(self, sess, data_x, len_list)
return ret_pred_prob
Make predictions from the provided model.
        Args:
            sess: tf.Session() obj
            data_x: input data matrix have the shape of (data_num, num_steps), change it to ndarray before this function is called
            len_list: input data_length have the shape of (data_num)
        Returns:
          ret_pred_prob: Probability of the prediction with respect to each class
Make predictions from the provided model.
        Args:
            sess: tf.Session() obj
            data_x: input data matrix have the shape of (data_num, num_steps), change it to ndarray before this function is called
            len_list: input data_length have the shape of (data_num)
        Returns:
          ret_pred_prob: Probability of the prediction with respect to each class
Make predictions from the provided model . Args : sess : tf . Session () obj data_x : input data matrix have the shape of ( data_num num_steps ) change it to ndarray before this function is called len_list : input data_length have the shape of ( data_num ) Returns : ret_pred_prob : Probability of the prediction with respect to each class
def predict(self, sess, data_x, len_list):
        """Make predictions from the provided model.
        Args:
            sess: tf.Session() obj
            data_x: input data matrix have the shape of (data_num, num_steps), change it to ndarray before this function is called
            len_list: input data_length have the shape of (data_num)
        Returns:
          ret_pred_prob: Probability of the prediction with respect to each class
        """
        ret_pred_prob = []
        for indices in helper.pred_data_iter_indices(len(data_x), self.config.batch_size):
            feed_dict = self.create_feed_dict(data_x[indices], len_list[indices])
            pred_prob = sess.run(self.predict_prob, feed_dict=feed_dict)
            ret_pred_prob.append(pred_prob)
        ret_pred_prob = np.concatenate(ret_pred_prob, axis=0)
        return ret_pred_prob
def predict ( self , sess , data_x , len_list ) : ret_pred_prob = [ ] for indices in helper . pred_data_iter_indices ( len ( data_x ) , self . config . batch_size ) : feed_dict = self . create_feed_dict ( data_x [ indices ] , len_list [ indices ] ) pred_prob = sess . run ( self . predict_prob , feed_dict = feed_dict ) ret_pred_prob . append ( pred_prob ) ret_pred_prob = np . concatenate ( ret_pred_prob , axis = 0 ) return ret_pred_prob