给出以下代码：
var body = document.body,
html = document.documentElement;
var pageHeight = Math.max( body.scrollHeight, body.offsetHeight,
html.clientHeight, html.scrollHeight, html.offsetHeight );
$( document ).mousemove(function( event ) {
var h = event.pageY;
$('#rwMaskTop').css({"height":h-40});
$('#rwMaskBottom').css({"height":pageHeight-h-40});
});
我收到此错误：
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')
但是如果取出---------------------------------------------------------------------------
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
部分：
trainable = False没有错误发生，并且可以正常工作。如果省略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')
并且仅保存并加载comparator，它也可以工作：
encoder在错误情况下，似乎无法保存/加载不可训练的权重，但我不知道为什么会这样。
要提出一个问题，该错误的原因是什么？如何在不跳过比较器的情况下解决该错误？