I intend during the train compute the loss using MSELoss but using max error as reference for stoping to train and to compute the error.

Note that I set reduce=False, so the loss_func will returns a loss per input/target element. How I can use an optimizer that I can use max error to compute?

Error:

```
/Envs/climaenv/lib/python3.6/site-packages/torch/optim/lbfgs.py in step(self, closure)
102 # evaluate initial f(x) and df/dx
103 orig_loss = closure()
--> 104 loss = float(orig_loss)
105 current_evals = 1
106 state['func_evals'] += 1
ValueError: only one element tensors can be converted to Python scalars
```

Here is the code:

```
def fit(self, x=None, y=None, lr=0.001, epochs=1000):
'''
Training function
Argurmets:
x: features train set
y: target train set
lr: Learning rate
epochs: number of epochs
'''
optimizer = torch.optim.LBFGS([{'params': [self.hidden.weight,self.predict.weight]}],
lr=lr,max_iter=20)
loss_func = torch.nn.MSELoss(reduce=False)
def closure():
optimizer.zero_grad() # clear gradients for next train
prediction = self(x) # input x and predict based on x
loss = loss_func(prediction, y)
loss.max().backward() # backpropagation, compute gradients
return loss
for t in range(epochs):
optimizer.step(closure) # apply gradient
return self.loss_epochs
```