Bug in initializers init_from_dict()

[RaulMurillo]

System information

• OS Platform and Distribution: Linux Ubuntu 18.04
• Python version: 3.6.9
• NumPy version: 1.19.5

Describe the current behavior
When calling the init_from_dict() method from numpy_ml/neural_nets/initializers, from both SchedulerInitializer and OptimizerInitializer classes, the returned object is None, rather than a propper object.
This is caused by the assignation of the set_params() method to the returned object. Such a method does not return an object but modifies the instance itself.

numpy-ml/numpy_ml/neural_nets/initializers/initializers.py

Lines 176 to 194 in b537fac

	def init_from_dict(self):
	O = self.param
	cc = O["cache"] if "cache" in O else None
	op = O["hyperparameters"] if "hyperparameters" in O else None
	if op is None:
	raise ValueError("Must have `hyperparemeters` key: {}".format(O))
	if op and op["id"] == "SGD":
	optimizer = SGD().set_params(op, cc)
	elif op and op["id"] == "RMSProp":
	optimizer = RMSProp().set_params(op, cc)
	elif op and op["id"] == "AdaGrad":
	optimizer = AdaGrad().set_params(op, cc)
	elif op and op["id"] == "Adam":
	optimizer = Adam().set_params(op, cc)
	elif op:
	raise NotImplementedError("{}".format(op["id"]))
	return optimizer

Describe the expected behavior
init_from_dict() should return a propper object that will be assigned to an attribute of a NN layer.

Code to reproduce the issue

from numpy_ml.neural_nets.layers import *

c1 = Conv2D(6, (3,3))
opt = c1.hyperparameters['optimizer'] # dict

c2=Conv2D(6, (3,3), optimizer=opt) # The optimizer is set to None
c2.hyperparameters

This raises AttributeError: 'NoneType' object has no attribute 'cache'.
The same happens with Scheduler.