def get_model(train_file, model_file, ndim=784, nout=10, val_fraction=0.3, islearning=True): ''' ''' args = parse_args(train_file, model_file, islearning, val_fraction) if args.network == 'mlp': data_shape = (ndim, ) net = get_mlp_spice(nout) elif args.network == 'cnn': data_shape = (15, 30) #sentence_size, num_embed net = get_cnn_spice(num_label=nout, batch_size=args.batch_size) # train model = models.fit(args, net, get_iterator_spice(data_shape)) model = load_obj(model_file) return model, args.network
def fit(store=None, data=None, **kwargs): return models.fit(Configuration(**kwargs), DataContext(store, data))
def fit(store=None, data=None, **kwargs): return models.fit(Configuration(**kwargs), DataContext(store, data))
if not os.path.exists(path + 'output\\'): os.mkdir(path + 'output\\') os.chdir(path + "output\\") result.to_csv(filename, index=None) zip = zipfile.ZipFile( filename.split('.')[0] + '.zip', "w", zipfile.ZIP_DEFLATED) zip.write(filename) zip.close() if __name__ == "__main__": '''提供了2018年1月1日至2018年12月31日的标的第一期的还款数据作为训练集, 需要选手预测2019年2月1日至2019年3月31日成交标的第一期的还款情况''' #时间序列,回归,或者分类(排除逾期的用户,剩下用户直接统计还款日作为结果) 二分类或多分类(逾期,逾期前1天、2天、3天还款) args = parse_command_params() # analysis_data(args['path']) train, test = gen_data(args) print(train.shape, test.shape) cols = list( set(train.columns) - set([ 'user_id', 'listing_id', 'auditing_date', 'due_date', 'insertdate', 'label' ])) model = models.fit(train, args, cols) result = gen_result(model, test, cols) save_zip(result, args) print('done !')
# For songs sampling "TEMPERATURE": 1, "TAKE_MAX_PROBABLE": False, "LIMIT_LEN": 300 } print(config) # model = VanillaRNN(config["VOCAB_SIZE"], config["HIDDEN"], config["VOCAB_SIZE"]).to(get_device()) model = LSTMSimple(config["VOCAB_SIZE"], config["HIDDEN"], config["VOCAB_SIZE"]).to(get_device()) criterion = CrossEntropyLoss() # Fit Model fit(model, train_encoded, val_encoded, config) # Report NLL for validation and test nll_val = negative_log_likelihood(model, val_encoded, criterion, config) nll_test = negative_log_likelihood(model, test_encoded, criterion, config) print("NLL Validation: {}".format(nll_val)) print("NLL Test: {}".format(nll_test)) # Save error plot to file save_loss_graph(model) # Save model to file print("Saving model...") now = datetime.now().strftime('%Y-%m-%d-%H-%M') torch.save(model.state_dict(), "model" + now + ".pth") print("Saved!")
def get_reward(actions, dataset): reward = models.fit(actions, dataset) return reward
def get_reward(self, actions): reward = models.fit(actions, self.dataset) return reward