训练tf时记录精度指标。估计员

你试过了吗 tf.contrib.estimator.add_metrics(estimator, metric_fn) ( doc )?它需要一个初始化的估计器(可以预先封装),并将由 metric_fn 。

用法示例:

def custom_metric(labels, predictions):
    # This function will be called by the Estimator, passing its predictions.
    # Let's suppose you want to add the "mean" metric...

    # Accessing the class predictions (careful, the key name may change from one canned Estimator to another)
    predicted_classes = predictions["class_ids"]  

    # Defining the metric (value and update tensors):
    custom_metric = tf.metrics.mean(labels, predicted_classes, name="custom_metric")

    # Returning as a dict:
    return {"custom_metric": custom_metric}

# Initializing your canned Estimator:
classifier = tf.estimator.DNNClassifier(feature_columns=columns_feat, hidden_units=[10, 10], n_classes=NUM_CLASSES)

# Adding your custom metrics:
classifier = tf.contrib.estimator.add_metrics(classifier, custom_metric)

# Training/Evaluating:
tf.logging.set_verbosity(tf.logging.INFO) # Just to have some logs to display for demonstration

train_spec = tf.estimator.TrainSpec(input_fn=lambda:your_train_dataset_function(),
                                    max_steps=TRAIN_STEPS)
eval_spec=tf.estimator.EvalSpec(input_fn=lambda:your_test_dataset_function(),
                                steps=EVAL_STEPS,
                                start_delay_secs=EVAL_DELAY,
                                throttle_secs=EVAL_INTERVAL)
tf.estimator.train_and_evaluate(classifier, train_spec, eval_spec)

日志:

...
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [20/200]
INFO:tensorflow:Evaluation [40/200]
...
INFO:tensorflow:Evaluation [200/200]
INFO:tensorflow:Finished evaluation at 2018-04-19-09:23:03
INFO:tensorflow:Saving dict for global step 1: accuracy = 0.5668, average_loss = 0.951766, custom_metric = 1.2442, global_step = 1, loss = 95.1766
...

如您所见 custom_metric 随默认指标和损失一起返回。

除了@Aldream的答案外,您还可以使用 TensorBoard 查看 custom_metric 。为此,请将其添加到TensorFlow摘要中,如下所示:

tf.summary.scalar('custom_metric', custom_metric)

使用 tf.estimator.Estimator 您不需要将摘要添加到 FileWriter ,因为它是自动完成的(默认情况下每100步合并并保存一次)。

要查看张力板,需要打开一个新的端子并键入:

tensorboard --logdir={$MODEL_DIR}

之后,您将能够在以下位置在浏览器中查看图形: localhost:6006 。