hell yeah last one

cornac/models/vbpr/recom_vbpr.py

@@ -154,7 +154,7 @@ def fit(self, train_set, val_set=None):
  raise CornacException("item_image modality is required but None.")
 
  # Item visual feature from CNN
- train_features = train_set.item_image.features[: self.train_set.total_items]
+ train_features = train_set.item_image.features[: train_set.total_items]
  train_features = train_features.astype(np.float32)
  self._init(
  n_users=train_set.total_users,
@@ -163,11 +163,11 @@ def fit(self, train_set, val_set=None):
  )
 
  if self.trainable:
- self._fit_torch(train_features)
+ self._fit_torch(train_set, train_features)
 
  return self
 
- def _fit_torch(self, train_features):
+ def _fit_torch(self, train_set, train_features):
  import torch
 
  def _l2_loss(*tensors):
@@ -213,11 +213,11 @@ def _inner(a, b):
  sum_loss = 0.0
  count = 0
  progress_bar = tqdm(
- total=self.train_set.num_batches(self.batch_size),
+ total=train_set.num_batches(self.batch_size),
  desc="Epoch {}/{}".format(epoch, self.n_epochs),
  disable=not self.verbose,
  )
- for batch_u, batch_i, batch_j in self.train_set.uij_iter(
+ for batch_u, batch_i, batch_j in train_set.uij_iter(
  self.batch_size, shuffle=True
  ):
  gamma_u = Gu[batch_u]