Применение слоев Flipout к DQN Keras

Question

Я создал DQN, используя tf-keras, и теперь я хотел бы расширить DQN, добавив несколько байесовских откидных слоев, используя вероятность тензорного потока.Однако я получаю сообщение об ошибке.Я думаю, что ошибка связана с тем, что я использую слои тензорного потока без области имен, но я не уверен, где я должен поместить область имен (я новичок в tf, так что ...)

Код DQN:

class DQN:
    def __init__(self, state_size, action_size):
        self.state_size = state_size
        self.action_size = action_size
        self.memory = deque(maxlen=2000)
        self.gamma = 0.95    # discount rate
        self.epsilon = 0.81  # exploration rate
        self.epsilon_min = 0.2
        self.epsilon_decay = 0.965
        self.lr = 0.001
        self.model = self._build_model()
        self.optimizer = Adam(lr=self.lr)
    def _build_model(self):
        model = Sequential()
##        model.add(tfp.layers.Convolution2DFlipout(2, kernel_size=5,
##                            activation='relu'
##                        ))
        model.add(tf.layers.Dropout(rate=0.3))
        model.add(Flatten())
        #model.add(tfp.layers.DenseFlipout(self.action_size * 4, activation='relu'))
        #model.add(tfp.layers.DenseFlipout(self.action_size * 3, activation='relu'))
        model.add(tfp.layers.DenseFlipout(self.action_size * 2, activation='relu'))
        model.add(tfp.layers.DenseFlipout(self.action_size))

        return model

    def remember(self, state, action, reward, next_state, done):
        self.memory.append((state, action, reward, next_state, done))

    def act(self, state):
       if np.random.rand() <= self.epsilon:
            return random.randrange(self.action_size)
       else:
            act_values = self.model.predict(state)
            return np.argmax(act_values[0])  # returns action

    def replay(self, batch_size):
        minibatch = random.sample(self.memory, batch_size)
        for state, action, reward, next_state, done in minibatch:
            target = reward
            if not done:
                target = (reward + self.gamma * np.amax(self.model.predict(next_state)[0]))
            target_f = self.model.predict(state)
            target_f[0][action] = target
            self.history = self.model.fit(state, target_f, epochs=1, verbose=0)
        if self.epsilon > self.epsilon_min:
            self.epsilon *= self.epsilon_decay
        loss = self.history.history['loss'][0]
        return loss

    def load(self, name):
        self.model.load_weights(name)

    def save(self, name):
        self.model.save_weights(name)

    def initialize(self):
        self.model.compile(loss='mean_squared_logarithmic_error', optimizer = self.optimizer)

Сообщение об ошибке:

Traceback (most recent call last):
  File "thoughtmouse.py", line 57, in <module>
    combined()
  File "thoughtmouse.py", line 18, in combined
    state_action_reward_loop(agent)
  File "thoughtmouse.py", line 30, in state_action_reward_loop
    rewards = agent.replay(1)
  File "/home/ai/Downloads/ScreenMouse/bdqn.py", line 88, in replay
    target = (reward + self.gamma * np.amax(self.model.predict(next_state)[0]))
  File "/home/ai/anaconda3/envs/drl/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py", line 1096, in predict
    x, check_steps=True, steps_name='steps', steps=steps)
  File "/home/ai/anaconda3/envs/drl/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py", line 2289, in _standardize_user_data
    self._set_inputs(cast_inputs)
  File "/home/ai/anaconda3/envs/drl/lib/python3.6/site-packages/tensorflow/python/training/checkpointable/base.py", line 442, in _method_wrapper
    method(self, *args, **kwargs)
  File "/home/ai/anaconda3/envs/drl/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py", line 2529, in _set_inputs
    outputs = self.call(inputs, training=training)
  File "/home/ai/anaconda3/envs/drl/lib/python3.6/site-packages/tensorflow/python/keras/engine/sequential.py", line 233, in call
    inputs, training=training, mask=mask)
  File "/home/ai/anaconda3/envs/drl/lib/python3.6/site-packages/tensorflow/python/keras/engine/sequential.py", line 253, in _call_and_compute_mask
    with ops.name_scope(layerf.epsilon_min:
            self.epsilon *= self.epsilon_decay
        loss = self.history.history['loss'][0]
        return loss

    def load(self, name):
        self.model.load_weights(name)
._name_scope()):
  File "/home/ai/anaconda3/envs/drl/lib/python3.6/site-packages/tensorflow/python/layers/base.py", line 284, in _name_scope
    return self._current_scope.original_name_scope
AttributeError: 'NoneType' object has no attribute 'original_name_scope'