def error(self):
class_n = self.label.shape[1]
FPR_list = []
TPR_list = []
PPV_list = []
for i in range(class_n):
true = self.label[:, i]
prob = self.prediction[1][:, i]
FPR, TPR, PPV = ac(true, prob, 0.5)
FPR_list.append(FPR)
TPR_list.append(TPR)
PPV_list.append(PPV)
return FPR_list, TPR_list, PPV_list
def error(self, label, prediction):
with tf.device('/device:GPU:' + self.GPUID):
class_n = label.shape[1]
FPR_list = []
TPR_list = []
PPV_list = []
for i in range(class_n):
true = label[:, i]
prob = prediction[1][:, i]
FPR, TPR, PPV = ac(true, prob, 0.5)
FPR_list.append(FPR)
TPR_list.append(TPR)
PPV_list.append(PPV)
return FPR_list, TPR_list, PPV_list
def error(self):
with tf.device('/device:GPU:' + self.GPUID):
class_n = self.label.shape[1]
FPR_list = []
TPR_list = []
PPV_list = []
for i in range(class_n):
true = self.label[:, i]
prob = self.prediction[1][:, i]
FPR, TPR, PPV = ac(true, prob, 0.5000)
#FPR, _ = tf.metrics.false_positives_at_thresholds(true, prob, [0.5000])
#TPR, _ = tf.metrics.true_negatives_at_thresholds(true, prob, [0.5000])
#PPV, _ = tf.metrics.precision_at_thresholds(true, prob, [0.5000])
FPR_list.append(FPR)
TPR_list.append(TPR)
PPV_list.append(PPV)
return FPR_list, TPR_list, PPV_list
