def run_nag(dataset, loss, alpha=1, regularizer=None, verbose=False, eta=0.01):
#normalized gradient from langford paper of print("dataset %s using model lineara, normalized gradient algorithm with %s-regularized %s loss."%(dataset,regularizer,loss))
from models.linear_model import LinearModel
m = LinearModel(2)
if loss == "squared_loss":
from losses.squared_loss import SquaredLoss
ls = SquaredLoss(m)
elif loss == "abs_loss":
from losses.abs_loss import AbsLoss
ls = AbsLoss(m)
else:
raise ValueError("no valid loss specified")
if regularizer == "l2":
from losses.regularized_loss import RegularizedLoss
from losses.regularizations.l2 import L2
l = RegularizedLoss(m, ls, L2(), alpha)
elif regularizer == "l1":
from losses.regularized_loss import RegularizedLoss
from losses.regularizations.l1 import L1
l = RegularizedLoss(m, ls, L1(), alpha)
elif regularizer == None:
l = ls
else:
raise ValueError("invalid regularization specified")
from algos.nag import NAG
alg = NAG(m, l, eta, verbose=verbose)
run_data("datasets/%s" % dataset, alg)
print("The parameter vector after training is")
print(m.get_param_vector())
del m, ls, l, alg
def run_nag(dataset,loss,alpha=1,regularizer=None,verbose=False,eta=0.01):
#normalized gradient from langford paper of print("dataset %s using model lineara, normalized gradient algorithm with %s-regularized %s loss."%(dataset,regularizer,loss))
from models.linear_model import LinearModel
m=LinearModel(2)
if loss=="squared_loss":
from losses.squared_loss import SquaredLoss
ls=SquaredLoss(m)
elif loss=="abs_loss":
from losses.abs_loss import AbsLoss
ls=AbsLoss(m)
else:
raise ValueError("no valid loss specified")
if regularizer=="l2":
from losses.regularized_loss import RegularizedLoss
from losses.regularizations.l2 import L2
l=RegularizedLoss(m,ls,L2(),alpha)
elif regularizer=="l1":
from losses.regularized_loss import RegularizedLoss
from losses.regularizations.l1 import L1
l=RegularizedLoss(m,ls,L1(),alpha)
elif regularizer==None:
l=ls
else:
raise ValueError("invalid regularization specified")
from algos.nag import NAG
alg=NAG(m,l,eta,verbose=verbose)
run_data("datasets/%s"%dataset,alg)
print("The parameter vector after training is")
print(m.get_param_vector())
del m, ls, l, alg
def run_ssgd(dataset,
loss,
model="linear",
alpha=1,
regularizer=None,
verbose=False,
scaler="scale_mean0",
replay=False,
period1=1,
period2=1):
#regularized sgd
from algos.ssgd import SSGD
print(
"dataset %s using model %s, %s-regularized sgd squared loss and scaling %s. eta=0.01 and replay=%s"
% (dataset, model, regularizer, scaler, replay))
if model == "linear":
from models.linear_model import LinearModel
m = LinearModel(2)
elif model == "affine":
from models.affine_model import AffineModel
m = AffineModel(2)
else:
raise ValueError("no valid model specified")
if loss == "squared_loss":
from losses.squared_loss import SquaredLoss
ls = SquaredLoss(m)
elif loss == "abs_loss":
from losses.abs_loss import AbsLoss
ls = AbsLoss(m)
else:
raise ValueError("no valid loss specified")
if regularizer == "l2":
from losses.regularized_loss import RegularizedLoss
from losses.regularizations.l2 import L2
l = RegularizedLoss(m, ls, L2(), alpha)
elif regularizer == "l1":
from losses.regularized_loss import RegularizedLoss
from losses.regularizations.l1 import L1
l = RegularizedLoss(m, ls, L1(), alpha)
elif regularizer == None:
l = ls
else:
raise ValueError("invalid regularization specified")
alg = SSGD(m, l, 1, verbose=verbose, scaler=scaler)
run_data("datasets/%s" % dataset, alg, replay=replay)
print("The parameter vector after training is")
print(m.get_param_vector())
del m, ls, l, alg
def run_sgd(dataset,loss,model="linear",alpha=1,regularizer=None,verbose=False):
#regularized sgd
from algos.sgd import SGD
print("dataset %s using model %s, %s-regularized sgd squared loss. eta=0.01"%(dataset,model,regularizer))
if model=="linear":
from models.linear_model import LinearModel
m=LinearModel(2)
elif model=="affine":
from models.affine_model import AffineModel
m=AffineModel(2)
else:
raise ValueError("no valid model specified")
if loss=="squared_loss":
from losses.squared_loss import SquaredLoss
ls=SquaredLoss(m)
elif loss=="abs_loss":
from losses.abs_loss import AbsLoss
ls=AbsLoss(m)
else:
raise ValueError("no valid loss specified")
if regularizer=="l2":
from losses.regularized_loss import RegularizedLoss
from losses.regularizations.l2 import L2
l=RegularizedLoss(m,ls,L2(),alpha)
elif regularizer=="l1":
from losses.regularized_loss import RegularizedLoss
from losses.regularizations.l1 import L1
l=RegularizedLoss(m,ls,L1(),alpha)
elif regularizer==None:
l=ls
else:
raise ValueError("invalid regularization specified")
alg=SGD(m,l,1,verbose=verbose)
run_data("datasets/%s"%dataset,alg)
print("The parameter vector after training is")
print(m.get_param_vector())
del m, ls, l, alg