def sgd(l_rate, parameters, grads): 
	""" 
	Stochastic Gradient Descent.

	Parameters
	----------
	:type lr: theano.tensor.scalar
	:param lr: Initial learning rate
	
	:type parameters: theano.shared
	:params parameters: Model parameters to update
	
	:type grads: Theano variable
	:params grads: Gradients of cost w.r.t to parameters
	"""

	updates = []
	for param, grad in zip(parameters, grads):
		updates.append((param, param - l_rate * grad))

	return updates