How to use AdaGrad optimizer using TF learn

This recipe helps you use AdaGrad optimizer using TF learn

Recipe Objective

This recipe explains what is AdaGrad in TFLayer.

Build Expedia Hotel Recommendation System using Machine Learning

AdaGrad

Its syntax is tflearn.optimizers.AdaGrad (learning_rate=0.001, initial_accumulator_value=0.1, use_locking=False, name='AdaGrad')
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, and initial_accumulator_value which is starting value for the accumulators.

class AdaGrad(optimizer):
    def __init__(self, lr=0.001, iav=0.1, ul=False, N="AdaGrad"):
       super(AdaGrad, self).__init__(lr, ul, N)
       self.iav = iav

    def build(self, st=None):
       self.Built = True
       self.Tensor = tf.train.AdagradOptimizer(self.lr, iav=self.iav, ul=self.ul, N=self.N)
adagrad = AdaGrad

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