def set_seeds(): torch.manual_seed(SimpleRandom.get_seed()) if gpu(): torch.cuda.manual_seed_all(SimpleRandom.get_seed()) np.random.seed(SimpleRandom.get_seed()) random.seed(SimpleRandom.get_seed())
def set_seeds(): torch.manual_seed(SimpleRandom.get_seed(seed_from_config_file)) if gpu(): torch.cuda.manual_seed_all(SimpleRandom.get_seed()) np.random.seed(SimpleRandom.get_seed()) random.seed(SimpleRandom.get_seed())
def prepare(data): #Note, we should just be passing in a sparse minibatch here! Doing todense on the entire datset is silly if issparse(data): data = data.todense() v = torch.FloatTensor(np.array(data)) if gpu(): return Variable(v.cuda()) return Variable(v)
def prepare(data): data = data.todense() v = torch.FloatTensor(np.array(data)) if gpu(): return Variable(v.cuda()) return Variable(v)
def prepare_with_labels(data, labels): data = data.todense() v = torch.FloatTensor(np.array(data)) if gpu(): return Variable(v.cuda()), Variable(torch.LongTensor(labels).cuda()) return Variable(v), Variable(torch.LongTensor(labels))
train_ds.read() dev_ds.read() test_ds = None if args.test is not None: test_ds = DataSet(file=args.test, reader=jlr, formatter=formatter) test_ds.read() train_feats, dev_feats, test_feats = f.load(train_ds, dev_ds, test_ds) f.save_vocab(mname) input_shape = train_feats[0].shape[1] model = SimpleMLP(input_shape,100,3) if gpu(): model.cuda() if model_exists(mname) and os.getenv("TRAIN").lower() not in ["y","1","t","yes"]: model.load_state_dict(torch.load("models/{0}.model".format(mname))) else: train(model, train_feats, 500, 1e-2, 90,dev_feats,early_stopping=EarlyStopping(mname)) torch.save(model.state_dict(), "models/{0}.model".format(mname)) print_evaluation(model, dev_feats, FEVERLabelSchema()) if args.test is not None: print_evaluation(model, test_feats, FEVERLabelSchema())