trying to calculate the roc_score per epoch. my idea is to combine all the batch predictions then calculate but i keep getting error. how do you approach this. i am predicting between 0 and 1

```
train_losses = []
valid_losses = []
for epoch in range(1, num_epochs + 1):
y_true = []
y_pred = []
train_loss = 0.0
valid_loss = 0.0
model.train()
for data, target in train_loader:
data = data.to(device)
target = target.to(device)
optimizer.zero_grad()
output = model(data)
target = target.unsqueeze(1).type_as(output)
loss = criterion(output, target)
loss.backward()
optimizer.step()
train_loss += loss.item() * data.size(0)
model.eval()
with torch.no_grad():
for data, target in valid_loader:
data = data.to(device)
target = target.to(device)
target = target.unsqueeze(1).type_as(output)
output = model(data)
loss = criterion(output, target)
valid_loss += loss.item() * data.size(0)
y_true.append(target.data.cpu().numpy())
y_pred.append(output.data.cpu().numpy())
roc_score = roc_auc_score(y_true,y_pred)
acc = (PREDS == TARGETS).mean() * 100.
# calculate-average-losses
train_loss = train_loss/len(train_loader.sampler)
valid_loss = valid_loss/len(valid_loader.sampler)
train_losses.append(train_loss)
valid_losses.append(valid_loss)
# print-training/validation-statistics
print('Epoch: {} \tTraining Loss: {:.6f} \tValidation Loss: {:.6f} \tValidation ROCSCORE: {:.6f}'.format(epoch, train_loss, valid_loss,roc_score))
```

error

```
ValueError Traceback (most recent call last)
<ipython-input-69-a1c7781e11ef> in <module>()
29 y_true.append(target.data.cpu().numpy())
30 y_pred.append(output.data.cpu().numpy())
---> 31 roc_score = roc_auc_score(y_true,y_pred)
32 acc = (PREDS == TARGETS).mean() * 100.
33 # calculate-average-losses
1 frames
/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_ranking.py in roc_auc_score(y_true, y_score, average, sample_weight, max_fpr, multi_class, labels)
366
367 y_type = type_of_target(y_true)
--> 368 y_true = check_array(y_true, ensure_2d=False, dtype=None)
369 y_score = check_array(y_score, ensure_2d=False)
370
/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py in check_array(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)
572 if not allow_nd and array.ndim >= 3:
573 raise ValueError("Found array with dim %d. %s expected <= 2."
--> 574 % (array.ndim, estimator_name))
575
576 if force_all_finite:
ValueError: Found array with dim 3. Estimator expected <= 2.
```