fetches = [self.yhat, self.final_state] feed_dict = {self.X: in_idxs} for i in range(self.layers): feed_dict[self.state[i]] = self.predict_state[i] preds, self.predict_state = self.sess.run(fetches, feed_dict) preds = np.asarray(preds).T return pd.DataFrame(data=preds, index=itemidmap.index) if __name__ == '__main__': defaults = Defaults() data = pd.read_csv(PATH_TO_TRAIN, sep='\t', dtype={'ItemId': np.int64}) valid = pd.read_csv(PATH_TO_TEST, sep='\t', dtype={'ItemId': np.int64}) defaults.n_items = len(data['ItemId'].unique()) defaults.dropout_p_hidden = 1.0 if defaults.is_training == 0 else 0.5 if not os.path.exists(defaults.checkpoint_dir): os.mkdir(defaults.checkpoint_dir) gpu_config = tf.ConfigProto() gpu_config.gpu_options.allow_growth = True with tf.Session(config=gpu_config) as session: predictor = Session4RecPredictor(defaults, session) if defaults.is_training: print('Start session4rec training...') predictor.train(data) else: