Adagrad optimizer
optim_adagrad( params, lr = 0.01, lr_decay = 0, weight_decay = 0, initial_accumulator_value = 0, eps = 1e-10 )
params |
(iterable): list of parameters to optimize or list parameter groups |
lr |
(float, optional): learning rate (default: 1e-2) |
lr_decay |
(float, optional): learning rate decay (default: 0) |
weight_decay |
(float, optional): weight decay (L2 penalty) (default: 0) |
initial_accumulator_value |
the initial value for the accumulator. (default: 0) Adagrad is an especially good optimizer for sparse data. It individually modifies learning rate for every single parameter, dividing the original learning rate value by sum of the squares of the gradients. It causes that the rarely occurring features get greater learning rates. The main downside of this method is the fact that learning rate may be getting small too fast, so that at some point a model cannot learn anymore. |
eps |
(float, optional): term added to the denominator to improve numerical stability (default: 1e-10) |
Update rule:
θ_{t+1} = θ_{t} - \frac{η }{√{G_{t} + ε}} \odot g_{t}
The equation above and some remarks quoted after An overview of gradient descent optimization algorithms by Sebastian Ruder.
Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.