def __init__(self, args, search_space, action_list, submodel_manager):
"""
Constructor for training algorithm.
Build sub-model manager and controller.
Build optimizer and cross entropy loss for controller.
Args:
args: From command line, picked up by `argparse`.
"""
self.args = args
self.controller_step = 0 # counter for controller
self.cuda = args.cuda
self.epoch = 0
self.start_epoch = 0
self.submodel_manager = None
self.controller = None
self.early_stop_manager = EarlyStop(10)
self.reward_manager = TopAverage(10)
super(RL_Selector, self).__init__(args, search_space, action_list,
submodel_manager)
self.build_model() # build controller
self.max_length = self.args.shared_rnn_max_length
controller_optimizer = _get_optimizer(self.args.controller_optim)
self.controller_optim = \
controller_optimizer(self.controller.parameters(),
lr=self.args.controller_lr)
def __init__(self, args):
self.args = args
if hasattr(args, 'dataset') and args.dataset in ["cora", "citeseer", "pubmed"]:
self.data = load(args)
self.args.in_feats = self.in_feats = self.data.features.shape[1]
self.args.num_class = self.n_classes = self.data.num_labels
self.early_stop_manager = EarlyStop(10)
self.reward_manager = TopAverage(10)
print('the experiment config:', '\n', args)
self.args = args
self.drop_out = args.in_drop
self.multi_label = args.multi_label
self.lr = args.lr
self.weight_decay = args.weight_decay
self.retrain_epochs = args.retrain_epochs
self.loss_fn = torch.nn.BCELoss() # binary cross entropy loss
self.epochs = args.epochs
self.train_graph_index = 0
self.train_set_length = 10
self.param_file = args.param_file
self.shared_params = None
self.loss_fn = torch.nn.functional.nll_loss
def __init__(self, args):
self.args = args
if hasattr(args, 'dataset') and args.dataset in [
"cora", "citeseer", "pubmed"
]:
self.data = load(args)
self.args.in_feats = self.in_feats = self.data.features.shape[1]
self.args.num_class = self.n_classes = self.data.num_labels
self.early_stop_manager = EarlyStop(10)
self.reward_manager = TopAverage(10)
"""
class TopAverage(object):
def __init__(self, top_k=10):
self.scores = []
self.top_k = top_k
def get_top_average(self):
if len(self.scores) > 0:
return np.mean(self.scores)
else:
return 0
def get_average(self, score):
if len(self.scores) > 0:
avg = np.mean(self.scores)
else:
avg = 0
# print("Top %d average: %f" % (self.top_k, avg))
self.scores.append(score)
self.scores.sort(reverse=True)
self.scores = self.scores[:self.top_k]
return avg
def get_reward(self, score):
reward = score - self.get_average(score)
return np.clip(reward, -0.5, 0.5)
"""
self.args = args
self.drop_out = args.in_drop
self.multi_label = args.multi_label
self.lr = args.lr
self.weight_decay = args.weight_decay
self.retrain_epochs = args.retrain_epochs
self.loss_fn = torch.nn.BCELoss()
self.epochs = args.epochs
self.train_graph_index = 0
self.train_set_length = 10
self.param_file = args.param_file
self.shared_params = None
self.loss_fn = torch.nn.functional.nll_loss