我有predicted_y和real_y。
是否有更快的方法来获得准确性：
from keras import backend as K
accuracy_array = K.eval(keras.metrics.categorical_accuracy(real_y, predicted_y))
print(sum(accuracy_array)/len(accuracy_array))
答案 0 :(得分：2)
从accuracy_score尝试scikit-learn。
import numpy as np
from sklearn.metrics import accuracy_score
y_pred = [0, 2, 1, 3]
y_true = [0, 1, 2, 3]
accuracy_score(y_true, y_pred)
accuracy_score(y_true, y_pred, normalize=False)
答案 1 :(得分：1)
我建议您使用scikit-learn作为我的评论中提到的目的。
示例1 ：
from sklearn import metrics
results = metrics.accuracy_score(real_y, predicted_y)
precision，recall，f1-scores的分类报告。示例2：
from sklearn.metrics import classification_report
y_true = [0, 1, 2, 2, 2]
y_pred = [0, 0, 2, 2, 1]
target_names = ['class 0', 'class 1', 'class 2']
print(classification_report(y_true, y_pred, target_names=target_names))
precision recall f1-score support
class 0 0.50 1.00 0.67 1
class 1 0.00 0.00 0.00 1
class 2 1.00 0.67 0.80 3
avg / total 0.70 0.60 0.61 5
示例3：
from sklearn.metrics import confusion_matrix
y_true = [0, 1, 2, 2, 2]
y_pred = [0, 0, 2, 2, 1]
confusion_matrix(y_true, y_pred)
array([[1, 0, 0],
[1, 0, 0],
[0, 1, 2]])
答案 2 :(得分：0)
感谢seralouk，我发现：
from sklearn import metrics
metrics.accuracy_score(real_y.argmax(axis=1), predicted_y.argmax(axis=1))
答案 3 :(得分：0)
I wrote a Python lib for confusion matrix analysis, you can use it for your purpose.
>>> from pycm import *
>>> y_actu = [2, 0, 2, 2, 0, 1, 1, 2, 2, 0, 1, 2] # or y_actu = numpy.array([2, 0, 2, 2, 0, 1, 1, 2, 2, 0, 1, 2])
>>> y_pred = [0, 0, 2, 1, 0, 2, 1, 0, 2, 0, 2, 2] # or y_pred = numpy.array([0, 0, 2, 1, 0, 2, 1, 0, 2, 0, 2, 2])
>>> cm = ConfusionMatrix(actual_vector=y_actu, predict_vector=y_pred) # Create CM From Data
>>> cm.classes
[0, 1, 2]
>>> cm.table
{0: {0: 3, 1: 0, 2: 0}, 1: {0: 0, 1: 1, 2: 2}, 2: {0: 2, 1: 1, 2: 3}}
>>> print(cm)
Predict 0 1 2
Actual
0 3 0 0
1 0 1 2
2 2 1 3
Overall Statistics :
95% CI (0.30439,0.86228)
Bennett_S 0.375
Chi-Squared 6.6
Chi-Squared DF 4
Conditional Entropy 0.95915
Cramer_V 0.5244
Cross Entropy 1.59352
Gwet_AC1 0.38931
Joint Entropy 2.45915
KL Divergence 0.09352
Kappa 0.35484
Kappa 95% CI (-0.07708,0.78675)
Kappa No Prevalence 0.16667
Kappa Standard Error 0.22036
Kappa Unbiased 0.34426
Lambda A 0.16667
Lambda B 0.42857
Mutual Information 0.52421
Overall_ACC 0.58333
Overall_RACC 0.35417
Overall_RACCU 0.36458
PPV_Macro 0.56667
PPV_Micro 0.58333
Phi-Squared 0.55
Reference Entropy 1.5
Response Entropy 1.48336
Scott_PI 0.34426
Standard Error 0.14232
Strength_Of_Agreement(Altman) Fair
Strength_Of_Agreement(Cicchetti) Poor
Strength_Of_Agreement(Fleiss) Poor
Strength_Of_Agreement(Landis and Koch) Fair
TPR_Macro 0.61111
TPR_Micro 0.58333
Class Statistics :
Classes 0 1 2
ACC(Accuracy) 0.83333 0.75 0.58333
BM(Informedness or bookmaker informedness) 0.77778 0.22222 0.16667
DOR(Diagnostic odds ratio) None 4.0 2.0
ERR(Error rate) 0.16667 0.25 0.41667
F0.5(F0.5 score) 0.65217 0.45455 0.57692
F1(F1 score - harmonic mean of precision and sensitivity) 0.75 0.4 0.54545
F2(F2 score) 0.88235 0.35714 0.51724
FDR(False discovery rate) 0.4 0.5 0.4
FN(False negative/miss/type 2 error) 0 2 3
FNR(Miss rate or false negative rate) 0.0 0.66667 0.5
FOR(False omission rate) 0.0 0.2 0.42857
FP(False positive/type 1 error/false alarm) 2 1 2
FPR(Fall-out or false positive rate) 0.22222 0.11111 0.33333
G(G-measure geometric mean of precision and sensitivity) 0.7746 0.40825 0.54772
LR+(Positive likelihood ratio) 4.5 3.0 1.5
LR-(Negative likelihood ratio) 0.0 0.75 0.75
MCC(Matthews correlation coefficient) 0.68313 0.2582 0.16903
MK(Markedness) 0.6 0.3 0.17143
N(Condition negative) 9 9 6
NPV(Negative predictive value) 1.0 0.8 0.57143
P(Condition positive) 3 3 6
POP(Population) 12 12 12
PPV(Precision or positive predictive value) 0.6 0.5 0.6
PRE(Prevalence) 0.25 0.25 0.5
RACC(Random accuracy) 0.10417 0.04167 0.20833
RACCU(Random accuracy unbiased) 0.11111 0.0434 0.21007
TN(True negative/correct rejection) 7 8 4
TNR(Specificity or true negative rate) 0.77778 0.88889 0.66667
TON(Test outcome negative) 7 10 7
TOP(Test outcome positive) 5 2 5
TP(True positive/hit) 3 1 3
TPR(Sensitivity, recall, hit rate, or true positive rate) 1.0 0.33333 0.5
>>> cm.matrix()
Predict 0 1 2
Actual
0 3 0 0
1 0 1 2
2 2 1 3
>>> cm.normalized_matrix()
Predict 0 1 2
Actual
0 1.0 0.0 0.0
1 0.0 0.33333 0.66667
2 0.33333 0.16667 0.5
Link : PyCM