import lasagne import cPickle import random import numpy as np class base_model(object): """the base model for both transductive and inductive learning.""" def __init__(self, args): """ args (an object): contains the arguments used for initalizing the model. """ self.embedding_size = args.embedding_size self.learning_rate = args.learning_rate self.batch_size = args.batch_size self.neg_samp = args.neg_samp self.model_file = args.model_file self.window_size = args.window_size self.path_size = args.path_size self.g_batch_size = args.g_batch_size self.g_learning_rate = args.g_learning_rate self.g_sample_size = args.g_sample_size self.use_feature = args.use_feature self.update_emb = args.update_emb self.layer_loss = args.layer_loss lasagne.random.set_rng(np.random) np.random.seed(13) random.seed(13) self.inst_generator = self.gen_train_inst() self.graph_generator = self.gen_graph() self.label_generator = self.gen_label_graph() def store_params(self): """serialize the model parameters in self.model_file. """ for i, l in enumerate(self.l): fout = open("{}.{}".format(self.model_file, i), 'w') params = lasagne.layers.get_all_param_values(l) cPickle.dump(params, fout, cPickle.HIGHEST_PROTOCOL) fout.close() def load_params(self): """load the model parameters from self.model_file. """ for i, l in enumerate(self.l): fin = open("{}.{}".format(self.model_file, i)) params = cPickle.load(fin) lasagne.layers.set_all_param_values(l, params) fin.close() def comp_iter(self, iter): """an auxiliary function used for computing the number of iterations given the argument iter. iter can either be an int or a float. """ if iter >= 1: return iter return 1 if random.random() < iter else 0 def train(self, init_iter_label, init_iter_graph, max_iter, iter_graph, iter_inst, iter_label): """training API. This method is a wrapper for init_train and step_train. Refer to init_train and step_train for more details (Cf. trans_model.py and ind_model.py). """ self.init_train(init_iter_label, init_iter_graph) self.step_train(max_iter, iter_graph, iter_inst, iter_label)