How to use AdaDelta optimizer using TF learn

This recipe helps you use AdaDelta optimizer using TF learn

Recipe Objective

This recipe explains what is AdaDelta in TFLayer.

AdaDelta

Its syntax is tflearn.optimizers.AdaDelta (learning_rate=0.001, rho=0.1, epsilon=1e-08, use_locking=False, name='AdaDelta')
where its arguments learning_rate which is learning rate, use_locking which if true uses locks for update operation, name which is the optional name prefix for the operations, epsilon which is a small value to avoid zero as denominator ,and rho which is the decay rate.

class AdaDelta(optimizer):
    def __init__(self, lr=0.001, r=0.1, e=1e-08, ul=False, N="AdaDelta"):
       super(AdaDelta, self).__init__(lr, ul, N)
       self.r = r
       self.e = e

    def build(self, st=None):
       self.Built = True
       self.Tensor = tf.train.AdadeltaOptimizer(self.lr, r=self.r, e=self.e, ul=self.ul, N=self.N)
adadelta = AdaDelta

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