Implement an RMSProp Optimizer with Exponential Decay

[liudong16]

Proposal

In the previous tensorflow code, the RMSProp Optimizer is often used with a constant Learning Rate, such as:
rmsp = tflearn.RMSProp(learning_rate=0.01, decay=0.999)
net = tflearn.regression(net, optimizer=rmsp, loss='categorical_crossentropy')
In my proposal, I try to use exponential decay before using RMSProp OPtimizer, i.e., append the following codes:
self.learning_rate = tf.train.exponential_decay(self.learning_rate, self.decay, self.momentum, self.epsilon, self.use_locking, self.name)
tf.add_to_collection(tf.GraphKeys.LR_VARIABLES, self.learning_rate)

Effects

Based on the experiment results, I find that the modifications could promote the accuracy of classifications to some extent. For instance, I use the new optimizer in the work of image classification on cifar-10 data set with ResNet 20. The training performs 10 rounds, each taking 50 epochs with the same parameter settings. According to the results, the accuracy of the previous RMSProp Optimizer on average is 80.05% while the new one is 82.46%.

[1292765944]

I'm interested in your proposal. Could you give some insight behind this design or example code? Thanks!