out = torch.Tensor([[0,3],
     [2,3],
     [1,0],
     [3,4]])
cond = torch.Tensor([[1,0],
      [0,1],
      [1,0],
      [1,0]])
 
persent = torch.mean(torch.eq(torch.argmax(out, dim=1), torch.argmax(cond, dim=1)).double())
print(persent)

out = [[0, 3],
  [2, 3],
  [1, 0],
  [3, 4]]
cond = [[1, 0],
  [0, 1],
  [1, 0],
  [1, 0]] 
a = np.argmax(out,axis=1)
b = np.argmax(cond, axis=1)
persent = np.mean(np.equal(a, b) + 0)
# persent = np.mean(a==b + 0)
print(persent)

IMAGE y_real y_predict 0其他 1豹纹 2弥漫 3斑片 4黄斑
/mnt/AI/HM/izy20200531c5/299/train/0其他/IM005111 (Copy).jpg 0 0 1 8.31E-19 7.59E-13 4.47E-15 2.46E-14
/mnt/AI/HM/izy20200531c5/299/train/0其他/IM005201 (Copy).jpg 0 0 1 5.35E-17 4.38E-11 8.80E-13 3.85E-11
/mnt/AI/HM/izy20200531c5/299/train/0其他/IM004938 (4) (Copy).jpg 0 0 1 1.20E-16 3.17E-11 6.26E-12 1.02E-11
/mnt/AI/HM/izy20200531c5/299/train/0其他/IM004349 (3) (Copy).jpg 0 0 1 5.66E-14 1.87E-09 6.50E-09 3.29E-09
/mnt/AI/HM/izy20200531c5/299/train/0其他/IM004673 (5) (Copy).jpg 0 0 1 5.51E-17 9.30E-12 1.33E-13 2.54E-12
/mnt/AI/HM/izy20200531c5/299/train/0其他/IM004450 (5) (Copy).jpg 0 0 1 4.81E-17 3.75E-12 3.96E-13 6.17E-13

from sklearn.metrics import precision_recall_curve
import numpy as np
from matplotlib import pyplot
from sklearn.metrics import f1_score
from sklearn.metrics import roc_curve, auc

n_classes=PM_y.shape[1]
fpr = dict()
tpr = dict()
roc_auc = dict()
for i in range(n_classes):
 fpr[i], tpr[i], _ = roc_curve(true_y[:, i], PM_y[:, i])
 roc_auc[i] = auc(fpr[i], tpr[i])

from scipy import interp
# First aggregate all false positive rates
all_fpr = np.unique(np.concatenate([fpr[i] for i in range(n_classes)]))
 
# Then interpolate all ROC curves at this points
mean_tpr = np.zeros_like(all_fpr)
for i in range(n_classes):
 mean_tpr += interp(all_fpr, fpr[i], tpr[i])
 
# Finally average it and compute AUC
mean_tpr /= n_classes
 
fpr["macro"] = all_fpr
tpr["macro"] = mean_tpr
roc_auc["macro"] = auc(fpr["macro"], tpr["macro"])

import matplotlib.pyplot as plt
from itertools import cycle
from matplotlib.ticker import FuncFormatter
lw = 2
# Plot all ROC curves
plt.figure()
labels=['Category 0','Category 1','Category 2','Category 3','Category 4']
plt.plot(fpr["macro"], tpr["macro"],
   label='macro-average ROC curve (area = {0:0.4f})'
    ''.format(roc_auc["macro"]),
   color='navy', linestyle=':', linewidth=4)
 
colors = cycle(['aqua', 'darkorange', 'cornflowerblue','blue','yellow'])
for i, color in zip(range(n_classes), colors):
 plt.plot(fpr[i], tpr[i], color=color, lw=lw,
    label=labels[i]+'(area = {0:0.4f})'.format(roc_auc[i]))
 
plt.plot([0, 1], [0, 1], 'k--', lw=lw)
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('1-Specificity (%)')
plt.ylabel('Sensitivity (%)')
plt.title('Some extension of Receiver operating characteristic to multi-class')
def to_percent(temp, position):
 return '%1.0f'%(100*temp)
plt.gca().yaxis.set_major_formatter(FuncFormatter(to_percent))
plt.gca().xaxis.set_major_formatter(FuncFormatter(to_percent))
plt.legend(loc="lower right")
plt.show()