def get_model_optimizer(args):
model = SVM(c=args.c, penalty=args.penalty)
if args.gpu >= 0:
model.to_gpu()
if args.penalty == 'L2':
optimizer = optimizers.SGD(lr=args.lr)
elif args.penalty == 'L1':
optimizer = SGD(lr=args.lr)
optimizer.setup(model)
return model, optimizer
def get_model_optimizer(args):
model = SVM(c=args.c, penalty=args.penalty)
if args.gpu >= 0:
model.to_gpu()
if args.penalty == 'L2':
optimizer = optimizers.SGD(lr=args.lr)
elif args.penalty == 'L1':
optimizer = SGD(lr=args.lr)
optimizer.setup(model)
return model, optimizer
elif optimizer_flag == 'Adam':
optimizer = Adam(lr=Adam_list[0],
p1=Adam_list[1],
p2=Adam_list[2],
eps=Adam_list[3])
elif optimizer_flag == 'RMSpropGraves':
optimizer = RMSpropGraves(lr=RMSpropGraves_list[0],
p=RMSpropGraves_list[1],
eps=RMSpropGraves_list[2])
elif optimizer_flag == 'SMORMS3':
optimizer = SMORMS3(lr=SMORMS3_list[0], eps=SMORSM3_list[1])
model = MLP(model_list)
#optimizerの設定
optimizer.setup(model)
#n_epochとbatchsizeを変更可能
train_loss_list, train_acc_list, test_loss_list, test_acc_list = learning(
model, optimizer, n_epoch, batchsize)
if gpu:
#cpuへの変換
train_loss_list = np.asnumpy(train_loss_list)
train_acc_list = np.asnumpy(train_acc_list)
test_loss_list = np.asnumpy(test_loss_list)
test_acc_list = np.asnumpy(test_acc_list)
import numpy as np # for plot
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