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Gaurav Baruah
2017-03-28 12:33:25 -04:00
parent 7b20f50800 46d913392b
commit 73ed76951c
2 changed files with 12 additions and 18 deletions
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sm-model/model.py
+3 -3
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@@ -59,7 +59,7 @@ class QAModel(nn.Module):
q = self.conv_q.forward(question)
q = F.max_pool1d(q, q.size()[2])
q = q.view(-1, self.conv_channels)
logger.debug('forward q: {}'.format(q))
# logger.debug('forward q: {}'.format(q))
a = self.conv_a.forward(answer)
a = F.max_pool1d(a, a.size()[2])
@@ -68,10 +68,10 @@ class QAModel(nn.Module):
x = None
if self.no_ext_feats:
x = torch.cat([q, a], 1)
logger.debug('no_ext_feats')
# logger.debug('no_ext_feats')
else:
x = torch.cat([q, a, ext_feats], 1)
logger.debug('with ext_feats')
# logger.debug('with ext_feats')
logger.debug('featvec x: {}'.format(x))
# logger.debug(x.creator)
sm-model/train.py
+9 -15
View File
@@ -58,11 +58,8 @@ class Trainer(object):
self.optimizer.zero_grad()
output = self.model(xq, xa, ext_feats)
# output = torch.exp(output)
loss = self.criterion(output, ys)
logger.debug('loss after criterion {}'.format(loss))
# logger.debug('loss after criterion {}'.format(loss))
# NOTE: regularizing location 1
# if not self.no_loss_reg:
@@ -71,20 +68,20 @@ class Trainer(object):
loss.backward()
logger.debug('AFTER backward')
# logger.debug('AFTER backward')
#logger.debug('params {}'.format([p for p in self.model.parameters()]))
logger.debug('params grads {}'.format([p.grad for p in self.model.parameters()]))
# logger.debug('params grads {}'.format([p.grad for p in self.model.parameters()]))
# NOTE: regularizing location 2. It would seem that location 1 is correct?
if not self.no_loss_reg:
loss = self.regularize_loss(loss)
logger.debug('loss after regularizing {}'.format(loss))
# logger.debug('loss after regularizing {}'.format(loss))
self.optimizer.step()
logger.debug('AFTER step')
# logger.debug('AFTER step')
#logger.debug('params {}'.format([p for p in self.model.parameters()]))
logger.debug('params grads {}'.format([p.grad for p in self.model.parameters()]))
# logger.debug('params grads {}'.format([p.grad for p in self.model.parameters()]))
return loss.data[0], self.pred_equals_y(output, ys)
@@ -134,17 +131,15 @@ class Trainer(object):
xq, xa, x_ext_feats = batch_inputs[0]
y = batch_labels[0]
pred = self.model(xq, xa, x_ext_feats)
pred = self.model(xq, xa, x_ext_feats)
loss = self.criterion(pred, y)
pred = torch.exp(pred)
total_loss += loss
total_correct += self.pred_equals_y(pred, y)
p_score, p_class = pred.max(1)
y_pred[ypc] = p_score.data.squeeze()[0]
y_pred[ypc] = pred.data.squeeze()[1] # we want to score for relevance, NOT the predicted class
ypc += 1
logger.info('{}_correct {}'.format(set_folder, total_correct))
logger.info('{}_loss {}'.format(set_folder, total_loss.data[0]))
logger.info('{} total {}'.format(set_folder, len(labels)))
@@ -229,8 +224,7 @@ class Trainer(object):
tensorized_inputs = []
for i in xrange(len(batch_ques)):
xq = Variable(self.make_input_matrix(batch_ques[i], word_vectors, vec_dim) ) #, requires_grad=False)
xs = Variable(self.make_input_matrix(batch_sents[i], word_vectors, vec_dim) ) #, requires_grad=False)
# ext_feats = Variable(torch.FloatTensor(batch_ext_feats[i]))
xs = Variable(self.make_input_matrix(batch_sents[i], word_vectors, vec_dim) ) #, requires_grad=False)
ext_feats = Variable(torch.FloatTensor(batch_ext_feats[i]))
ext_feats =torch.unsqueeze(ext_feats, 0)
y[i] = batch_labels[i]