Hi,

When training my model, at the end of each epoch I check the accuracy on the validation set. To do this I use model.eval() and then set it to model.train() after checking the validation set. This leads to an accuracy of around 90%. However when I run my model without checking the validation set until after the whole training is complete, the accuracy becomes 80%. This could be a random shift in the accuracy by the end, but I was wondering if I am doing something wrong. The code is roughly below. Check accuracy just feeds the dataloader through the model and compares the outputs to calc the accuracy

```
for e in range(epochs):
for t, x in enumerate(my_dataloader):
x_arr = np.array(x)
x1 = x_arr[:,0]
x1 = Batch.from_data_list(x1).to(device)
x2 = torch.stack(x_arr[:,2].tolist(), dim=0).to(device=device, dtype=torch.long)
x3 = x_arr[:, 3]
x3 = Batch.from_data_list(x3).to(device)
model.train() # put model to training mode
# x = x.to(device=device) # move to device, e.g. GPU
outputs = model(x1,x3,x2)
# maxes = outputs.max(1)[0].reshape(-1,1)
# print(len(maxes))
loss = nn.CrossEntropyLoss(weight=weight)(outputs, x1.y)
optimizer.zero_grad()
loss.backward()
# Update the parameters of the model using the gradients
optimizer.step()
if t % print_every == 0:
print('Epoch: %d, Iteration %d, loss = %.4f' % (e, t, loss.item()))
print()
model.eval()
with torch.no_grad():
my_dataloader = DataListLoader(X_test, batch_size=128)
acc, y_pred, y_true = check_accuracy(my_dataloader, model)
accs_test.append(acc)
model.train()
```