Tensorflow計算正確率、精確率、召回率

二分類模型的評價指標

https://www.cnblogs.com/xiaoniu-666/p/10511694.html

參考tf的方法

predictions = tf.argmax(predict, 1) actuals = tf.argmax(real, 1)

ones_like_actuals = tf.ones_like(actuals) zeros_like_actuals = tf.zeros_like(actuals) ones_like_predictions = tf.ones_like(predictions) zeros_like_predictions = tf.zeros_like(predictions)

Lable:      1   1 0 0 predi: 1   0   0   1 Tp Fp Tn Fn tp: = and       1 tn = ont(or)            1 lab-pred:       0   1   0   -1 lab-pred>=0.6:  0   1 0 0 fp = and(lable, lab-pred): 0 1   0   1 lab-pred<=-1.0: 0   0   0   1
    not-lable:      0   0   1   1 fn = and(not-lable, lab-pred<-1.0)

可能用到的方法：

tf.less_equal tf.less tf.greater_equal tf.greater
count_nonzero

參考：

https://blog.csdn.net/sinat_35821976/article/details/81334181

https://tensorflow.google.cn/api_docs/python/tf/math/count_nonzero