classifier_tf.py 文件源码

def write_summaries(self, X, y, label, step, summary_writer=None):
        if not X:
            return
        y_pred, loss = self.predict_proba_with_loss(X, y)
        metrics = classification_metrics(y, y_pred, self.threshold)
        metrics['loss'] = loss
        if summary_writer is not None:
            summary = tf.Summary()
            for key, value in metrics.items():
                summary.value.add(tag="metrics/{}".format(key), simple_value=float(value))
            if not self.summary_tensors:
                self.summary_tensors["positive_predictions_input"] = tf.placeholder(
                    tf.float32, [None], "positive_predictions_input")
                self.summary_tensors["positive_predictions"] = tf.summary.histogram(
                    "positive_predictions", self.summary_tensors["positive_predictions_input"])
                self.summary_tensors["negative_predictions_input"] = tf.placeholder(
                    tf.float32, [None], "negative_predictions_input")
                self.summary_tensors["negative_predictions"] = tf.summary.histogram(
                    "negative_predictions", self.summary_tensors["negative_predictions_input"])
            summary_writer.add_summary(
                self.summary_tensors["positive_predictions"].eval(
                    feed_dict={self.summary_tensors["positive_predictions_input"]: y_pred[y]}),
                step)
            summary_writer.add_summary(
                self.summary_tensors["negative_predictions"].eval(
                    feed_dict={self.summary_tensors["negative_predictions_input"]: y_pred[~y]}),
                step)
            summary_writer.add_summary(summary, step)
            summary_writer.flush()