Any help please
def training_model(model,model_type,train_dataloader,validation_dataloader,epochs):
training_loss = []
validation_loss = []
validation_acc = []
optimizer = AdamW(model.parameters(), lr=1e-3, correct_bias=True )
total_steps = len(train_dataloader) * epochs
warmup_steps = 0
scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps = warmup_steps,num_training_steps = total_steps)
start_time = time.time()
for epoch in range(epochs):
total_train_loss = 0
model.train()
print('Epoch: {}'.format(epoch+1))
for step, batch in enumerate(Bar(train_dataloader)):
b_input_ids = batch[0].to(device)
b_input_mask = batch[1].to(device)
b_labels = batch[2].to(device)
model.zero_grad()
loss, logits = model(b_input_ids,token_type_ids=None, attention_mask=b_input_mask,labels=b_labels) #it computes loss function so it takes labels
total_train_loss += loss.item()
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
optimizer.step()
scheduler.step()
avg_train_loss = total_train_loss / len(train_dataloader)
model.eval()
save_model(model,epoch+1,model_type)
total_eval_accuracy = 0
total_eval_loss = 0
nb_eval_steps = 0
print("Validation")
for batch in validation_dataloader:
b_input_ids = batch[0].to(device)
b_input_mask = batch[1].to(device)
b_labels = batch[2].to(device)
with torch.no_grad():
(loss, logits) = model(b_input_ids,token_type_ids=None,attention_mask=b_input_mask,labels=b_labels)
total_eval_loss += loss.item()
logits = logits.detach().cpu().numpy()
label_ids = b_labels.to('cpu').numpy()
avg_val_loss = total_eval_loss / len(validation_dataloader)
print("Validation Loss :", avg_val_loss )
training_loss.append(avg_train_loss)
validation_loss.append(avg_val_loss)
elapsed_time = time.time() - start_time
print("Done")
print("Total training took :",str(datetime.timedelta(seconds=int(round((elapsed_time))))))
return model,training_loss,validation_loss
train_dataloader ,validation_dataloader = prepare_data(dataset_cbow,0.99,64) #dataset_cbow
model_embed,training_loss_embed,validation_loss_embed = training_model(model_embed,"embed",train_dataloader,validation_dataloader,epochs=5)
AttributeError Traceback (most recent call last)
<ipython-input-43-2fd6dc3ee641> in <module>()
----> 1 model_embed,training_loss_embed,validation_loss_embed = training_model(model_embed,"embed",train_dataloader,validation_dataloader,epochs=5)
<ipython-input-42-f3427a8de88a> in training_model(model, model_type, train_dataloader, validation_dataloader, epochs)
3 validation_loss = []
4 validation_acc = []
----> 5 optimizer = torch.optim.AdamW(model.parameters(), lr=1e-3, correct_bias=True )
6 total_steps = len(train_dataloader) * epochs
7 warmup_steps = 0
AttributeError: 'str' object has no attribute 'parameters'
