keras: weights saved/loaded incorrectly when weights frozen

Given this code:

vgg = VGG16(weights='imagenet', include_top=False, input_shape=(360, 480, 3))

for layer in vgg.layers[:-4]:
    layer.trainable = False

inputs = Input(shape=(360, 480, 3))

encoder = vgg(inputs)
encoder = Flatten()(encoder)
encoder = Dense(16)(encoder)
encoder = Model(inputs, encoder)

in1 = Input(shape=(360, 480, 3))

comparator = Model(in1, encoder(in1))

comparator.save_weights('h.h5')
comparator.load_weights('h.h5')

I get this error:

---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
<ipython-input-54-a45f35902442> in <module>()
     16 
     17 comparator.save_weights('h.h5')
---> 18 comparator.load_weights('h.h5')

c:\users\seanh\appdata\local\programs\python\python36\lib\site-packages\keras\engine\network.py in load_weights(self, filepath, by_name, skip_mismatch, reshape)
   1178             else:
   1179                 saving.load_weights_from_hdf5_group(
-> 1180                     f, self.layers, reshape=reshape)
   1181 
   1182     def _updated_config(self):

c:\users\seanh\appdata\local\programs\python\python36\lib\site-packages\keras\engine\saving.py in load_weights_from_hdf5_group(f, layers, reshape)
    914                                                        original_keras_version,
    915                                                        original_backend,
--> 916                                                        reshape=reshape)
    917         if len(weight_values) != len(symbolic_weights):
    918             raise ValueError('Layer #' + str(k) +

c:\users\seanh\appdata\local\programs\python\python36\lib\site-packages\keras\engine\saving.py in preprocess_weights_for_loading(layer, weights, original_keras_version, original_backend, reshape)
    555         weights = convert_nested_time_distributed(weights)
    556     elif layer.__class__.__name__ in ['Model', 'Sequential']:
--> 557         weights = convert_nested_model(weights)
    558 
    559     if original_keras_version == '1':

c:\users\seanh\appdata\local\programs\python\python36\lib\site-packages\keras\engine\saving.py in convert_nested_model(weights)
    531                     weights=weights[:num_weights],
    532                     original_keras_version=original_keras_version,
--> 533                     original_backend=original_backend))
    534                 weights = weights[num_weights:]
    535 

c:\users\seanh\appdata\local\programs\python\python36\lib\site-packages\keras\engine\saving.py in preprocess_weights_for_loading(layer, weights, original_keras_version, original_backend, reshape)
    555         weights = convert_nested_time_distributed(weights)
    556     elif layer.__class__.__name__ in ['Model', 'Sequential']:
--> 557         weights = convert_nested_model(weights)
    558 
    559     if original_keras_version == '1':

c:\users\seanh\appdata\local\programs\python\python36\lib\site-packages\keras\engine\saving.py in convert_nested_model(weights)
    543                     weights=weights[:num_weights],
    544                     original_keras_version=original_keras_version,
--> 545                     original_backend=original_backend))
    546                 weights = weights[num_weights:]
    547         return new_weights

c:\users\seanh\appdata\local\programs\python\python36\lib\site-packages\keras\engine\saving.py in preprocess_weights_for_loading(layer, weights, original_keras_version, original_backend, reshape)
    672                                  str(weights[0].size) + '. ')
    673             weights[0] = np.reshape(weights[0], layer_weights_shape)
--> 674         elif layer_weights_shape != weights[0].shape:
    675             weights[0] = np.transpose(weights[0], (3, 2, 0, 1))
    676             if layer.__class__.__name__ == 'ConvLSTM2D':

IndexError: list index out of range

but if the trainable = False part is taken out:

vgg = VGG16(weights='imagenet', include_top=False, input_shape=(360, 480, 3))

# for layer in vgg.layers[:-4]:
#     layer.trainable = False

inputs = Input(shape=(360, 480, 3))

encoder = vgg(inputs)
encoder = Flatten()(encoder)
encoder = Dense(16)(encoder)
encoder = Model(inputs, encoder)

in1 = Input(shape=(360, 480, 3))

comparator = Model(in1, encoder(in1))

comparator.save_weights('h.h5')
comparator.load_weights('h.h5')

No error occurs and it works. It also works if the comparator is omitted and just the encoder is saved and loaded:

vgg = VGG16(weights='imagenet', include_top=False, input_shape=(360, 480, 3))

for layer in vgg.layers[:-4]:
    layer.trainable = False

inputs = Input(shape=(360, 480, 3))

encoder = vgg(inputs)
encoder = Flatten()(encoder)
encoder = Dense(16)(encoder)
encoder = Model(inputs, encoder)

encoder.save_weights('h.h5')
encoder.load_weights('h.h5')

It seems like in the error case it is failing to save/load the nontrainable weights, but I don’t see why that would be the case.

To put it into a question, what is the cause of this error and how can I get around it without skipping the comparator?