Question: 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'. 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 MODEL: 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])  ERROR: 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 <module> 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'``` 

Answer: You are mixing imports between keras and tf.keras, they are not the same library and doing this is not supported. You should make all imports from one of the libraries, either keras or tf.keras.