()
output, hidden = model(data, hidden)
loss = criterion(output.view(-1, ntokens), targets)
# if args.admm:
if stage == 'admm':
ce_loss = loss
admm.admm_update(args,ADMM,model,None,None,None,epoch,None,batch) # update Z and U
ce_loss,admm_loss,mixed_loss = admm.append_admm_loss(args,ADMM,model,ce_loss) # append admm losss
loss = mixed_loss
loss.backward()
# `clip_grad_norm` helps prevent the exploding gradient problem in RNNs / LSTMs.
torch.nn.utils.clip_grad_norm_(model.parameters(), args.clip)
if stage == 'masked_retrain':
for name,W in model.named_parameters():
if name in config.masks:
W.grad.data *= config.masks[name]
for p in model.parameters():
p.data.add_(-lr, p.grad.data)
total_loss += loss.item()
if batch % args.log_interval == 0 and batch > 0:
cur_loss = total_loss / args.log_interval
elapsed = time.time() - start_time
print('| epoch {:3d} | {:5d}/{:5d} batches | lr {:02.2f} | ms/batch {:5.2f} | '
'loss {:5.2f} | ppl {:8.2f}'.format(
epoch, batch, len(train_data) // args.bptt, lr,
elapsed * 1000 / args.log_interval, cur_loss, math.exp(cur_loss)))
total_loss = 0
start_time = time.time()
def eval_smooth(prev_model, model, num_pts=1):
alphas = np.arange(1, num_pts + 1) / (num_pts + 1)
gnorm = eval_grad(prev_model)
update_size = utils.norm_diff(utils.get_model_params(model), \
utils.get_model_params(prev_model))
max_smooth = -1
for alpha in alphas:
new_model = copy.deepcopy(prev_model)
for n, p in new_model.named_parameters():
p.data = alpha * p.data + (
1 - alpha) * {n: p
for n, p in model.named_parameters()}[n].data
eval_grad(new_model)
smooth = utils.norm_diff(
utils.get_model_grads(new_model),
utils.get_model_grads(prev_model)) / (update_size * (1 - alpha))
max_smooth = max(smooth, max_smooth)
return max_smooth, gnorm
def train():
# Turn on training mode which enables dropout.
model.train()
total_loss = 0
start_time = time.time()
ntokens = len(corpus.dictionary)
hidden = model.init_hidden(args.batch_size)
for batch, i in enumerate(range(0, train_data.size(0) - 1, args.bptt)):
data, targets = get_batch(train_data, i)
# Starting each batch, we detach the hidden state from how it was previously produced.
# If we didn't, the model would try backpropagating all the way to start of the dataset.
hidden = repackage_hidden(hidden)
model.zero_grad()
output, hidden = model(data, hidden)
#print(output.size())
loss = criterion(output.view(-1, ntokens), targets)
loss.backward()
# `clip_grad_norm` helps prevent the exploding gradient problem in RNNs / LSTMs.
torch.nn.utils.clip_grad_norm(model.parameters(), args.clip)
#print(model.state_dict())
#print("Done***")
for n,p in model.named_parameters():
p.data.add_(-lr, p.grad.data)
total_loss += loss.data
if batch % args.log_interval == 0 and batch > 0:
cur_loss = total_loss[0] / args.log_interval
elapsed = time.time() - start_time
print('| epoch {:3d} | {:5d}/{:5d} batches | lr {:02.2f} | ms/batch {:5.2f} | '
'loss {:5.2f} | perplexity {:8.2f}'.format(
epoch, batch, len(train_data) // args.bptt, lr,
elapsed * 1000 / args.log_interval, cur_loss, math.exp(cur_loss)))
total_loss = 0
start_time = time.time()
def train(epoch, optimizer, compression_scheduler=None):
# Turn on training mode which enables dropout.
model.train()
total_samples = train_data.size(0)
steps_per_epoch = math.ceil(total_samples / args.bptt)
total_loss = 0.
start_time = time.time()
ntokens = len(corpus.dictionary)
hidden = model.init_hidden(args.batch_size)
# The line below was fixed as per: https://github.com/pytorch/examples/issues/214
for batch, i in enumerate(range(0, train_data.size(0), args.bptt)):
data, targets = get_batch(train_data, i)
# Starting each batch, we detach the hidden state from how it was previously produced.
# If we didn't, the model would try backpropagating all the way to start of the dataset.
hidden = repackage_hidden(hidden)
if compression_scheduler:
compression_scheduler.on_minibatch_begin(
epoch,
minibatch_id=batch,
minibatches_per_epoch=steps_per_epoch)
output, hidden = model(data, hidden)
loss = criterion(output.view(-1, ntokens), targets)
if compression_scheduler:
# Before running the backward phase, we add any regularization loss computed by the scheduler
regularizer_loss = compression_scheduler.before_backward_pass(
epoch,
minibatch_id=batch,
minibatches_per_epoch=steps_per_epoch,
loss=loss)
loss += regularizer_loss
optimizer.zero_grad()
loss.backward()
# `clip_grad_norm` helps prevent the exploding gradient problem in RNNs / LSTMs.
torch.nn.utils.clip_grad_norm_(model.parameters(), args.clip)
optimizer.step()
total_loss += loss.item()
if compression_scheduler:
compression_scheduler.on_minibatch_end(
epoch,
minibatch_id=batch,
minibatches_per_epoch=steps_per_epoch)
if batch % args.log_interval == 0 and batch > 0:
cur_loss = total_loss / args.log_interval
elapsed = time.time() - start_time
lr = optimizer.param_groups[0]['lr']
msglogger.info(
'| epoch {:3d} | {:5d}/{:5d} batches | lr {:02.4f} | ms/batch {:5.2f} '
'| loss {:5.2f} | ppl {:8.2f}'.format(
epoch, batch,
len(train_data) // args.bptt, lr,
elapsed * 1000 / args.log_interval, cur_loss,
math.exp(cur_loss)))
total_loss = 0
start_time = time.time()
stats = ('Peformance/Training/',
OrderedDict([('Loss', cur_loss),
('Perplexity', math.exp(cur_loss)),
('LR', lr), ('Batch Time', elapsed * 1000)]))
steps_completed = batch + 1
distiller.log_training_progress(stats, model.named_parameters(),
epoch, steps_completed,
steps_per_epoch, args.log_interval,
[tflogger])
}
training_loader = DataLoader(training_set, **train_params)
testing_loader = DataLoader(testing_set, **test_params)
# GPU check and setting the device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
n_gpu = torch.cuda.device_count()
print(torch.cuda.get_device_name(0))
# Object of RobertaBaseClass and setting to device
model = model.RobertaBaseClass()
model.to(device)
# Model parameters
param_optimizer = list(model.named_parameters())
no_decay = ["bias", "LayerNorm.bias", "LayerNorm.weight"]
optimizer_parameters = [
{
"params": [
p for n, p in param_optimizer
if not any(nd in n for nd in no_decay)
],
"weight_decay":
0.001,
},
{
"params":
[p for n, p in param_optimizer if any(nd in n for nd in no_decay)],
"weight_decay":
0.0,
def main():
dataframe = pd.read_csv('../input/imdb.csv') # load dataframe
dataframe.sentiment = dataframe.sentiment.apply(lambda x: 1
if x == 'positive' else 0)
# sentiment is category target variable so we have to label encode it, we can do it like this by hand, or simply with sklearn.model_selection.LabelEncoder
# now split data into validation and training
df_train, df_valid = model_selection.train_test_split(
dataframe,
test_size=0.1, # 10 percent of dataframe will be for validation
random_state=
42, # if we are going to run multiple time this script, random state enables that everytime we get same split with same random state
shuffle=True, # shuffle indices
stratify=dataframe.sentiment.
values # same distribution in train and valid
)
df_train = df_train.reset_index(
drop=True) # we reset indices from 0 to len(df_train)
df_valid = df_valid.reset_index(
drop=True) # we reset indices from 0 to len(df_valid)
# make datasets with our class in order to make data loaders
training_dataset = dataset.BERTdataset(review=df_train.review.values,
sentiment=df_train.sentiment.values)
# from dataset to dataloader
training_data_loader = torch.utils.data.DataLoader(
dataset=training_dataset,
batch_size=config.TRAINING_BATCH_SIZE,
shuffle=True,
num_workers=4)
validation_dataset = dataset.BERTdataset(
review=df_valid.review.values,
sentiment=df_valid.sentiment.values,
)
# from dataset to dataloader
validation_data_loader = torch.utils.data.DataLoader(
dataset=validation_dataset,
batch_size=config.VALIDATION_BATCH_SIZE,
shuffle=False,
num_workers=4)
device = torch.device('cuda')
model = model.BERTsentiment()
model.to(device) # move model to cuda device
# params to optimize
param_optimizer = list(model.named_parameters())
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
optimizer_parameters = [{
'params':
[p for n, p in param_optimizer if not any(nd in n for nd in no_decay)],
'weight_decay':
0.001
}, {
'params':
[p for n, p in param_optimizer if any(nd in n for nd in no_decay)],
'weight_decay':
0.00
}]
number_of_training_steps = int(
len(df_train) / config.TRAINING_BATCH_SIZE * config.EPOCHS)
#AdamW focuses on regularization and model does better on generalization
optimizer = AdamW(params=param_optimizer, lr=3e-5)
scheduler = get_linear_schedule_with_warmup(
optimizer=optimizer,
num_warmup_steps=0,
num_training_steps=number_of_training_steps,
)
best_accuracy = []
for epoch in range(config.EPOCHS):
print('EPOCH:', epoch + 1)
engine.training_loop(training_data_loader, model, optimizer, scheduler,
device)
outputs, sentiments = engine.validation_loop(validation_data_loader,
model, device)
# distribution is 50 50 so we can use acc score
outputs = np.array(outputs) >= 0.5 # positive class
accuracy = metrics.accuracy_score(sentiments, outputs)
print('ACCURACY SCORE', {accuracy})
if accuracy > best_accuracy:
torch.save(model.state_dict(),
config.MODEL_PATH) # save model in working dir
best_accuracy = accuracy
def main():
"""
Training and validation.
"""
global epochs_since_improvement, start_epoch, label_map, best_loss, epoch, checkpoint
# Initialize model or load checkpoint
if checkpoint is None:
model = SSD300(n_classes=n_classes)
# Initialize the optimizer, with twice the default learning rate for biases, as in the original Caffe repo
biases = list()
not_biases = list()
for param_name, param in model.named_parameters():
if param.requires_grad:
if param_name.endswith('.bias'):
biases.append(param)
else:
not_biases.append(param)
optimizer = torch.optim.SGD(params=[{
'params': biases,
'lr': 2 * lr
}, {
'params': not_biases
}],
lr=lr,
momentum=momentum,
weight_decay=weight_decay)
else:
checkpoint = torch.load(checkpoint)
start_epoch = checkpoint['epoch'] + 1
epochs_since_improvement = checkpoint['epochs_since_improvement']
best_loss = checkpoint['best_loss']
print(
'\nLoaded checkpoint from epoch %d. Best loss so far is %.3f.\n' %
(start_epoch, best_loss))
model = checkpoint['model']
optimizer = checkpoint['optimizer']
# Move to default device
model = model.to(device)
criterion = MultiBoxLoss(priors_cxcy=model.priors_cxcy).to(device)
# Custom dataloaders
train_dataset = PascalVOCDataset(data_folder, split='train')
val_dataset = PascalVOCDataset(data_folder, split='test')
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=batch_size,
shuffle=True,
collate_fn=train_dataset.collate_fn,
num_workers=workers,
pin_memory=True) # note that we're passing the collate function here
val_loader = torch.utils.data.DataLoader(val_dataset,
batch_size=batch_size,
shuffle=True,
collate_fn=val_dataset.collate_fn,
num_workers=workers,
pin_memory=True)
# Epochs
for epoch in range(start_epoch, epochs):
# Paper describes decaying the learning rate at the 80000th, 100000th, 120000th 'iteration', i.e. model update or batch
# The paper uses a batch size of 32, which means there were about 517 iterations in an epoch
# Therefore, to find the epochs to decay at, you could do,
# if epoch in {80000 // 517, 100000 // 517, 120000 // 517}:
# adjust_learning_rate(optimizer, 0.1)
# In practice, I just decayed the learning rate when loss stopped improving for long periods,
# and I would resume from the last best checkpoint with the new learning rate,
# since there's no point in resuming at the most recent and significantly worse checkpoint.
# So, when you're ready to decay the learning rate, just set checkpoint = 'BEST_checkpoint_ssd300.pth.tar' above
# and have adjust_learning_rate(optimizer, 0.1) BEFORE this 'for' loop
# One epoch's training
train(train_loader=train_loader,
model=model,
criterion=criterion,
optimizer=optimizer,
epoch=epoch)
# One epoch's validation
val_loss = validate(val_loader=val_loader,
model=model,
criterion=criterion)
# Did validation loss improve?
is_best = val_loss < best_loss
best_loss = min(val_loss, best_loss)
if not is_best:
epochs_since_improvement += 1
print("\nEpochs since last improvement: %d\n" %
(epochs_since_improvement, ))
else:
epochs_since_improvement = 0
# Save checkpoint
save_checkpoint(epoch, epochs_since_improvement, model, optimizer,
val_loss, best_loss, is_best)
def run():
df_train1 = pd.read_csv(config.TRAIN_PATH_1, usecols=['comment_text', 'toxic']).fillna('none')
df_train2 = pd.read_csv(config.TRAIN_PATH_2, usecols=['comment_text', 'toxic']).fillna('none')
df_train_full = pd.concat([df_train1, df_train2], axis=0).reset_index(drop=True)
df_train_full = df_train_full.sample(frac=1).reset_index(drop=True).head(400000)
df_valid = pd.read_csv(config.VALID_PATH)
tokenizer = config.tokenizer
train_targets = df_train.toxic.values
valid_targets = df_valid.toxic.values
train_dataset = dataset.BERTDatasetTraining(
comment_text=df_train.comment_text.values,
targets=train_targets,
tokenizer=tokenizer,
max_length=config.MAX_LEN
)
train_sampler = torch.utils.data.distributed.DistributedSampler(
train_dataset,
num_replicas=xm.xrt_world_size(),
rank=xm.get_ordinal(),
shuffle=True
)
train_data_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=config.TRAIN_BATCH_SIZE,
sampler=train_sampler,
drop_last=True,
num_workers=4
)
valid_dataset = dataset.BERTDatasetTraining(
comment_text=df_valid.comment_text.values,
targets=valid_targets,
tokeizer=config.tokenizer,
max_length=config.MAX_LEN
)
valid_sampler = torch.utils.data.distributed.DistributedSampler(
valid_dataset,
num_replicas=xm.xrt_world_size(),
rank=xm.get_ordinal(),
shuffle=False
)
valid_data_loader = torch.utils.data.DataLoader(
valid_dataset,
batch_size=config.VALID_BATCH_SIZE,
sampler=valid_sampler,
drop_last=True,
num_workers=4
)
device = xm.xla_device()
model = BERTBaseUncased(bert_path=config.MODEL_PATH).to(device)
param_optimizer = list(model.named_parameters())
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [
{'params':[p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay':0.01},
{'params':[p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay':0.0}
]
lr = 3e-5 * xm.xrt_world_size()
num_train_steps = int(len(train_dataset)/config.TRAIN_BATCH_SIZE/xm.xrt_world_size()*EPOCHS)
xm.master_print(f'num_train_steps={num_train_steps}, world_size={xm.world_size()}')
optimizer = AdamW(optimizer_grouped_parameters, lr=lr)
scheduler = get_constant_schedule_with_warmup(
optimizer,
num_warmup_steps=0,
num_training_steps=num_train_steps
)
for epoch in range(config.EPOCHS):
para_loader = pl.ParallelLoader(train_data_loader, [device])
engine.train_fn(para_loader.per_device_loader(device), model,
optimizer, device, scheduler=scheduler)
para_loader = pl.ParallelLoader(valid_data_loader, [device])
o, t = engine.eval_fn(para_loader.per_device_loader, model, device)
xm.save(model.state_dict(), 'model.bin')
auc = metrics.roc_auc_score(np.array(t) >= 0.5, o)
xm.master_print(f'AUC ={auc}')
elapsed = time.time() - start_time
logging.info(
'| epoch {:3d} | {:5d}/{:5d} batches | lr {:02.2f} | ms/batch {:5.2f} | '
'loss {:5.2f} | ppl {:8.2f}'.format(
epoch, batch,
len(train_data) // args.bptt,
optimizer.param_groups[0]['lr'],
elapsed * 1000 / args.log_interval, cur_loss,
math.exp(cur_loss)))
total_loss = 0
start_time = time.time()
batch += 1
i += seq_len
for k, v in model.named_parameters():
print(k)
print(v)
# Loop over epochs.
lr = args.lr
best_val_loss = []
stored_loss = 100000000
# At any point you can hit Ctrl + C to break out of training early.
try:
if args.continue_train:
optimizer_state = torch.load(os.path.join(args.save, 'optimizer.pt'))
if 't0' in optimizer_state['param_groups'][0]:
optimizer = torch.optim.ASGD(model.parameters(),
lr=args.lr,
t0=0,
def train():
# Turn on training mode which enables dropout.
model.train()
total_loss = 0.
start_time = time.time()
ntokens = len(corpus.dictionary)
hidden = model.init_hidden(args.batch_size)
for batch, i in enumerate(range(0, train_data.size(0) - 1, args.bptt)):
data, targets = get_batch(train_data, i)
# Starting each batch, we detach the hidden state from how it was previously produced.
# If we didn't, the model would try backpropagating all the way to start of the dataset.
hidden = repackage_hidden(hidden)
model.zero_grad()
noises = {}
if args.sharpness_smoothing:
for key, p in model.named_parameters():
if hasattr(model,
'quiet_parameters') and (key
in model.quiet_parameters):
continue
if args.adapt_type == 'weight':
noise = (torch.cuda.FloatTensor(p.size()).uniform_() * 2. -
1.) * args.sharpness_smoothing * torch.abs(p.data)
elif args.adapt_type == 'none':
noise = (torch.cuda.FloatTensor(p.size()).uniform_() * 2. -
1.) * args.sharpness_smoothing
else:
raise ValueError('Unkown --adapt-type')
noises[key] = noise
p.data.add_(noise)
output, hidden = model(data, hidden)
loss = criterion(output.view(-1, ntokens), targets)
loss.backward()
# denoise @ each mini-mini-batch.
if args.sharpness_smoothing:
for key, p in model.named_parameters():
if key in noises:
p.data.sub_(noises[key])
# `clip_grad_norm` helps prevent the exploding gradient problem in RNNs / LSTMs.
torch.nn.utils.clip_grad_norm_(model.parameters(), args.clip)
for p in model.parameters():
p.data.add_(-lr, p.grad.data)
total_loss += loss.item()
if batch % args.log_interval == 0 and batch > 0:
cur_loss = total_loss / args.log_interval
elapsed = time.time() - start_time
print(
'| epoch {:3d} | {:5d}/{:5d} batches | lr {:02.2f} | ms/batch {:5.2f} | '
'loss {:5.2f} | ppl {:8.2f}'.format(
epoch, batch,
len(train_data) // args.bptt, lr,
elapsed * 1000 / args.log_interval, cur_loss,
math.exp(cur_loss)))
total_loss = 0
start_time = time.time()
retraction=opt.retraction,
lr=opt.lr * hvd.size(),
)
print(f'Size of hvd process : {hvd.size()}')
# optimizer = Adam(
# model.parameters(),
# lr=opt.lr * hvd.size(),
# )
lr = opt.lr
# Add Horovod Distributed Optimizer
optimizer = hvd.DistributedOptimizer(
optimizer, named_parameters=model.named_parameters())
_lr_multiplier = 0.1
NoDisplayObj = 66 # Number of entities to display on the graph
# scheduler = MultiStepLR(optimizer, milestones=[opt.burnin]+list(range(int(opt.epochs/10), opt.epochs, int(opt.epochs/10))), gamma=_lr_multiplier)
# scheduler = StepLR(optimizer, step_size=10, gamma=0.9)
# Broadcast parameters from rank 0 to all other processes.
hvd.broadcast_parameters(model.state_dict(), root_rank=0)
loader = th.utils.data.DataLoader(
data,
batch_size=opt.batchsize,
#shuffle=True,
num_workers=opt.ndproc,
val_loss2 = evaluate(val_data)
print('-' * 89)
print('| end of epoch {:3d} | time: {:5.2f}s | valid loss {:5.2f} | '
'valid ppl {:8.2f} | valid bpc {:8.3f}'.format(
epoch, (time.time() - epoch_start_time), val_loss2,
math.exp(val_loss2), val_loss2 / math.log(2)))
print('-' * 89)
print("MAX EPOCH = ", args.epochs + 1)
for epoch in range(args.start, args.epochs + 1):
epoch_start_time = time.time()
train(epoch)
print("TRAIN FINISHED")
if 't0' in optimizer.param_groups[0]:
tmp = {}
for param_name, prm in model.named_parameters():
tmp[param_name] = prm.data.clone()
try:
prm.data = optimizer.state[prm]['ax'].clone()
except:
pass
val_loss2 = evaluate(val_data)
print('-' * 89)
print(
'| end of epoch {:3d} | time: {:5.2f}s | valid loss {:5.2f} | '
'valid ppl {:8.2f} | valid bpc {:8.3f}'.format(
epoch, (time.time() - epoch_start_time), val_loss2,
math.exp(val_loss2), val_loss2 / math.log(2)))
print('-' * 89)
if epoch % 30 == 0:
# Make results dir
out_dir = os.path.join(args.result_dir, args.name)
if not os.path.exists(out_dir):
os.makedirs(out_dir)
###############################################################################
# Build the model
###############################################################################
ntokens = len(corpus.dictionary)
model = model.RNNModel(args.class_type, args.r, args.model, ntokens,
args.emsize, args.nhid, args.nlayers, args.dropout,
args.tied).to(device)
# Print params
for name, param in model.named_parameters():
if param.requires_grad:
print(('Parameter name, shape: ', name, param.data.shape))
criterion = nn.CrossEntropyLoss()
###############################################################################
# Training code
###############################################################################
def repackage_hidden(h):
"""Wraps hidden states in new Tensors, to detach them from their history."""
if isinstance(h, torch.Tensor):
return h.detach()
else:
def train_model(model, trainds, testds, config, device, writer=None):
batch_size = config['data']['batch_size']
status = config['training']['status']
epochs = config['training']['epochs']
balanced_loss = config['loss']['balanced']
# nval = config['nval']
nval_tests = config['nval_tests']
nsave = config['nsave']
model_save = config['model_save']
rank = config['rank']
nranks = config['nranks']
hvd = config['hvd']
num_classes = config['data']['num_classes']
## create samplers for these datasets
train_sampler = torch.utils.data.distributed.DistributedSampler(
trainds, nranks, rank, shuffle=True, drop_last=True)
test_sampler = torch.utils.data.distributed.DistributedSampler(
testds, nranks, rank, shuffle=True, drop_last=True)
## create data loaders
train_loader = torch.utils.data.DataLoader(
trainds,
shuffle=False,
sampler=train_sampler,
num_workers=config['data']['num_parallel_readers'],
batch_size=batch_size,
persistent_workers=True)
test_loader = torch.utils.data.DataLoader(
testds,
shuffle=False,
sampler=test_sampler,
num_workers=config['data']['num_parallel_readers'],
batch_size=batch_size,
persistent_workers=True)
loss_func = loss.get_loss(config)
ave_loss = CalcMean.CalcMean()
acc_func = accuracy.get_accuracy(config)
ave_acc = CalcMean.CalcMean()
opt_func = optimizer.get_optimizer(config)
opt = opt_func(model.parameters(), **config['optimizer']['args'])
lrsched_func = optimizer.get_learning_rate_scheduler(config)
lrsched = lrsched_func(opt, **config['lr_schedule']['args'])
# Add Horovod Distributed Optimizer
if hvd:
opt = hvd.DistributedOptimizer(
opt, named_parameters=model.named_parameters())
# Broadcast parameters from rank 0 to all other processes.
hvd.broadcast_parameters(model.state_dict(), root_rank=0)
model.to(device)
for epoch in range(epochs):
logger.info(' epoch %s of %s', epoch, epochs)
train_sampler.set_epoch(epoch)
test_sampler.set_epoch(epoch)
model.to(device)
for batch_counter, (inputs, targets, class_weights,
nonzero_mask) in enumerate(train_loader):
# move data to device
inputs = inputs.to(device)
targets = targets.to(device)
class_weights = class_weights.to(device)
nonzero_mask = nonzero_mask.to(device)
# zero grads
opt.zero_grad()
outputs, endpoints = model(inputs)
# set the weights
if balanced_loss:
weights = class_weights
nonzero_to_class_scaler = torch.sum(
nonzero_mask.type(torch.float32)) / torch.sum(
class_weights.type(torch.float32))
else:
weights = nonzero_mask
nonzero_to_class_scaler = torch.ones(1, device=device)
loss_value = loss_func(outputs, targets.long())
loss_value = torch.mean(
loss_value * weights) * nonzero_to_class_scaler
# backward calc grads
loss_value.backward()
# apply grads
opt.step()
ave_loss.add_value(float(loss_value.to('cpu')))
# calc acc
ave_acc.add_value(
float(acc_func(outputs, targets, weights).to('cpu')))
# print statistics
if batch_counter % status == 0:
logger.info(
'<[%3d of %3d, %5d of %5d]> train loss: %6.4f acc: %6.4f',
epoch + 1, epochs, batch_counter,
len(trainds) / nranks / batch_size, ave_loss.mean(),
ave_acc.mean())
if writer and rank == 0:
global_batch = epoch * len(
trainds) / nranks / batch_size + batch_counter
writer.add_scalars('loss', {'train': ave_loss.mean()},
global_batch)
writer.add_scalars('accuracy', {'train': ave_acc.mean()},
global_batch)
#writer.add_histogram('input_trans',endpoints['input_trans'].view(-1),global_batch)
ave_loss = CalcMean.CalcMean()
ave_acc = CalcMean.CalcMean()
# release tensors for memory
del inputs, targets, weights, endpoints, loss_value
if config['batch_limiter'] and batch_counter > config[
'batch_limiter']:
logger.info('batch limiter enabled, stop training early')
break
# save at end of epoch
torch.save(model.state_dict(),
model_save + '_%05d.torch_model_state_dict' % epoch)
if nval_tests == -1:
nval_tests = len(testds) / nranks / batch_size
logger.info('epoch %s complete, running validation on %s batches',
epoch, nval_tests)
model.to(device)
# every epoch, evaluate validation data set
with torch.no_grad():
vloss = CalcMean.CalcMean()
vacc = CalcMean.CalcMean()
vious = [CalcMean.CalcMean() for i in range(num_classes)]
for valid_batch_counter, (inputs, targets, class_weights,
nonzero_mask) in enumerate(test_loader):
inputs = inputs.to(device)
targets = targets.to(device)
class_weights = class_weights.to(device)
nonzero_mask = nonzero_mask.to(device)
# set the weights
if balanced_loss:
weights = class_weights
nonzero_to_class_scaler = torch.sum(
nonzero_mask.type(torch.float32)) / torch.sum(
class_weights.type(torch.float32))
else:
weights = nonzero_mask
nonzero_to_class_scaler = torch.ones(1, device=device)
outputs, endpoints = model(inputs)
loss_value = loss_func(outputs, targets.long())
loss_value = torch.mean(
loss_value * weights) * nonzero_to_class_scaler
vloss.add_value(float(loss_value.to('cpu')))
# calc acc
vacc.add_value(
float(acc_func(outputs, targets, weights).to('cpu')))
# calc ious
ious = get_ious(outputs, targets, weights, num_classes)
for i in range(num_classes):
vious[i].add_value(float(ious[i]))
if valid_batch_counter > nval_tests:
break
mean_acc = vacc.mean()
mean_loss = vloss.mean()
# if config['hvd'] is not None:
# mean_acc = config['hvd'].allreduce(torch.tensor([mean_acc]))
# mean_loss = config['hvd'].allreduce(torch.tensor([mean_loss]))
mious = float(
torch.sum(torch.FloatTensor([x.mean()
for x in vious]))) / num_classes
ious_out = {
'jet': vious[0].mean(),
'electron': vious[1].mean(),
'bkgd': vious[2].mean(),
'all': mious
}
# add validation to tensorboard
if writer and rank == 0:
global_batch = epoch * len(
trainds) / nranks / batch_size + batch_counter
writer.add_scalars('loss', {'valid': mean_loss}, global_batch)
writer.add_scalars('accuracy', {'valid': mean_acc},
global_batch)
writer.add_scalars('IoU', ious_out, global_batch)
logger.warning(
'>[%3d of %3d, %5d of %5d]<<< ave valid loss: %6.4f ave valid acc: %6.4f on %s batches >>>',
epoch + 1, epochs, batch_counter,
len(trainds) / nranks / batch_size, mean_loss, mean_acc,
valid_batch_counter + 1)
logger.warning(' >> ious: %s', ious_out)
# update learning rate
lrsched.step()
T.RandomSizedCrop(base_model.input_side),
T.RandomHorizontalFlip(),
normalize
]), download = True)
dataset_eval = opts.dataset(opts.data, train = False, transform = transforms.Compose([
T.Scale(256),
T.CenterCrop(base_model.input_side),
normalize
]), download = True)
adapt_sampler = lambda batch, dataset, sampler, **kwargs: type('', (torch.utils.data.Sampler, ), dict(__len__ = dataset.__len__, __iter__ = lambda _: itertools.chain.from_iterable(sampler(batch, dataset, **kwargs))))()
loader_train = torch.utils.data.DataLoader(dataset_train, sampler = adapt_sampler(opts.batch, dataset_train, opts.sampler), num_workers = opts.threads, batch_size = opts.batch, drop_last = True, pin_memory = True)
loader_eval = torch.utils.data.DataLoader(dataset_eval, shuffle = False, num_workers = opts.threads, batch_size = opts.batch, pin_memory = True)
model = opts.model(base_model, dataset_train.num_training_classes).cuda()
model_weights, model_biases, base_model_weights, base_model_biases = [[p for k, p in model.named_parameters() if p.requires_grad and ('bias' in k) == is_bias and ('base' in k) == is_base] for is_base in [False, True] for is_bias in [False, True]]
base_model_lr_mult = model.optimizer_params.pop('base_model_lr_mult', 1.0)
optimizer = model.optimizer([dict(params = base_model_weights, lr = base_model_lr_mult * model.optimizer_params['lr']), dict(params = base_model_biases, lr = base_model_lr_mult * model.optimizer_params['lr'], weight_decay = 0.0), dict(params = model_biases, weight_decay = 0.0)], **model.optimizer_params)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, **model.lr_scheduler_params)
log = open(opts.log, 'w', 0)
for epoch in range(opts.epochs):
scheduler.step()
model.train()
loss_all, norm_all = [], []
for batch_idx, batch in enumerate(loader_train if model.criterion is not None else []):
tic = time.time()
images, labels = [tensor.cuda() for tensor in batch]
loss = model.criterion(model(images), labels)
loss_all.append(float(loss))
def run():
df1 = pd.read_csv("../data/jigsaw-toxic-comment-train.csv",
usecols=["comment_text", "toxic"])
df2 = pd.read_csv("../data/jigsaw-unintended-bias-train.csv",
usecols=["comment_text", "toxic"])
df_train = pd.concat([df1, df2], axis=0).reset_index(drop=True)
df_valid = pd.read_csv("../data/validation.csv")
train_dataset = dataset.BERTDataset(
comment_text=df_train.comment_text.values,
target=df_train.toxic.values)
train_data_loader = torch.utils.data.DataLoader(
train_dataset, batch_size=config.TRAIN_BATCH_SIZE, num_workers=4)
valid_dataset = dataset.BERTDataset(
comment_text=df_valid.comment_text.values,
target=df_valid.toxic.values)
valid_data_loader = torch.utils.data.DataLoader(
valid_dataset, batch_size=config.VALID_BATCH_SIZE, num_workers=1)
device = torch.device("cuda")
model = BERTBaseUncased()
model.to(device)
param_optimizer = list(model.named_parameters())
no_decay = ["bias", "LayerNorm.bias", "LayerNorm.weight"]
optimizer_parameters = [
{
'params': [
p for n, p in param_optimizer
if not any(nd in n for nd in no_decay)
],
'weight_decay':
0.001
},
{
'params':
[p for n, p in param_optimizer if any(nd in n for nd in no_decay)],
'weight_decay':
0.0
},
]
num_train_steps = int(
len(df_train) / config.TRAIN_BATCH_SIZE * config.EPOCHS)
optimizer = AdamW(optimizer_parameters, lr=3e-5)
scheduler = get_linear_schedule_with_warmup(
optimizer, num_warmup_steps=0, num_training_steps=num_train_steps)
model = nn.DataParallel(model)
best_accuracy = 0
for epoch in range(config.EPOCHS):
engine.train_fn(train_data_loader, model, optimizer, device, scheduler)
outputs, targets = engine.eval_fn(valid_data_loader, model, device)
targets = np.array(targets) >= 0.5
accuracy = metrics.roc_auc_score(targets, outputs)
print(f"AUC Score = {accuracy}")
if accuracy > best_accuracy:
torch.save(model.state_dict(), config.MODEL_PATH)
best_accuracy = accuracy
0., 0., 0., 0., 0., 0., args.n_experts, args.emblocks, args.emdensity,
sparse_mode=args.sparse_mode,
sparse_fract=args.sparse_fract)
if args.cuda:
if not args.multi_gpu:
parallel_model = model.cuda()
else:
parallel_model = nn.DataParallel(model, dim=1).cuda()
else:
parallel_model = model
logging('Args: {}'.format(args))
params_total, params_encoder, params_rnns = 0, 0, 0
for n, p in model.named_parameters():
#print('param {}: {}'.format(n, p.nelement()))
if 'encoder' in n:
params_encoder += p.nelement()
elif 'rnns' in n:
params_rnns += p.nelement()
params_total += p.nelement()
logging('params encoder: {}M'.format(params_encoder / 1.e6))
logging('params rnns: {}M'.format(params_rnns / 1.e6))
logging('params total: {}M'.format(params_total / 1.e6))
log_value('params rnn', params_rnns, 0)
log_value('params encoder', params_encoder, 0)
log_value('params total', params_total, 0)
#write out model
def train(model, trainloader):
if opt.use_cuda:
model = model.cuda()
model.train()
criterion = torch.nn.CrossEntropyLoss()
lr = opt.lr
# optimizer = torch.optim.Adam(model.parameters(), lr, weight_decay=opt.weight_decay)
weight_p = []
bias_p = []
for name, para in model.named_parameters():
if 'bias' in name:
bias_p += [para]
else:
weight_p += [para]
optimizer = torch.optim.Adam([{
'params': weight_p,
'weight_decay': opt.weight_decay
}, {
'params': bias_p,
'weight_decay': 0
}],
lr=lr)
previous_loss = 1e10
pEpoch = []
pLoss = []
for epoch in range(opt.epoch):
loss_all = 0
total_accuracy = 0
for i, (input, target) in enumerate(trainloader):
if opt.use_cuda:
input = input.cuda()
target = target.cuda()
optimizer.zero_grad()
score = model(input)
loss = criterion(score, target)
loss.backward()
optimizer.step()
pred = torch.max(score, 1)[1]
accuracy = float((pred == target).sum())
accuracy = accuracy * 100 / input.size(0)
# print((pred == target).sum(dim=0,keepdim=False))
total_accuracy += accuracy
loss_all += float(loss)
if i % opt.printinter == 0:
print("Epoch: ", epoch, "| Iter:", i, "| Loss:", float(loss),
"| Accuracy:", accuracy, "%")
avgloss = loss_all / len(trainloader)
avgaccuracy = total_accuracy / len(trainloader)
print("the end of Epoch: ", epoch, "| AVGLoss:", avgloss,
"| Accuracy:", avgaccuracy, "%")
save(model, epoch)
# plot
pEpoch.append(epoch)
pLoss.append(avgloss)
plotlc(pEpoch, pLoss)
def train():
# Turn on training mode which enables dropout.
if args.model == 'QRNN': model.reset()
total_loss = 0
start_time = time.time()
hidden = model.init_hidden(args.batch_size)
batch, i = 0, 0
while i < train_data.size(1) - 1 - 1:
# seq_len=args.bptt
lr2 = optimizer.param_groups[0]['lr']
# optimizer.param_groups[0]['lr'] = lr2 * seq_len / args.bptt
model.train()
data, targets = get_batchlz(train_data, train_label, i,
args.batch_size)
hidden = repackage_hidden(hidden)
optimizer.zero_grad()
'''训练步'''
output, hidden, rnn_hs, dropped_rnn_hs = model(data,
None,
return_h=True)
raw_loss = criterion(output, targets)
wri.add_scalar('raw_loss', raw_loss, epoch * lzarg + batch)
loss = raw_loss
# Activiation Regularization
if args.alpha:
loss = loss + sum(args.alpha * dropped_rnn_h.pow(2).mean()
for dropped_rnn_h in dropped_rnn_hs[-1:])
wri.add_scalar('alpha_loss', loss - raw_loss, epoch * lzarg + batch)
# Temporal Activation Regularization (slowness)
if args.beta:
loss = loss + sum(args.beta *
(rnn_h[1:] - rnn_h[:-1]).pow(2).mean()
for rnn_h in rnn_hs[-1:])
wri.add_scalar('alphabeta_loss', loss - raw_loss,
epoch * lzarg + batch)
loss.backward()
# wri.add_scalar('loss',loss,epoch*179+batch)
# `clip_grad_norm` helps prevent the exploding gradient problem in RNNs / LSTMs.
if args.clip: torch.nn.utils.clip_grad_norm_(params, args.clip)
optimizer.step()
total_loss += raw_loss.data
optimizer.param_groups[0]['lr'] = lr2
if batch % args.log_interval == 0 and batch > 0:
cur_loss = total_loss.item(
) / args.log_interval #log_interval平均loss
wri.add_scalar(
'bat_curraw_loss', cur_loss,
epoch * (lzarg // args.log_interval) +
batch / args.log_interval)
elapsed = time.time() - start_time
pred_y = torch.max(output, 1)[1].data
accuracy = (pred_y == targets).float().sum() / len(targets)
wri.add_scalar(
'train_accuracy', accuracy,
epoch * (lzarg // args.log_interval) +
batch / args.log_interval)
print(
'ACC={:3.2f} | epoch {:3d} | {:5d}/{:5d} batches | lr {:05.5f} | ms/batch {:5.2f} | '
'平均loss {:5.2f} | ppl {:5.2f} | bpc {:5.3f}'.format(
accuracy, epoch, batch, lzarg,
optimizer.param_groups[0]['lr'],
elapsed * 1000 / args.log_interval, cur_loss,
math.exp(cur_loss), cur_loss / math.log(2)), '\n')
total_loss = 0
start_time = time.time()
###
batch += 1
i += args.batch_size
'''# 保存梯度数据来可视化'''
if epoch % 10 == 0 and epoch != 0:
# targets_image=image[targets]
# wri.add_embedding(rnn_hs[0][-1,:,:].clone().cpu().data.numpy(),metadata=targets,label_img=targets_image.data,global_step=epoch/10)
for name, param in model.named_parameters():
wri.add_histogram(name,
param.clone().cpu().data.numpy(), epoch / 10)
test_batch_size = 1
train_data = batchify(corpus.train, args.batch_size, args)
val_data = batchify(corpus.valid, eval_batch_size, args)
test_data = batchify(corpus.test, test_batch_size, args)
###############################################################################
# Build the model
###############################################################################
criterion = None
ntokens = len(corpus.dictionary)
model = model.RNNModel(args.model, ntokens, args.emsize, args.nhid, args.chunk_size, args.nlayers,
args.dropout, args.dropouth, args.dropouti, args.dropoute, args.wdrop, args.tied)
for name, weight in model.named_parameters():
print(name, weight.numel())
###
if args.resume:
print('Resuming model ...')
model_load(args.resume)
optimizer.param_groups[0]['lr'] = args.lr
model.dropouti, model.dropouth, model.dropout, args.dropoute = args.dropouti, args.dropouth, args.dropout, args.dropoute
if args.wdrop:
for rnn in model.rnn.cells:
rnn.hh.dropout = args.wdrop
###
if not criterion:
splits = []
if ntokens > 500000:
# One Billion
