Keras to CoreML conversion fails: 'InputLayer' object is not iterable

Code Block 
for dense_layer in dense_layers:
    for layer_size in layer_sizes:
        for conv_layer in conv_layers:
            NAME = "{}-conv-{}-nodes-{}-dense-{}".format(conv_layer, layer_size, dense_layer, int(time.time()))
            print(NAME)
            i = i + 1
            print(i)
            model = Sequential()
            model.add(Conv2D(8, (5, 5), padding='same', activation='relu', input_shape=X.shape[1:]))
            model.add(MaxPooling2D(pool_size=(2, 2), strides=(2,2), padding='same'))
            model.add(Dropout(0.2))
            model.add(Conv2D(layer_size, (3, 3), padding='same', activation='relu'))
            model.add(MaxPooling2D(pool_size=(2, 2), strides=(2,2), padding='same'))
            model.add(Dropout(0.2))
            for l in range(conv_layer-1):
                model.add(Conv2D(layer_size, (3, 3), padding='same', activation='relu'))
                model.add(MaxPooling2D(pool_size=(2, 2), strides=(2,2), padding='same'))
                model.add(Dropout(0.2))
            model.add(Flatten())
            for _ in range(dense_layer):
                model.add(Dense(layer_size))
                model.add(Activation('relu'))
            model.add(Dense(CLASSNAME_SIZE))
            model.add(Activation('softmax'))
            tensorboard = TensorBoard(log_dir="logs/{}".format(NAME))
            
            model.compile(loss='sparse_categorical_crossentropy',
              optimizer='sgd',
              metrics=['accuracy'])
            
            model.fit(X, Y, batch_size=32, epochs=1, validation_data=(X_val,Y_val), shuffle=True, callbacks=[tensorboard])
            model.save('traffic_signsv8.model')
            model.save('traffic_signsv8.h5')
            model = load_model('traffic_signsv8.h5')
            coremlModel = coremltools.converters.keras.convert(model, input_names = 'image', image_input_names = 'image', input_name_shape_dict={'input_1:0': [3, 48, 48, 1]}, image_scale=1.0/255.0)
            coremlModel.save('traffic_signsv8.mlmodel')
            spec = coremltools.utils.load_spec("traffic_signsv8.mlmodel")
            input = spec.description.input[0]
            input.type.imageType.colorSpace = ft.ImageFeatureType.RGB
            input.type.imageType.height = 48 
            input.type.imageType.width = 48
            coremltools.utils.save_spec(spec, "try.mlmodel")