cal_accuracy()用于精度计算。
import numpy as np
import tensorflow as tf
def cal_accuracy(cls_prob,label):
'''
:param cls_prob:
:param label:
:return:calculate classification accuracy for pos and neg examples only
'''
# get the index of maximum value along axis one from cls_prob
# 0 for negative 1 for positive
#按行返回cls_prob的最大值的索引，索引值为0或者1，索引等于0时
#表示这个图片网络预测为非人脸；为1时网络预测这张图片为人脸
pred = tf.argmax(cls_prob,axis=1)
label_int = tf.cast(label,tf.int64)
#tf.greater_equal()函数判断label_int是否大于等于0,返回True或者False
#tf.where函数（）返回True值对应的索引，即cond是pos样本和part样本对应的索引
cond = tf.where(tf.greater_equal(label_int,0))
picked = tf.squeeze(cond)
# 获得pos样本和part样本的label
label_picked = tf.gather(label_int,picked)
#获得pos和part的预测值
pred_picked = tf.gather(pred,picked)
with tf.Session() as sess:
print("pred:%s" % (sess.run(pred)))
print("label_int:%s" % (sess.run(label_int)))
print("cond:%s" % (sess.run(cond)))
print("picked:%s" % (sess.run(picked)))
print("label_picked:%s" % (sess.run(label_picked)))
print("pred_picked:%s" % (sess.run(pred_picked)))
#通过tf.equal()函数返回的True或者False值得到网络的预测值是否准确
#将True和Flase转化为1和0求得平均值即得到准确率
t = tf.cast(tf.equal(label_picked,pred_picked),tf.float32)
accuracy_op = tf.reduce_mean(t)
with tf.Session() as sess:
print("t:%s" % (sess.run(t)))
print("accuracy_op:%s" % sess.run(accuracy_op))
return accuracy_op
cls_prob = tf.random_uniform([2,2],0,1,seed = 100)
with tf.Session() as sess:
print("cls_prob:%s"%(sess.run(cls_prob)))
label = np.array([1, 0])
cal_accuracy(cls_prob,label)
