Merge branch 'master' of https://github.com/ddbourgin/numpy-ml into master

Author: ddbourgin

numpy_ml/neural_nets/initializers/initializers.py

@@ -122,13 +122,14 @@ def init_from_dict(self):
  raise ValueError("Must have `hyperparameters` key: {}".format(S))
 
  if sc and sc["id"] == "ConstantScheduler":
- scheduler = ConstantScheduler().set_params(sc)
+ scheduler = ConstantScheduler()
  elif sc and sc["id"] == "ExponentialScheduler":
- scheduler = ExponentialScheduler().set_params(sc)
+ scheduler = ExponentialScheduler()
  elif sc and sc["id"] == "NoamScheduler":
- scheduler = NoamScheduler().set_params(sc)
+ scheduler = NoamScheduler()
  elif sc:
  raise NotImplementedError("{}".format(sc["id"]))
+ scheduler.set_params(sc)
  return scheduler
 
 
@@ -182,15 +183,16 @@ def init_from_dict(self):
  raise ValueError("Must have `hyperparemeters` key: {}".format(O))
 
  if op and op["id"] == "SGD":
- optimizer = SGD().set_params(op, cc)
+ optimizer = SGD()
  elif op and op["id"] == "RMSProp":
- optimizer = RMSProp().set_params(op, cc)
+ optimizer = RMSProp()
  elif op and op["id"] == "AdaGrad":
- optimizer = AdaGrad().set_params(op, cc)
+ optimizer = AdaGrad()
  elif op and op["id"] == "Adam":
- optimizer = Adam().set_params(op, cc)
+ optimizer = Adam()
  elif op:
  raise NotImplementedError("{}".format(op["id"]))
+ optimizer.set_params(op, cc)
  return optimizer
 
 

numpy_ml/neural_nets/layers/layers.py

@@ -119,12 +119,12 @@ def set_params(self, summary_dict):
  if k in self.hyperparameters:
  if k == "act_fn":
  layer.act_fn = ActivationInitializer(v)()
- if k == "optimizer":
+ elif k == "optimizer":
  layer.optimizer = OptimizerInitializer(sd[k])()
- if k not in ["wrappers", "optimizer"]:
- setattr(layer, k, v)
- if k == "wrappers":
+ elif k == "wrappers":
  layer = init_wrappers(layer, sd[k])
+ elif k not in ["wrappers", "optimizer"]:
+ setattr(layer, k, v)
  return layer
 
  def summary(self):