tensorFlowNetwork.py 文件源码

def trainAndCrossValidate(self):
        num_folds = min(self.num_cross_folds, len(self.crossVal_X))
        accs = []
        aucs = []
        f1s = []
        precisions = []
        recalls = []
        for f in range(num_folds):
            val_X = self.crossVal_X[f]
            val_Y = self.crossVal_y[f]
            train_folds_X = [self.crossVal_X[x] for x in range(num_folds) if x != f]
            train_folds_Y = [self.crossVal_y[x] for x in range(num_folds) if x != f]
            train_X = train_folds_X[0]
            train_Y = train_folds_Y[0]
            for i in range(1,len(train_folds_X)):
                train_X = np.concatenate((train_X,train_folds_X[i]))
                train_Y = np.concatenate((train_Y,train_folds_Y[i]))

            self.train_X = train_X
            self.train_y = train_Y
            self.val_X = val_X
            self.val_y = val_Y
            acc, auc, f1, precision, recall = self.trainAndValidate()
            accs.append(acc)
            aucs.append(auc)
            f1s.append(f1)
            precisions.append(precision)
            recalls.append(recall)
        if PRINT_CROSS_VAL_FOLDS: print "\t\tPer-fold cross-validation accuracy: ", accs
        return np.nanmean(accs), np.nanmean(aucs), np.nanmean(f1s), np.nanmean(precisions), np.nanmean(recalls)