RMSProp Optimization Algorithm Explained In PyTorch
RMSProp explained: automatically adapts learning rate to parameters, uses 8 arguments for initialization and step() updates parameters. Example usage with PyTorch's RMSprop optimizer.
Buy Me a Coffee☕ *Memos: My post explains RMSProp. My post explains Module(). RMSProp() can do gradient descent by automatically adapting learning rate to parameters as shown below: *Memos: The 1st argument for initialization is params(Required-Type:generator). The 2nd argument for initialization is lr(Optional-Default:0.01-Type:int or float). *It must be 0 <= x. The 3rd argument for initialization is alpha(Optional-Default:0.99-Type:int or float). *It must be 0 <= x. The 4th argument for initialization is eps(Optional-Default:1e-08-Type:int or float). *It must be 0 <= x. The 5th argument...