我对模块 tf.metrics 的函数返回的值有点困惑(例如 tf.metrics.accuracy )。
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))
哪些输出
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
据我所知,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。
最佳答案
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重新初始化。
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,从而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值(value),例如 0.800000011920929
而不是 0.8
。也是deprecated正如 OP 在评论中指出的那样。
来源:
https://www.tensorflow.org/api_docs/python/tf/metrics/accuracy
关于python - tensorflow 精度指标返回值的含义,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/48909330/