Running keras.Sequential model on GPU

I am using Keras.Sequential model. I want to run it on GPU as the processing is taking a lot of time. I am using kaggle to run my code. I have set this configuration on Kaggle but whenever my model runs, it keeps using CPU and GPU usage does not changes.

import keras
import tensorflow as tf

config = tf.compat.v1.ConfigProto( device_count = {'GPU': 1,'CPU': 56 } )
config = tf.compat.v1.ConfigProto(log_device_placement=True )

sess = tf.compat.v1.Session(config=config) 
keras.backend.set_session(sess)

Here is my model class:

class DQNAgent(nn.Module):
    def __init__(self, state_size, action_size):
        super().__init__()
        self.state_size = state_size
        self.action_size = action_size
        self.memory = deque(maxlen=2000)
        self.gamma = 0.95  # discount rate
        self.epsilon = 1.0  # exploration rate
        self.epsilon_min = 0.01
        self.epsilon_decay = 0.995
        self.learning_rate = 0.001
        self.model = self._build_model()
    def _build_model(self):
        # Neural Net for Deep-Q learning Model
        model = Sequential()
        model.add(Dense(24, input_dim=100, activation='relu'))
        model.add(Dense(24, activation='relu'))
        model.add(Dense(self.action_size, activation='linear'))
        model.compile(loss='mse',
                      optimizer=Adam(lr=self.learning_rate))
        
        return model
    def memorize(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)
        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.model.fit(state, target_f, epochs=1, verbose=0)
        if self.epsilon > self.epsilon_min:
            self.epsilon *= self.epsilon_decay
    def load(self, name):
        self.model.load_weights(name)
    def save(self, name):
        torch.save(name)
        print()