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