Anyone has tried to implement the EASGD Algorithm in MXNet, I tried to do this but the training does not converge to the target accuracy. I come here to get some help.

```
def _update_params_on_kvstore_easgd(param_arrays, grad_arrays, central_arrays, kvstore, param_names,
easgd_beta, easgd_iter, learning_rate, updater):
"""
:param param_arrays:
:param grad_arrays:
:param central_arrays:
:param kvstore:
:param param_names:
:param easgd_beta: the beta value set for easgd algorithm
:param easgd_iter: how often the weight is updated
:param learning_rate:
:param updater: the updater used for the worker update operation
:return:
"""
for index, pair in enumerate(zip(param_arrays, grad_arrays, central_arrays)):
arg_list, grad_list, central_list = pair
if grad_list[0] is None:
continue
name = param_names[index]
kvstore.pull(name, central_list, priority= -index)
for k, p in enumerate(zip(arg_list, central_list, grad_list)):
w_local, w_central, w_grad = p
w_central[:] = easgd_beta/(easgd_iter*kvstore.num_workers) * (w_local - w_central)
w_local[:] = w_local - learning_rate * w_grad
kvstore.push(name, central_list, priority= -index)
```

Above is the implementation code in the worker node. The central weights will be delivered to the server node to update the central_weights in the server node. The *easgd_beta* is set to 0.9 according to the NIPSâ€™15 (Deep learning with Elastic Averaging SGD). Anyone with any suggestions? Thank you very much. More details can be provided if you are interested in this discussion.