Question

考虑以下示例：

import numpy as np 
import tensorflow as tf

labels = np.zeros([2, 3, 3, 1])
labels[0,0,1,0], labels[0,0,2,0] = 1, 1
predictions = np.zeros([2, 3, 3, 1])
predictions[0,0,0,0], predictions[0,0,1,0], predictions[0,0,2,0] = 1, 1, 1

我将数据写入tf.constant：

tf_labels = tf.constant(labels, dtype=tf.float32)
tf_predictions = tf.constant(predictions, dtype=tf.float32)

现在奇怪的行为：

tf_metric, tf_metric_update = tf.metrics.mean_iou(tf_labels,
                                                  tf_predictions, 2,
                                                  name="my_metric")

with tf.control_dependencies([tf_metric_update]):
    tf_metric = tf.identity(tf_metric)

with tf.Session() as session:
    session.run(tf.local_variables_initializer())

    for _ in range(20):    
        # Calculate the score
        score = session.run(tf_metric)
        print(score)

结果包含一些奇怪的数字

现在，我删除tf.control_dependencies块并显式运行tf_metric_update操作。

tf_metric, tf_metric_update = tf.metrics.mean_iou(tf_labels,
                                                  tf_predictions, 2,
                                                  name="my_metric")

with tf.Session() as session:
    session.run(tf.local_variables_initializer())

    for _ in range(20):
        session.run(tf_metric_update)

        # Calculate the score
        score = session.run(tf_metric)
        print(score)

现在一切都可以正常运行

我以错误的方式使用tf.control_dependencies吗？

使用tf.control_dependencies时tf.metrics.mean_iou返回怪异数字

0 个答案: