tensorflow 精度指标返回值的含义

Question

我对模块 tf.metrics（例如 tf.metrics.accuracy）的函数返回的值有点困惑。

一段简单的代码，我在其中使用 tf.metrics.accuracy 并使用 tp、tn、fp 和 fn 计算准确度。

import tensorflow as tf

# true and predicted tensors
y_p = tf.placeholder(dtype=tf.int64)
y_t = tf.placeholder(dtype=tf.int64)

# Count true positives, true negatives, false positives and false negatives.
tp = tf.count_nonzero(y_p * y_t)
tn = tf.count_nonzero((y_p - 1) * (y_t - 1))
fp = tf.count_nonzero(y_p * (y_t - 1))
fn = tf.count_nonzero((y_p - 1) * y_t)

acc = tf.metrics.accuracy(y_p, y_t)

# Calculate accuracy, precision, recall and F1 score.
accuracy = (tp + tn) / (tp + fp + fn + tn)

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    sess.run(tf.local_variables_initializer())

    for i in range(4):
        if i == 0:
            yop = [0,0,0,0,0,0,0,0,0,0]
        elif i == 1:
            yop = [0,0,0,0,0,0,0,0,1,1]
        elif i == 2:
            yop = [1,1,1,0,0,0,0,0,0,1]
        else:
            yop = [0,1,1,1,1,1,1,0,0,0]
        tf_a = sess.run(acc, feed_dict={y_p: [0,0,0,0,0,0,0,0,0,0], y_t: yop})
        my_a = sess.run(accuracy, feed_dict={y_p: [0,0,0,0,0,0,0,0,0,0], y_t: yop})
        print("TF accuracy: {0}".format(tf_a))
        print("My accuracy: {0}".format(my_a))

输出

TF accuracy: (0.0, 1.0)
My accuracy: 1.0
TF accuracy: (1.0, 0.9)
My accuracy: 0.8
TF accuracy: (0.9, 0.8)
My accuracy: 0.6
TF accuracy: (0.8, 0.7)
My accuracy: 0.4

我知道 tf.metrics.accuracy（update_op）的第二个返回值是函数调用次数的平均准确度。但是，我无法理解第一个值，它应该代表准确性。为什么它与我自己计算的准确度值不同？有没有办法获取精度的非累积值？

提前致谢。

Answer 1

import tensorflow as tf
from sklearn.metrics import accuracy_score

# true and predicted tensors
y_p = tf.placeholder(dtype=tf.int64)
y_t = tf.placeholder(dtype=tf.int64)

# Count true positives, true negatives, false positives and false negatives.
tp = tf.count_nonzero(y_p * y_t)
tn = tf.count_nonzero((y_p - 1) * (y_t - 1))
fp = tf.count_nonzero(y_p * (y_t - 1))
fn = tf.count_nonzero((y_p - 1) * y_t)

acc = tf.metrics.accuracy(predictions=y_p, labels=y_t)

# Calculate accuracy, precision, recall and F1 score.
accuracy = (tp + tn) / (tp + fp + fn + tn)

with tf.Session() as sess:
    for i in range(4):
        sess.run(tf.global_variables_initializer())
        sess.run(tf.local_variables_initializer())


        if i == 0:
            yop = [0,0,0,0,0,0,0,0,0,0]
        elif i == 1:
            yop = [0,0,0,0,0,0,0,0,1,1]
        elif i == 2:
            yop = [1,1,1,0,0,0,0,0,0,1]
        else:
            yop = [0,1,1,1,1,1,1,0,0,0]
        print('accuracy_score', accuracy_score([0,0,0,0,0,0,0,0,0,0], yop))
        tf_a = sess.run(acc, feed_dict={y_p: [0,0,0,0,0,0,0,0,0,0], y_t: yop})
        my_a = sess.run(accuracy, feed_dict={y_p: [0,0,0,0,0,0,0,0,0,0], y_t: yop})
        print("TF accuracy: {0}".format(tf_a))
        print("My accuracy: {0}".format(my_a))
        print()

输出：

accuracy_score 1.0
TF accuracy: (0.0, 1.0)
My accuracy: 1.0

accuracy_score 0.8
TF accuracy: (0.0, 0.8)
My accuracy: 0.8

accuracy_score 0.6
TF accuracy: (0.0, 0.6)
My accuracy: 0.6

accuracy_score 0.4
TF accuracy: (0.0, 0.4)
My accuracy: 0.4

只需在循环内移动 tf.local_variables_initializer() 即可确保精度度量张量中的值得到 re-initialised.

为什么有效？

根据文档

The accuracy function creates two local variables, total and count that are used to compute the frequency with which predictions matches labels.

如果我们不重新初始化局部变量，那么先前迭代的值将保留在其中，导致您遇到的错误结果。

另一种方法是使用：

tf.contrib.metrics.accuracy 而不是 tf.metrics.accuracy。但这在最后给出了一些剩余价值，例如 0.800000011920929 而不是 0.8。正如 OP 在评论中指出的那样，它也是 deprecated。

来源：

https://www.tensorflow.org/api_docs/python/tf/metrics/accuracy

https://github.com/tensorflow/tensorflow/issues/3971

tensorflow 精度指标返回值的含义

Meaning of values returned by tensorflow accuracy metric

python

metrics

tensorflow