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Fixed bug in layers.set_params() #62

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merged 2 commits into from
Mar 5, 2021
Merged

Fixed bug in layers.set_params() #62

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51d82a7
Fixed bug in layers.set_params()
RaulMurillo Mar 4, 2021
3db6423
Fixed bug in NN initializers
RaulMurillo Mar 4, 2021
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16 changes: 9 additions & 7 deletions numpy_ml/neural_nets/initializers/initializers.py
View file
Original file line number Diff line number Diff line change
@@ -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


8 changes: 4 additions & 4 deletions numpy_ml/neural_nets/layers/layers.py
View file
Original file line number Diff line number Diff line change
@@ -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):