AttributeError: объект 'Tensor' не имеет атрибута 'assign' в модели внимания

Я пытаюсь построить модель классификации документов с вниманием, используя keras (часть модели в статье «Иерархические сети внимания для классификации документов»). Следующие коды являются тестовыми кодами. Я создаю birnn и настраиваемый слой внимания ссылаюсь на https://github.com/person-lee/LSTM_ATTENTION_CLASSIFY/blob/master/utils.py и https://github.com/richliao/textClassifier/blob/master/textClassifierHATT.py. Но у меня возникла ошибка (подробности см. Ниже).

Коды:

from keras.models import Model
from keras.layers import Input
from keras.layers.embeddings import Embedding
from keras.layers.recurrent import GRU
from keras.layers.wrappers import Bidirectional, TimeDistributed
from keras.layers.core import Dropout, Dense, Lambda, Masking
from keras.layers import merge
from keras.engine.topology import Layer

from keras import backend as K
from keras import initializers
import keras
class AttentionLayer(Layer):
    '''
    Attention layer. 
    '''
    def __init__(self, init='glorot_uniform', **kwargs):
        super(AttentionLayer, self).__init__(**kwargs)
        self.supports_masking = True
        self.init = initializers.get(init)

    def build(self, input_shape):
        input_dim = input_shape[-1]
        self.Uw = self.init((input_dim, ))
        self.trainable_weights = [self.Uw]
        super(AttentionLayer, self).build(input_shape)  

    def compute_mask(self, input, mask):
        return mask

    def call(self, x, mask=None):
        eij = K.tanh(K.squeeze(K.dot(x, K.expand_dims(self.Uw)), axis=-1))
        ai = K.exp(eij)
        weights = ai/K.expand_dims(K.sum(ai, axis=1),1)

        weighted_input = x*K.expand_dims(weights,2)
        return K.sum(weighted_input, axis=1)

    def get_output_shape_for(self, input_shape):
        newShape = list(input_shape)
        newShape[-1] = 1
        return tuple(newShape)

sentence_input = Input(shape=(None,5))
# embedded_sequences = embedding_layer(sentence_input)
l_lstm = Bidirectional(GRU(10, return_sequences=True),merge_mode='concat')(sentence_input)
# l_dense = TimeDistributed(Dense(200))(l_lstm)
l_att = AttentionLayer()(l_lstm)
cls = Dense(10, activation='softmax')(l_att)
sentEncoder = Model(sentence_input, cls)

sentEncoder.compile(loss='categorical_crossentropy',
              optimizer='rmsprop',
              metrics=['acc'])
import numpy as np
x_train = np.array([[1,2,3,4,5],
                    [1,2,3,4,5],
                    [1,2,3,4,5],
                    [1,2,3,4,5],
                    [1,2,3,4,5],
                    [1,2,3,4,5],
                    [1,2,3,4,5],
                    [1,2,3,4,5],
                    [1,2,3,4,5],
                    [1,2,3,4,5]])
y_train = np.array([1,2,3,4,5,6,7,8,9,0])
y_train = keras.utils.to_categorical(y_train, 10)
x_train = np.expand_dims(x_train,0)
y_train = np.expand_dims(y_train,0)

sentEncoder.fit(x=x_train,y=y_train,validation_split=0.1)

И получили следующие ошибки:

AttributeError                            Traceback (most recent call last)
<ipython-input-13-3f6bb30d8618> in <module>()
----> 1 sentEncoder.fit(x=x_train,y=y_train,validation_split=0.1)

~/.conda/envs/21/lib/python3.6/site-packages/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)
   1011         else:
   1012             ins = x + y + sample_weights
-> 1013         self._make_train_function()
   1014         f = self.train_function
   1015 

~/.conda/envs/21/lib/python3.6/site-packages/keras/engine/training.py in _make_train_function(self)
    495                     training_updates = self.optimizer.get_updates(
    496                         params=self._collected_trainable_weights,
--> 497                         loss=self.total_loss)
    498                 updates = (self.updates +
    499                            training_updates +

~/.conda/envs/21/lib/python3.6/site-packages/keras/legacy/interfaces.py in wrapper(*args, **kwargs)
     89                 warnings.warn('Update your `' + object_name +
     90                               '` call to the Keras 2 API: ' + signature, stacklevel=2)
---> 91             return func(*args, **kwargs)
     92         wrapper._original_function = func
     93         return wrapper

~/.conda/envs/21/lib/python3.6/site-packages/keras/optimizers.py in get_updates(self, loss, params)
    262                 new_p = p.constraint(new_p)
    263 
--> 264             self.updates.append(K.update(p, new_p))
    265         return self.updates
    266 

~/.conda/envs/21/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py in update(x, new_x)
    968         The variable `x` updated.
    969     """
--> 970     return tf.assign(x, new_x)
    971 
    972 

~/.conda/envs/21/lib/python3.6/site-packages/tensorflow/python/ops/state_ops.py in assign(ref, value, validate_shape, use_locking, name)
    282         ref, value, use_locking=use_locking, name=name,
    283         validate_shape=validate_shape)
--> 284   return ref.assign(value, name=name)
    285 
    286 

AttributeError: 'Tensor' object has no attribute 'assign'

Я понятия не имею, что не так. Я погуглил и спросил у людей, хорошо разбирающихся в этом, но не понял. Это из-за bidirectional? Кто-нибудь знает, что происходит не так?