PolynomialLR
class torch.optim.lr_scheduler.PolynomialLR(optimizer, total_iters=5, power=1.0, last_epoch=- 1, verbose=False)
Decays the learning rate of each parameter group using a polynomial
   function in the given total_iters. When last_epoch=-1, sets initial
   lr as lr.
Parameters:
      * optimizer (Optimizer) -- Wrapped optimizer.
  * **total_iters** (*int*) -- The number of steps that the
    scheduler decays the learning rate. Default: 5.

  * **power** (*int*) -- The power of the polynomial. Default:
    1.0.

  * **verbose** (*bool*) -- If "True", prints a message to stdout
    for each update. Default: "False".

-[ Example ]-



Assuming optimizer uses lr = 0.001 for all groups
lr = 0.001     if epoch == 0
lr = 0.00075   if epoch == 1
lr = 0.00050   if epoch == 2
lr = 0.00025   if epoch == 3
lr = 0.0       if epoch >= 4
scheduler = PolynomialLR(self.opt, total_iters=4, power=1.0)


