def main():
np.random.seed(args.seed)
cur_acc = 0
max_acc = 0
num_param = 12
cur_param = np.zeros(num_param)
max_pt = np.zeros(num_param)
for iii in range(args.n_iter):
for jjj in range(args.n_samples):
for kkk in range(num_param):
cur_param[kkk] = np.random.beta(1, 1)
cur_param[2] *= 0.5
cur_param[4] *= 0.5
cur_param[9] *= 0.5
cur_param[5] /= 0.5
cur_param[6] *= 0.5
cur_param[11] /= 0.5
cur_param[10] *= 0.5
cur_acc = black_box_function(cur_param)
if max_acc < cur_acc:
max_acc = cur_acc
max_pt = cur_param.copy()
hyp_param = np.zeros(6)
hyp_param[0] = max_pt[0]
hyp_param[1] = 1 - max_pt[0]
hyp_param[2] = max_pt[1]
hyp_param[3] = max_pt[2]
hyp_param[4] = max_pt[3]
hyp_param[5] = max_pt[4]
rate_schedule = hyp_param[0] * (1 - np.exp(-hyp_param[3] * np.power(
np.arange(args.n_epoch, dtype=float),
hyp_param[2]))) + hyp_param[1] * (1 - 1 / np.power(
(hyp_param[5] * np.arange(args.n_epoch, dtype=float) + 1),
hyp_param[4]))
print('Schedule:', rate_schedule, hyp_param)
'''
rate_schedule=np.ones(args.n_epoch)*forget_rate
rate_schedule[:10]=np.arange(10,dtype=float)/10*forget_rate
# rate_schedule[10:]=np.arange(args.n_epoch-10,dtype=float)/(args.n_epoch-10)*forget_rate+forget_rate
rate_schedule=np.zeros(args.n_epoch)
print(rate_schedule)
'''
epoch = 0
mean_pure_ratio1 = 0
mean_pure_ratio2 = 0
print('building model...')
cnn1 = CNN_large(n_outputs=num_classes)
cnn1.cuda()
print(cnn1.parameters)
optimizer1 = torch.optim.Adam(cnn1.parameters(), lr=learning_rate)
cnn2 = CNN_large(n_outputs=num_classes)
cnn2.cuda()
print(cnn2.parameters)
optimizer2 = torch.optim.Adam(cnn2.parameters(), lr=learning_rate)
train_acc1 = 0
train_acc2 = 0
# evaluate models with random weights
test_acc1, test_acc2 = evaluate(test_loader, cnn1, cnn2)
print(
'Epoch [%d/%d] Test Accuracy on the %s test images: Model1 %.4f %% Model2 %.4f %% Pure Ratio1 %.4f %% Pure Ratio2 %.4f %%'
% (epoch + 1, args.n_epoch, len(test_dataset), test_acc1, test_acc2,
mean_pure_ratio1, mean_pure_ratio2))
# save results
with open(txtfile, "a") as myfile:
myfile.write(
str(int(epoch)) + ' ' + str(train_acc1) + ' ' + str(train_acc2) +
' ' + str(test_acc1) + " " + str(test_acc2) + ' ' +
str(mean_pure_ratio1) + ' ' + str(mean_pure_ratio2) + ' ' +
str(rate_schedule[epoch]) + "\n")
# training
for epoch in range(1, args.n_epoch):
# train models
cnn1.train()
adjust_learning_rate(optimizer1, epoch)
cnn2.train()
adjust_learning_rate(optimizer2, epoch)
train_acc1, train_acc2, pure_ratio_1_list, pure_ratio_2_list = train(
train_loader, epoch, cnn1, optimizer1, cnn2, optimizer2,
rate_schedule)
# evaluate models
test_acc1, test_acc2 = evaluate(test_loader, cnn1, cnn2)
# save results
mean_pure_ratio1 = sum(pure_ratio_1_list) / len(pure_ratio_1_list)
mean_pure_ratio2 = sum(pure_ratio_2_list) / len(pure_ratio_2_list)
print(
'Epoch [%d/%d] Test Accuracy on the %s test images: Model1 %.4f %% Model2 %.4f %%, Pure Ratio 1 %.4f %%, Pure Ratio 2 %.4f %%'
% (epoch + 1, args.n_epoch, len(test_dataset), test_acc1,
test_acc2, mean_pure_ratio1, mean_pure_ratio2))
with open(txtfile, "a") as myfile:
myfile.write(
str(int(epoch)) + ' ' + str(train_acc1) + ' ' +
str(train_acc2) + ' ' + str(test_acc1) + " " + str(test_acc2) +
' ' + str(mean_pure_ratio1) + ' ' + str(mean_pure_ratio2) +
' ' + str(rate_schedule[epoch]) + "\n")
def black_box_function(opt_param):
mean_pure_ratio1 = 0
mean_pure_ratio2 = 0
print('building model...')
cnn1 = CNN_large(n_outputs=num_classes)
cnn1.cuda()
print(cnn1.parameters)
optimizer1 = torch.optim.Adam(cnn1.parameters(), lr=learning_rate)
cnn2 = CNN_large(n_outputs=num_classes)
cnn2.cuda()
print(cnn2.parameters)
optimizer2 = torch.optim.Adam(cnn2.parameters(), lr=learning_rate)
# rate_schedule=opt_param[0]*(1-np.exp(-opt_param[2]*np.power(np.arange(args.n_epoch,dtype=float),opt_param[1])))+(1-opt_param[0])*(1-1/np.power((opt_param[4]*np.arange(args.n_epoch,dtype=float)+1),opt_param[3]))-np.power(np.arange(args.n_epoch,dtype=float)/args.n_epoch,opt_param[5])*opt_param[6]
rate_schedule=opt_param[0]*(1-np.exp(-opt_param[2]*np.power(np.arange(args.n_epoch,dtype=float),opt_param[1])))\
+(1-opt_param[0])*opt_param[7]*(1-1/np.power((opt_param[4]*np.arange(args.n_epoch,dtype=float)+1),opt_param[3]))\
+(1-opt_param[0])*(1-opt_param[7])*(1-np.log(1+opt_param[8])/np.log(1+opt_param[8]+opt_param[9]*np.arange(args.n_epoch,dtype=float)))\
-np.power(np.arange(args.n_epoch,dtype=float)/args.n_epoch,opt_param[5])*opt_param[6]\
-np.log(1+np.power(np.arange(args.n_epoch,dtype=float),opt_param[11]))/np.log(1+np.power(args.n_epoch,opt_param[11]))*opt_param[10]
print('Schedule:', rate_schedule, opt_param)
epoch = 0
train_acc1 = 0
train_acc2 = 0
# evaluate models with random weights
val_acc1, val_acc2 = evaluate(val_loader, cnn1, cnn2)
test_acc1, test_acc2 = evaluate(test_loader, cnn1, cnn2)
print(
'Epoch [%d/%d] Test Accuracy on the %s test images: Model1 %.4f %% Model2 %.4f %% Pure Ratio1 %.4f %% Pure Ratio2 %.4f %%'
% (epoch + 1, args.n_epoch, len(test_dataset), test_acc1, test_acc2,
mean_pure_ratio1, mean_pure_ratio2))
# save results
with open(txtfile, "a") as myfile:
myfile.write(
str(int(epoch)) + ' ' + str(train_acc1) + ' ' + str(train_acc2) +
' ' + str(val_acc1) + ' ' + str(val_acc2) + ' ' + str(test_acc1) +
" " + str(test_acc2) + ' ' + str(mean_pure_ratio1) + ' ' +
str(mean_pure_ratio2) + ' ' + str(rate_schedule[epoch]) + "\n")
# training
for epoch in range(1, args.n_epoch):
# train models
cnn1.train()
adjust_learning_rate(optimizer1, epoch)
cnn2.train()
adjust_learning_rate(optimizer2, epoch)
train_acc1, train_acc2, pure_ratio_1_list, pure_ratio_2_list = train(
train_loader, epoch, cnn1, optimizer1, cnn2, optimizer2,
rate_schedule)
# evaluate models
val_acc1, val_acc2 = evaluate(val_loader, cnn1, cnn2)
test_acc1, test_acc2 = evaluate(test_loader, cnn1, cnn2)
# save results
mean_pure_ratio1 = sum(pure_ratio_1_list) / len(pure_ratio_1_list)
mean_pure_ratio2 = sum(pure_ratio_2_list) / len(pure_ratio_2_list)
print(
'Epoch [%d/%d] Test Accuracy on the %s test images: Model1 %.4f %% Model2 %.4f %%, Pure Ratio 1 %.4f %%, Pure Ratio 2 %.4f %%'
% (epoch + 1, args.n_epoch, len(test_dataset), test_acc1,
test_acc2, mean_pure_ratio1, mean_pure_ratio2))
with open(txtfile, "a") as myfile:
myfile.write(
str(int(epoch)) + ' ' + str(train_acc1) + ' ' +
str(train_acc2) + ' ' + str(val_acc1) + ' ' + str(val_acc2) +
' ' + str(test_acc1) + " " + str(test_acc2) + ' ' +
str(mean_pure_ratio1) + ' ' + str(mean_pure_ratio2) + ' ' +
str(rate_schedule[epoch]) + "\n")
return (test_acc1 + test_acc2) / 200
def black_box_function(opt_param):
mean_pure_ratio1 = 0
mean_pure_ratio2 = 0
print('building model...')
cnn1 = CNN_large(n_outputs=num_classes)
cnn1.cuda()
print(cnn1.parameters)
optimizer1 = torch.optim.Adam(cnn1.parameters(), lr=learning_rate)
cnn2 = CNN_large(n_outputs=num_classes)
cnn2.cuda()
print(cnn2.parameters)
optimizer2 = torch.optim.Adam(cnn2.parameters(), lr=learning_rate)
rate_schedule = opt_param.copy()
print('Schedule:', rate_schedule)
epoch = 0
train_acc1 = 0
train_acc2 = 0
# evaluate models with random weights
test_acc1, test_acc2 = evaluate(test_loader, cnn1, cnn2)
print(
'Epoch [%d/%d] Test Accuracy on the %s test images: Model1 %.4f %% Model2 %.4f %% Pure Ratio1 %.4f %% Pure Ratio2 %.4f %%'
% (epoch + 1, args.n_epoch, len(test_dataset), test_acc1, test_acc2,
mean_pure_ratio1, mean_pure_ratio2))
# save results
with open(txtfile, "a") as myfile:
myfile.write(
str(int(epoch)) + ' ' + str(train_acc1) + ' ' + str(train_acc2) +
' ' + str(test_acc1) + " " + str(test_acc2) + ' ' +
str(mean_pure_ratio1) + ' ' + str(mean_pure_ratio2) + ' ' +
str(rate_schedule[epoch]) + "\n")
# training
for epoch in range(1, args.n_epoch):
# train models
cnn1.train()
adjust_learning_rate(optimizer1, epoch)
cnn2.train()
adjust_learning_rate(optimizer2, epoch)
train_acc1, train_acc2, pure_ratio_1_list, pure_ratio_2_list = train(
train_loader, epoch, cnn1, optimizer1, cnn2, optimizer2,
rate_schedule)
# evaluate models
test_acc1, test_acc2 = evaluate(test_loader, cnn1, cnn2)
# save results
mean_pure_ratio1 = sum(pure_ratio_1_list) / len(pure_ratio_1_list)
mean_pure_ratio2 = sum(pure_ratio_2_list) / len(pure_ratio_2_list)
print(
'Epoch [%d/%d] Test Accuracy on the %s test images: Model1 %.4f %% Model2 %.4f %%, Pure Ratio 1 %.4f %%, Pure Ratio 2 %.4f %%'
% (epoch + 1, args.n_epoch, len(test_dataset), test_acc1,
test_acc2, mean_pure_ratio1, mean_pure_ratio2))
with open(txtfile, "a") as myfile:
myfile.write(
str(int(epoch)) + ' ' + str(train_acc1) + ' ' +
str(train_acc2) + ' ' + str(test_acc1) + " " + str(test_acc2) +
' ' + str(mean_pure_ratio1) + ' ' + str(mean_pure_ratio2) +
' ' + str(rate_schedule[epoch]) + "\n")
return (test_acc1 + test_acc2) / 200