def main(): FLAGS = build_parser() input_image = FLAGS.input_image model_dir = FLAGS.model_directory predictor = Predictor(model_dir) preds = predictor.predict(input_image) label_preds = [p[1] for p in [x for x in preds]] print(label_preds)
a['Close'].replace(0, np.nan, inplace=True) a['Close'].fillna(method='ffill', inplace=True) values = a['Close'].values.reshape(-1, 1) values = values.astype('float32') scaler = MinMaxScaler(feature_range=(0, 1)) scaled = scaler.fit_transform(values) train_size = int(len(scaled) * 0.8) valid_size = int(len(scaled) * 0.9) test_size = len(scaled) - train_size train, valid = scaled[0:train_size, :], scaled[train_size:valid_size, :] test = scaled[valid_size:, :] train_dict = create_data_dict(train, 4) test_dict = create_data_dict(valid, 4) model = LSTMRegressor(input_size=1, hidden_size=32, dropout_rate=0.5 ) predictor = Predictor(model=model, train_data=train_dict, test_data=test_dict, batch_size=64, use_cuda=True) predictor.fit(300, 30) results = predictor.predict(predictor.test_loader).reshape(1, -1)[0] test_y = test_dict["y"].numpy().reshape(1, -1)[0] pyplot.plot(results, label='predict') pyplot.plot(test_y, label='true') pyplot.legend() pyplot.show()