title: Keras and TensorBoard - AttributeError: 'Sequential' object has no attribute '_get_distribution_strategy'
body: <p>I am using keras and trying to plot the logs using tensorboard. Bellow you can find out the error I am getting and also the list of packages versions I am using. I can not understand it is giving me the error of 'Sequential' object has no attribute '_get_distribution_strategy'.</p>
  
  <p>Package:
  Keras                2.3.1
  Keras-Applications   1.0.8
  Keras-Preprocessing  1.1.0
  tensorboard          2.1.0
  tensorflow           2.1.0
  tensorflow-estimator 2.1.0</p>
  
  <p><strong>MODEL:</strong></p>
  
  <pre><code>model = Sequential()
      model.add(Embedding(MAX_NB_WORDS, EMBEDDING_DIM, input_shape=(X.shape[1],)))
      model.add(GlobalAveragePooling1D())
      #model.add(Dense(10, activation='sigmoid'))
      model.add(Dense(len(CATEGORIES), activation='softmax'))
      model.summary()
      #opt = 'adam'       # Here we can choose a certain optimizer for our model
      opt = 'rmsprop'
      model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy'])                  # Here we choose the loss function, input our optimizer choice, and set our metrics.
  
      # Create a TensorBoard instance with the path to the logs directory
      tensorboard = TensorBoard(log_dir='logs/{}'.format(time()),
                      histogram_freq = 1,
                      embeddings_freq = 1,
                      embeddings_data = X)
  
      history = model.fit(X, Y, epochs=epochs, batch_size=batch_size, validation_split=0.1, callbacks=[tensorboard])
  </code></pre>
  
  <p><strong>ERROR:</strong></p>
  
  <pre><code>C:\Users\Bruno\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\callbacks\tensorboard_v2.py:102: UserWarning: The TensorBoard callback does not support embeddings display when using TensorFlow 2.0. Embeddings-related arguments are ignored.
    warnings.warn('The TensorBoard callback does not support '
  C:\Users\Bruno\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow_core\python\framework\indexed_slices.py:433: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.
    "Converting sparse IndexedSlices to a dense Tensor of unknown shape. "
  Train on 1123 samples, validate on 125 samples
  Traceback (most recent call last):
    File ".\NN_Training.py", line 128, in &lt;module&gt;
      history = model.fit(X, Y, epochs=epochs, batch_size=batch_size, validation_split=0.1, callbacks=[tensorboard])    # Feed in the train
  set for X and y and run the model!!!
    File "C:\Users\Bruno\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\engine\training.py", line 1239, in fit
      validation_freq=validation_freq)
    File "C:\Users\Bruno\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\engine\training_arrays.py", line 119, in fit_loop
      callbacks.set_model(callback_model)
    File "C:\Users\Bruno\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\callbacks\callbacks.py", line 68, in set_model
      callback.set_model(model)
    File "C:\Users\Bruno\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\callbacks\tensorboard_v2.py", line 116, in set_model
      super(TensorBoard, self).set_model(model)
    File "C:\Users\Bruno\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow_core\python\keras\callbacks.py", line 1532, in
  set_model
      self.log_dir, self.model._get_distribution_strategy())  # pylint: disable=protected-access
  AttributeError: 'Sequential' object has no attribute '_get_distribution_strategy'```
  </code></pre>