Problem training inside a loop

I have a feed forward NN that I want to train several times and the get the best model.
For that I am doing something like:

#read data and store in Variable.

# Define model and train it.
for n in times_to_train:
     model = define_model()

My problem is that the performance is quite different when I use this loop and when I train one model a time (instead of using the for loop, execute the code several times).
My question is. Should I clear any variable or restart something before training inside the loop?

Thank you very much!

What have you defined in train()?
If you are creating a new model, you should also create a new optimizer.

I don’t understand the issue completely.
If you use the for loop you’ll get several different models.
Now if you unroll the loop and train several models, you get a completely different result?

model1 = define_model()

model2 = define_model()

I have just check what you suggest and used two data sets for comparing. The problems I see about very different training and validation errors appear just for one of the sets, the one with lower data points so I guess the problem is with my data points.

Thank you very much for the answer.

I also post the code of train() in case there is also something wrong.

def train(x_train, y_train, x_val, y_val, model, max_it):
      loss = torch.nn.MSELoss(size_average=True)
      opt = torch.optim.Rprop(model.parameters(), lr=0.5)

      for epoch in xrange(max_it):
           tr_loss = train_epoch(model, x_train, y_train, loss, opt)
           v_loss = val_epoch(model, x_val, y_val, loss)
          # some clauses for exiting if overfitting or convergency reached.

def  train_epoch(model, x, y, loss, opt):
      y_pred = model(x)
      loss_tr = loss(y_pred, y)

def  val_epoch(model, x, y_, loss):
      y_pred = model(x)
      loss_val = loss(y_pred, y)