From the course: Deep Learning with Python: Optimizing Deep Learning Models

Root Mean Square Propagation (RMSProp) - Python Tutorial

From the course: Deep Learning with Python: Optimizing Deep Learning Models

Root Mean Square Propagation (RMSProp)

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- [Instructor] RMSProp, which stands for root mean squared propagation, was developed to address the diminishing learning rate problem observed in AdaGrad. It modifies AdaGrad by introducing an exponential decay average, or moving average, of the squared gradients. Instead of accumulating all past squared gradients, RMSProp keeps a running average that decays over time. This allows the algorithm to forget older gradients and focus on more recent ones. One of the significant benefits of RMSProp is its ability to maintain adaptive learning rates without the issue of the learning rates decaying too quickly. By using an exponential moving average, RMSProp ensures that the accumulated squared gradients do not grow indefinitely, preventing the learning rates from becoming too small. RMSProp is particularly effective in training models on non-stationary objectives, where the underlying data distribution changes over time. It also handles noisy and sparse gradients well, making it suitable for training recurrent neural networks and other complex architectures. Furthermore, RMSProp is relatively easy to implement. It builds upon AdaGrad by adding a simple modification to the way the squared gradients are accumulated. This makes it a practical choice for those looking to improve upon AdaGrad's limitations without introducing significant complexity. Despite its advantages, RMSProp introduces additional hyperparameters, such as the decay rate, which need to be carefully tuned. The performance of RMSProp can be sensitive to the choice of these hyperparameters, and improper tuning can lead to suboptimal results or convergence issues. In some cases, RMSProp may not converge or may converge to a suboptimal solution, especially if the hyperparameters are not well chosen. This can be a challenge for practitioners who may not have the time or resources to perform extensive hyperparameter optimization. Moreover, RMSProp lacks a strong theoretical foundation compared to some other optimizers. This can make it harder to predict its behavior in certain scenarios and may pose challenges when trying to understand or debug the training process.

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