Compatibility with Tensorflow 2.0

I’m trying to create a model using keras-self-attentionon Google colab, and since the default Tensorflow version is 2.0 now, this error prompt :

model = models.Sequential()
model.add( Embedding(max_features, 32))
model.add(Bidirectional( LSTM(32, return_sequences=True)))
# adding an attention layer
model.add(SeqWeightedAttention())

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
/usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py in _get_default_graph()
     65     try:
---> 66         return tf.get_default_graph()
     67     except AttributeError:

AttributeError: module 'tensorflow' has no attribute 'get_default_graph'

During handling of the above exception, another exception occurred:

RuntimeError                              Traceback (most recent call last)
4 frames
<ipython-input-7-9c4e625938a2> in <module>()
      3 model.add(Bidirectional( LSTM(32, return_sequences=True)))
      4 # adding an attention layer
----> 5 model.add(SeqWeightedAttention())

/usr/local/lib/python3.6/dist-packages/keras_self_attention/seq_weighted_attention.py in __init__(self, use_bias, return_attention, **kwargs)
     10 
     11     def __init__(self, use_bias=True, return_attention=False, **kwargs):
---> 12         super(SeqWeightedAttention, self).__init__(**kwargs)
     13         self.supports_masking = True
     14         self.use_bias = use_bias

/usr/local/lib/python3.6/dist-packages/keras/engine/base_layer.py in __init__(self, **kwargs)
    130         if not name:
    131             prefix = self.__class__.__name__
--> 132             name = _to_snake_case(prefix) + '_' + str(K.get_uid(prefix))
    133         self.name = name
    134 

/usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py in get_uid(prefix)
     84     """
     85     global _GRAPH_UID_DICTS
---> 86     graph = _get_default_graph()
     87     if graph not in _GRAPH_UID_DICTS:
     88         _GRAPH_UID_DICTS[graph] = defaultdict(int)

/usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py in _get_default_graph()
     67     except AttributeError:
     68         raise RuntimeError(
---> 69             'It looks like you are trying to use '
     70             'a version of multi-backend Keras that '
     71             'does not support TensorFlow 2.0. We recommend '

**RuntimeError: It looks like you are trying to use a version of multi-backend Keras that does not support TensorFlow 2.0. We recommend using `tf.keras`, or alternatively, downgrading to TensorFlow 1.14.**

[CyberZHG]

@grkhr @Juhong-Namgung

The codes were always tested with the latest version of TensorFlow: https://travis-ci.org/github/CyberZHG/keras-self-attention/jobs/707084673#L2342

What were your error log?

[Juhong-Namgung]

@mohamedScikitLearn My working code is as follows:

from tensorflow.keras.layers import *
import os
os.environ['TF_KERAS'] = '1'

model = tf.keras.Sequential()
    model.add(Embedding(input_dim=max_vocab_len, output_dim=emb_dim, input_length=max_len, embeddings_regularizer=W_reg))
    model.add(Dropout(0.2))
    model.add(LSTM(units=128, return_sequences=True))
    model.add(Dropout(0.5))
    model.add(Layer(SeqSelfAttention(attention_activation='relu')))
    model.add(Flatten())
    model.add(Dense(9472, activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(21, activation='softmax'))

I think you should add tensorflow.keras.layers.Layer()