def main():
args = get_train_args()
utils.set_seed(args.seed)
device = utils.get_device(args)
# Initialize Dataset for each tasks
assert args.dataset in ["splitMNIST", "permutedMNIST", 'fashionMNIST']
if args.dataset == "splitMNIST" or args.dataset == 'fashionMNIST':
labels_list = [[0, 1], [2, 3], [4, 5], [6, 7], [8, 9]]
elif args.dataset == 'permutedMNIST':
labels_list = [list(range(10))] * 5
# Run VCL
task_final_accs, all_accs = vcl.run_vcl(args, device, labels_list)
# Plots
config_str = '_{}_coreset_{}'.format(args.dataset, args.coreset_size)
utils.plot_small(task_final_accs, config_str)
utils.plot_all(all_accs, config_str)
avg_acc = np.mean(all_accs[-1])
print ("Final Average Accuracy: {}".format(avg_acc))
return next_x_train, next_y_train, next_x_test, next_y_test
hidden_size = [100, 100]
batch_size = 256
no_epochs = 100
single_head = True
num_tasks = 5
# Run vanilla VCL
tf.set_random_seed(12)
np.random.seed(1)
coreset_size = 0
data_gen = PermutedMnistGenerator(num_tasks)
vcl_result = vcl.run_vcl(hidden_size, no_epochs, data_gen,
coreset.rand_from_batch, coreset_size, batch_size, single_head)
print vcl_result
# Run random coreset VCL
tf.reset_default_graph()
tf.set_random_seed(12)
np.random.seed(1)
coreset_size = 200
data_gen = PermutedMnistGenerator(num_tasks)
rand_vcl_result = vcl.run_vcl(hidden_size, no_epochs, data_gen,
coreset.rand_from_batch, coreset_size, batch_size, single_head)
print rand_vcl_result
# Run k-center coreset VCL
tf.reset_default_graph()
num_tasks = args.tasknum
train_info = {}
train_info['date'] = args.date
train_info['experiment'] = args.experiment
train_info['trial'] = args.trial
train_info['batch'] = args.batch
train_info['tasknum'] = args.tasknum
# Run vanilla VCL
tf.set_random_seed(11 + args.trial)
np.random.seed(args.trial)
train_info['coreset_method']='none'
coreset_size = 0
data_gen = PermutedMnistGenerator(num_tasks)
vcl_result = vcl.run_vcl(hidden_size, no_epochs, data_gen,
coreset.rand_from_batch, coreset_size, batch_size, single_head, train_info)
print (vcl_result)
"""
# Run random coreset VCL
tf.reset_default_graph()
tf.set_random_seed(11 + args.trial)
np.random.seed(args.trial)
train_info['coreset_method']='random'
coreset_size = 200
data_gen = PermutedMnistGenerator(num_tasks)
rand_vcl_result = vcl.run_vcl(hidden_size, no_epochs, data_gen,
coreset.rand_from_batch, coreset_size, batch_size, single_head, train_info)
print (rand_vcl_result)
