What optimizer is used in the model, and what are the values set for the learning rate, decay rate, and decay step?
The optimizer used in the Cryptocurrency-predicting RNN Model is the Adam optimizer. The Adam optimizer is a popular choice for training deep neural networks due to its adaptive learning rate and momentum-based approach. It combines the benefits of two other optimization algorithms, namely AdaGrad and RMSProp, to provide efficient and effective optimization.
The learning rate is a hyperparameter that determines the step size at which the optimizer updates the model's parameters during training. In the Cryptocurrency-predicting RNN Model, the learning rate is set to 0.001. This value was chosen based on empirical experimentation and fine-tuning to achieve good convergence and performance.
The decay rate and decay step refer to the parameters used for learning rate decay. Learning rate decay is a technique used to gradually reduce the learning rate during training to improve convergence and prevent overshooting. In the Cryptocurrency-predicting RNN Model, a decay rate of 0.5 and a decay step of 10000 are used.
The decay rate of 0.5 means that the learning rate will be multiplied by 0.5 at each decay step. This gradual reduction allows the optimizer to make larger updates initially and smaller updates as training progresses, which can help the model converge to a better solution.
The decay step of 10000 indicates that the learning rate will be decayed every 10000 training steps. This value was chosen based on the characteristics of the dataset and the desired training behavior. By decaying the learning rate at regular intervals, the model can adapt to changing dynamics in the data and potentially avoid getting stuck in local minima.
To illustrate the effect of learning rate decay, consider the following example. Suppose the initial learning rate is 0.001 and the decay rate is 0.5 with a decay step of 10000. After 10000 training steps, the learning rate will be reduced to 0.001 * 0.5 = 0.0005. After another 10000 steps, it will be further reduced to 0.0005 * 0.5 = 0.00025, and so on.
The Cryptocurrency-predicting RNN Model uses the Adam optimizer with a learning rate of 0.001, a decay rate of 0.5, and a decay step of 10000. These values were chosen based on experimentation and fine-tuning to achieve optimal convergence and performance.
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