e666da279a878b3f1fe2969363cc5d6fa8d5408d,base.py,TFBaseModel,test_on_batch,#TFBaseModel#,222

Before Change

        
        evaluate sum of batch loss
        
        feed_dict_ = {self._get_input_data(): x,
                      self._get_input_target(): y, self.train_flag: False}
        loss = self.sess.run(self.metric_list, feed_dict=feed_dict_)
        return loss[0]

After Change

        
        evaluate sum of batch loss
        
        feed_dict_ = {self._get_input_target(): y, self.train_flag: False,
                      self.sample_weight: np.ones(y.shape[0])}
        input_data = self._get_input_data()
        if isinstance(input_data, list):
            for i in range(len(input_data)):
                feed_dict_[input_data[i]] = x[i]
        else:
            feed_dict_[input_data] = x
        score = self.sess.run(self.metric_list, feed_dict=feed_dict_)

        if class_weight is None and sample_weight is None:
            return np.mean(score[0])

Instances