Python 3.7.4 TensorFlow2.0 TensorBoardで 'MergeSummary'の入力エラー

現在以下のTensorFlow1.xのコードをTensorFlow2.0用に書き換えを行っています。
https://github.com/ChengBinJin/V-GAN-tensorflow

その際
"TypeError: Tensors in list passed to 'inputs' of 'MergeSummary' Op have types [bool, bool, bool, bool, bool, bool, bool] that do not match expected type string."というエラーが出てしまい、解決できずにいます。
詳細は以下に示します。

ValueError: Tensor conversion requested dtype string for Tensor with dtype bool: <tf.Tensor 'auc_pr_summary/write_summary/Const:0' shape=() dtype=bool>

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File "main.py", line 48, in <module>
app.run(main)
File "/home/miura/anaconda3/lib/python3.7/site-packages/absl/app.py", line 299, in run
_run_main(main, args)
File "/home/miura/anaconda3/lib/python3.7/site-packages/absl/app.py", line 250, in _run_main
sys.exit(main(argv))
File "main.py", line 40, in main
solver = Solver(FLAGS)
File "/home/miura/200208_深層学習修正/codes/solver.py", line 31, in init
self.model = CGAN(self.sess, self.flags, self.dataset.image_size)
File "/home/miura/200208_深層学習修正/codes/model.py", line 30, in init
self._init_assign_op() # initialize assign operations
File "/home/miura/200208_深層学習修正/codes/model.py", line 116, in _init_assign_op
sensitivity_summ, specificity_summ, score_summ])
File "/home/miura/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/summary/summary.py", line 371, in merge
val = _gen_logging_ops.merge_summary(inputs=inputs, name=name)
File "/home/miura/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/ops/gen_logging_ops.py", line 530, in merge_summary
"MergeSummary", inputs=inputs, name=name)
File "/home/miura/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/framework/op_def_library.py", line 493, in _apply_op_helper
(prefix, dtype.name))
TypeError: Tensors in list passed to 'inputs' of 'MergeSummary' Op have types [bool, bool, bool, bool, bool, bool, bool] that do not match expected type string.

以下コード(足りない部分があれば、お伝えいだだけると追記いたします。)

Python
1#main.py
2import os
3import tensorflow as tf
4from solver import Solver
5from absl import app
6from absl import flags
7
8FLAGS = flags.FLAGS
9
10'''
11ここにコマンドライン引数をflags.DEFINE_integer, flags.DEFINE_string等で定義するが略
12'''
13
14def main(_):
15    os.environ['CUDA_VISIBLE_DEVICES'] = FLAGS.gpu_index
16
17    solver = Solver(FLAGS)
18    if FLAGS.is_test:
19        solver.test()
20    if not FLAGS.is_test:
21        solver.train()
22
23
24if __name__ == '__main__':
25    app.run(main)
26

Python
1#solver.py
2import os
3import time
4import collections
5import numpy as np
6import matplotlib.pyplot as plt
7import matplotlib.gridspec as gridspec
8import tensorflow as tf
9from PIL import Image
10
11from dataset import Dataset
12# noinspection PyPep8Naming
13import TensorFlow_utils as tf_utils
14import utils as utils
15from model import CGAN
16from tensorflow.compat.v1 import ConfigProto
17from tensorflow.compat.v1 import Session
18
19class Solver(object):
20    def __init__(self, flags):
21        run_config = ConfigProto()
22        run_config.gpu_options.allow_growth = True
23        self.sess = Session(config=run_config)
24
25        self.flags = flags
26        self.dataset = Dataset(self.flags.dataset, self.flags)
27        self.model = CGAN(self.sess, self.flags, self.dataset.image_size)
28
29        self.best_auc_sum = 0.
30        self._make_folders()
31
32        self.saver = tf.train.Saver()
33        self.sess.run(tf.global_variables_initializer())
34
35        tf_utils.show_all_variables()
36'''
37以下メソッド略
38'''

Python
1import tensorflow as tf
2# noinspection PyPep8Naming
3import TensorFlow_utils as tf_utils
4import keras
5from tensorflow.compat.v1 import placeholder
6from tensorflow.compat.v1 import variable_scope
7from tensorflow.compat.v1 import layers
8from tensorflow.compat.v1 import trainable_variables
9from tensorflow.compat.v1.train import AdamOptimizer
10from tensorflow.compat.v1.summary import FileWriter
11from tensorflow.compat.v1.summary import merge_all
12
13class CGAN(object):
14    def __init__(self, sess, flags, image_size):
15        tf.compat.v1.disable_eager_execution()
16        self.sess = sess
17        self.flags = flags
18        self.image_size = image_size
19
20        self.alpha_recip = 1. / self.flags.ratio_gan2seg if self.flags.ratio_gan2seg > 0 else 0
21        self._gen_train_ops, self._dis_train_ops = [], []
22        self.gen_c, self.dis_c = 32, 32
23
24        self._build_net()
25        self._init_assign_op()  # initialize assign operations
26
27        print('Initialized CGAN SUCCESS!\n')
28
29def _init_assign_op(self):
30        self.best_auc_sum_placeholder = placeholder(tf.float32, name='best_auc_sum_placeholder')
31        self.auc_pr_placeholder = placeholder(tf.float32, name='auc_pr_placeholder')
32        self.auc_roc_placeholder = placeholder(tf.float32, name='auc_roc_placeholder')
33        self.dice_coeff_placeholder = placeholder(tf.float32, name='dice_coeff_placeholder')
34        self.acc_placeholder = placeholder(tf.float32, name='acc_placeholder')
35        self.sensitivity_placeholder = placeholder(tf.float32, name='sensitivity_placeholder')
36        self.specificity_placeholder = placeholder(tf.float32, name='specificity_placeholder')
37        self.score_placeholder = placeholder(tf.float32, name='score_placeholder')
38
39        self.best_auc_sum = tf.Variable(0., trainable=False, dtype=tf.float32, name='best_auc_sum')
40        auc_pr = tf.Variable(0., trainable=False, dtype=tf.float32, name='auc_pr')
41        auc_roc = tf.Variable(0., trainable=False, dtype=tf.float32, name='auc_roc')
42        dice_coeff = tf.Variable(0., trainable=False, dtype=tf.float32, name='dice_coeff')
43        acc = tf.Variable(0., trainable=False, dtype=tf.float32, name='acc')
44        sensitivity = tf.Variable(0., trainable=False, dtype=tf.float32, name='sensitivity')
45        specificity = tf.Variable(0., trainable=False, dtype=tf.float32, name='specificity')
46        score = tf.Variable(0., trainable=False, dtype=tf.float32, name='score')
47
48        self.best_auc_sum_assign_op = self.best_auc_sum.assign(self.best_auc_sum_placeholder)
49        auc_pr_assign_op = auc_pr.assign(self.auc_pr_placeholder)
50        auc_roc_assign_op = auc_roc.assign(self.auc_roc_placeholder)
51        dice_coeff_assign_op = dice_coeff.assign(self.dice_coeff_placeholder)
52        acc_assign_op = acc.assign(self.acc_placeholder)
53        sensitivity_assign_op = sensitivity.assign(self.sensitivity_placeholder)
54        specificity_assign_op = specificity.assign(self.specificity_placeholder)
55        score_assign_op = score.assign(self.score_placeholder)
56
57        self.measure_assign_op = tf.group(auc_pr_assign_op, auc_roc_assign_op, dice_coeff_assign_op,
58                                          acc_assign_op, sensitivity_assign_op, specificity_assign_op,
59                                          score_assign_op)
60
61        # for tensorboard
62        if not self.flags.is_test:
63            self.writer = FileWriter("{}/logs/{}_{}_{}".format(
64                self.flags.dataset, self.flags.discriminator, self.flags.train_interval, self.flags.batch_size))
65
66        auc_pr_summ = tf.summary.scalar("auc_pr_summary", auc_pr)
67        auc_roc_summ = tf.summary.scalar("auc_roc_summary", auc_roc)
68        dice_coeff_summ = tf.summary.scalar("dice_coeff_summary", dice_coeff)
69        acc_summ = tf.summary.scalar("acc_summary", acc)
70        sensitivity_summ = tf.summary.scalar("sensitivity_summary", sensitivity)
71        specificity_summ = tf.summary.scalar("specificity_summary", specificity)
72        score_summ = tf.summary.scalar("score_summary", score)
73
74        self.measure_summary = merge([auc_pr_summ, auc_roc_summ, dice_coeff_summ, acc_summ, sensitivity_summ, specificity_summ, score_summ])
75'''
76以下メソッド略
77'''
78
79