I am running into some issues about net.eval() inconsistency in PyTorch. I’ve trained a model which has batchnorm and dropout, then I’ve evaluated model performance on validation set but every time I am getting a different result. I was wondering if anyone else faced a similar problem. I shared my problem here as well https://discuss.pytorch.org/t/model-eval-gives-incorrect-loss-for-model-with-batchnorm-layers/7561/3.

But hope and believe you guys can help me better understand my problem.

```
def evaluate(net, valid_dl):
val_true = []
val_pred = []
for i, data in enumerate(valid_dl, 0):
#get the inputs
inputs, labels = data
#wrap them in Variable
inputs, labels = V(inputs.float()), V(labels)
outputs = net(inputs)
#get cross entropy loss and accuracy
val_true.append(labels.data)
val_pred.append(outputs.data)
#define validation variables
val_pred_concat = V(torch.cat(val_pred))
val_true_concat = V(torch.cat(val_true))
#validation cross entropy loss
val_loss = criterion(val_pred_concat, val_true_concat).data[0]
#validation accuracy
_, class_pred = torch.max(val_pred_concat, 1)
val_acc = sum((class_pred == val_true_concat).data) / len(class_pred)
#confusion matrix
cmat = confusion_matrix(val_true_concat.data.numpy(), class_pred.data.numpy())
return val_loss, val_acc, cmat
```

**Without Batchnorm and Dropoout**

**Edit**

Ok it’s solved, a silly mistake. For others who may do the same thing, is that my dropout training argument was hard-coded.

**Bad Mistake**

x = F.dropout(x, p=0.5, training=True)

**Correct way**

x = F.dropout(x, p=0.5, training=self.training)

Thanks !