How to use base optimizer using TF learn

This recipe helps you use base optimizer using TF learn

Recipe Objective

This recipe explains how to use base optimizer using TFLayer.

Base Optimizer class

Its syntax is tflearn.optimizers.Optimizer (learning_rate, use_locking, name)
where its arguments learning_rate which is learning rate, use_locking which if true uses locks for update operation and name which is the optimizer name.
In the below code we have specified decay, learning rate, and an optimizer with non-default parameters while a step tensor and the optimizer will be built by themselves by TFLearn estimators.

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class optimizer(Object):

    def __init__(self, lr, ul, N):
       self.N = N
       self.lr = lr
       self.ul = ul
       self.Built = False
       self.Tensor = None
       self.has_decay = False

    def build(self, st=None):
       raise NotImplementedError

    def __call__(self):
       if not self.Built:
          self.build()
       return self.Tensor

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