multi_2.py

# Now generate TPR, FPR and ROC data

tpr,fpr,roc_auc = ([[]]*number_of_classes for _ in range(3))

f,ax = plt.subplots()


for i in range(number_of_classes):
    fpr[i], tpr[i], _ = roc_curve(labels==i, predictions[:, i])
    roc_auc[i] = auc(fpr[i], tpr[i])
    ax.plot(fpr[i],tpr[i])

plt.legend(['Class {:d}'.format(d) for d in range(number_of_classes)])

plt.xlabel('FPR')

plt.ylabel('TPR')