def evaluate(model, data, loss_function, word_to_ix, name='dev'):
model.eval()
avg_loss = 0.0
truth_res = []
pred_res = []
#print(data)
#for sent, label in data:
for sentwords, dgr in data:
dgr = dataLoaderRegresser.prepare_degree(dgr)
#truth_res.append(label_to_ix[label])
truth_res.append(dgr)
#detaching it from its history on the last instance
model.hidden = model.init_hidden()
sent = dataLoaderRegresser.prepare_sequence(sentwords, word_to_ix)
#label = dataLoader.prepare_label(label, label_to_ix)
pred = model(sent)
#pred_label = pred.data.max(1)[1].numpy()
#pred_res.append(pred_label)
print('->gold-degree %.4f, predicted-degree %.4f %s' %
(dgr.item(), pred.item(), sentwords))
pred_res.append(pred)
#model.zero_grad() # Note that we don't need to keep this when evaluating the model
#loss = loss_function(pred, label)
loss = loss_function(pred, dgr)
#avg_loss += loss.data[0]
avg_loss += loss.item()
avg_loss /= len(data)
#acc = get_accuracy(truth_res, pred_res)
#print(name + ' avg_loss: %g train acc: %g' % (avg_loss, acc))
print(name + ' avg_loss: %g' % avg_loss)
return avg_loss, pred_res
def train_epoch(model, train_data, loss_function, optimizer, word_to_ix, i):
model.train()
# https://stackoverflow.com/questions/51433378/what-does-model-train-do-in-pytorch
# model.train() tells your model that you are training the model.
# So effectively layers like dropout, batchnorm etc. which behave different on the train and test procedure know what is going on and hence can behave accordingly.
# You can call either model.eval() or model.train(mode=False) to tell that you are testing.
avg_loss = 0.0
count = 0
truth_res = []
pred_res = []
#for sent, label in train_data:
for sentwords, dgr in train_data:
dgr = dataLoaderRegresser.prepare_degree(dgr)
#truth_res.append(label_to_ix[label])
truth_res.append(dgr)
#detaching it from its history on the last instance
model.hidden = model.init_hidden()
sent = dataLoaderRegresser.prepare_sequence(sentwords, word_to_ix)
#label = dataLoader.prepare_label(label, label_to_ix)
pred = model(sent)
print('->gold-degree %.4f, predicted-degree %.4f %s' %
(dgr.item(), pred.item(), sentwords))
#pred_label = pred.data.max(1)[1].numpy()
#pred_res.append(pred_label)
#print('->pred:', pred)
#print('->dgr:', dgr)
pred_res.append(pred)
model.zero_grad(
) # set gradients of all model parameters to zero; same as optimizer.zero_grad()
# https://discuss.pytorch.org/t/model-zero-grad-or-optimizer-zero-grad/28426/2
# model.zero_grad() vs optimizer.zero_grad()
# if optimizer = optim.SGD(model.parameters()), model.zero_grad() and optimizer.zero_grad are the same.
# They are still the same whether the optimizer is SGD, Adam, RMSProp etc.
#loss = loss_function(pred, label)
loss = loss_function(pred, dgr)
#print('->loss', loss.item())
#avg_loss += loss.data[0]
avg_loss += loss.item(
) # https://github.com/pytorch/pytorch/issues/6061
count += 1
#if count % 500 == 0: #print out every 500 sentences
if count % 50 == 0: #print out every 50 sentences
#print('epoch: %d iterations: %d loss: %g' % (i, count, loss.data[0]))
print('epoch: %d iterations: %d loss: %g' %
(i, count, loss.item()))
loss.backward(
) # Calling .backward() multiple times accumulates the gradient (by addition) for each parameter.
# This is why you should call optimizer.zero_grad() after each .step() call.
# Note that following the thirst .backward call, a second call is only possible after you have performed another forward pass.
optimizer.step(
) # It performs a parameter update based on the current gradient (stored in .grad attribute of a parameter) and the update rule.
avg_loss /= len(train_data)
print('epoch: %d done!\ntrain avg_loss: %g' % (i, avg_loss))