kerasで学習させたh5モデルからmlmodelへの変換しようとするとエラーがでる

前提・実現したいこと

kerasで学習させたh5モデルからcoremltoolsを使ったmlmodelへの変換

発生している問題・エラーメッセージ

Traceback (most recent call last):
  File "converter.py", line 8, in <module>
    class_labels='labels.txt')
  File "/anaconda3/envs/tf-200/lib/python3.6/site-packages/coremltools/converters/keras/_keras_converter.py", line 745, in convert
    custom_conversion_functions=custom_conversion_functions)
  File "/anaconda3/envs/tf-200/lib/python3.6/site-packages/coremltools/converters/keras/_keras_converter.py", line 543, in convertToSpec
    custom_objects=custom_objects)
  File "/anaconda3/envs/tf-200/lib/python3.6/site-packages/coremltools/converters/keras/_keras2_converter.py", line 182, in _convert
    model = _keras.models.load_model(model, custom_objects = custom_objects)
  File "/anaconda3/envs/tf-200/lib/python3.6/site-packages/keras/models.py", line 233, in load_model
    model = model_from_config(model_config, custom_objects=custom_objects)
  File "/anaconda3/envs/tf-200/lib/python3.6/site-packages/keras/models.py", line 307, in model_from_config
    return layer_module.deserialize(config, custom_objects=custom_objects)
  File "/anaconda3/envs/tf-200/lib/python3.6/site-packages/keras/layers/__init__.py", line 54, in deserialize
    printable_module_name='layer')
  File "/anaconda3/envs/tf-200/lib/python3.6/site-packages/keras/utils/generic_utils.py", line 139, in deserialize_keras_object
    list(custom_objects.items())))
  File "/anaconda3/envs/tf-200/lib/python3.6/site-packages/keras/models.py", line 1204, in from_config
    if 'class_name' not in config[0] or config[0]['class_name'] == 'Merge':
KeyError: 0

python
1path = './keras_cnn.h5'
2
3import coremltools
4coreml_model = coremltools.converters.keras.convert(
5    path,
6    input_names='image',
7    image_input_names='image',
8    class_labels='labels.txt')
9coreml_model.save('predict.mlmodel')

from keras.models import Sequential

from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense
from keras.utils import np_utils
import keras
import numpy as np


classes = ["perfect", "normal", "bad"]
num_classes = len(classes)
image_size = 75


def main():
    X_train, X_test, y_train, y_test = np.load("./Judgment.npy")
    X_train = X_train.astype("float") / 256
    X_test = X_test.astype("float") / 256
    y_train = np_utils.to_categorical(y_train, num_classes)
    y_test = np_utils.to_categorical(y_test, num_classes)

    model = model_train(X_train, y_train)
    model_eval(model, X_test, y_test)


def model_train(X, y):
    model = Sequential()
    model.add(Conv2D(32, (3, 3), padding='same', input_shape=X.shape[1:]))
    model.add(Activation('relu'))
    model.add(Conv2D(32, (3, 3)))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.25))

    model.add(Conv2D(64, (3, 3), padding='same'))
    model.add(Activation('relu'))
    model.add(Conv2D(64, (3, 3)))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.25))

    model.add(Flatten())
    model.add(Dense(512))
    model.add(Activation('relu'))
    model.add(Dropout(0.5))
    model.add(Dense(3))
    model.add(Activation('softmax'))

    opt = keras.optimizers.rmsprop(lr=0.0001, decay=1e-6)
    model.compile(
        loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy'])
    model.fit(X, y, batch_size=32, epochs=100)


    model.save('./keras_cnn.h5')

    return model


def model_eval(model, X, y):
    scores = model.evaluate(X, y, verbose=1)
    print('Test Loss: ', scores[0])
    print('Test Accuracy: ', scores[1])


if __name__ == "__main__":
    main()