无法使用自定义距离函数加载带有lambda函数的keras模型

我正在尝试构建一个系统,使用连体LSTM模型检查句子的相似性,该模型使用曼哈顿距离作为距离函数,同时合并两个层。 我正在使用本文中的代码

https://medium.com/mlreview/implementing-malstm-on-kaggles-quora-question-pairs-competition-8b31b0b16a07

问题是,在我将模型构建并保存在json文件中后,由于抛出错误,我无法加载该模型

未定义名称“指数负曼哈顿距离”

以下是代码:

# Model variables
n_hidden = 50
gradient_clipping_norm = 1.25
batch_size = 64
n_epoch = 5

def exponent_neg_manhattan_distance(left, right):
    ''' Helper function for the similarity estimate of the LSTMs outputs'''
    return K.exp(-K.sum(K.abs(left-right), axis=1, keepdims=True))

# The visible layer
left_input = Input(shape=(max_seq_length,), dtype='int32')
right_input = Input(shape=(max_seq_length,), dtype='int32')

embedding_layer = Embedding(len(embeddings), embedding_dim, weights=[embeddings], input_length=max_seq_length, trainable=False)

# Embedded version of the inputs
encoded_left = embedding_layer(left_input)
encoded_right = embedding_layer(right_input)

# Since this is a siamese network, both sides share the same LSTM
shared_lstm = LSTM(n_hidden)

left_output = shared_lstm(encoded_left)
right_output = shared_lstm(encoded_right)

# Calculates the distance as defined by the MaLSTM model
malstm_distance = Merge(mode=lambda x: exponent_neg_manhattan_distance(x[0], x[1]), output_shape=lambda x: (x[0][0], 1))([left_output, right_output])

# Pack it all up into a model
malstm = Model([left_input, right_input], [malstm_distance])

# Adadelta optimizer, with gradient clipping by norm
optimizer = Adadelta(clipnorm=gradient_clipping_norm)

malstm.compile(loss='mean_squared_error', optimizer=optimizer, metrics=['accuracy'])

# Start training
training_start_time = time()

malstm_trained = malstm.fit([X_train['left'], X_train['right']], Y_train, batch_size=batch_size, nb_epoch=n_epoch,
                            validation_data=([X_validation['left'], X_validation['right']], Y_validation))

print("Training time finished.\n{} epochs in {}".format(n_epoch, datetime.timedelta(seconds=time()-training_start_time)))

malstm.save('malstm.h5')
model_json = malstm.to_json()

with open ('malstm.json', 'w') as file:
    file.write(model_json)

malstm.save_weights('malst_w.h5')

现在,当我尝试加载模型时,我得到以下错误:

model = model_from_json(open('malstm.json').read(), custom_objects = {"exponent_neg_manhattan_distance":exponent_neg_manhattan_distance})
C:\Users\archi\Miniconda3\lib\site-packages\keras\engine\topology.py:1271: UserWarning: The `Merge` layer is deprecated and will be removed after 08/2017. Use instead layers from `keras.layers.merge`, e.g. `add`, `concatenate`, etc.
  return cls(**config)
Traceback (most recent call last):

  File "<ipython-input-12-4c72a4db6c29>", line 1, in <module>
    model = model_from_json(open('malstm.json').read(), custom_objects = {"exponent_neg_manhattan_distance":exponent_neg_manhattan_distance})

  File "C:\Users\archi\Miniconda3\lib\site-packages\keras\models.py", line 349, in model_from_json
    return layer_module.deserialize(config, custom_objects=custom_objects)

  File "C:\Users\archi\Miniconda3\lib\site-packages\keras\layers\__init__.py", line 55, in deserialize
    printable_module_name='layer')

  File "C:\Users\archi\Miniconda3\lib\site-packages\keras\utils\generic_utils.py", line 144, in deserialize_keras_object
    list(custom_objects.items())))

  File "C:\Users\archi\Miniconda3\lib\site-packages\keras\engine\topology.py", line 2524, in from_config
    process_node(layer, node_data)

  File "C:\Users\archi\Miniconda3\lib\site-packages\keras\engine\topology.py", line 2483, in process_node
    layer(input_tensors, **kwargs)

  File "C:\Users\archi\Miniconda3\lib\site-packages\keras\engine\topology.py", line 619, in __call__
    output = self.call(inputs, **kwargs)

  File "C:\Users\archi\Miniconda3\lib\site-packages\keras\legacy\layers.py", line 209, in call
    return self.mode(inputs, **arguments)

  File "<ipython-input-19-913812c640b3>", line 28, in <lambda>

NameError: name 'exponent_neg_manhattan_distance' is not defined

我在网上搜索过,这个问题可能是因为使用了lambda函数。我有没有办法加载这个模型,因为它花了大量的时间来训练。任何帮助都将不胜感激!