tuning hyperparameters using bayesian optimisation, optimiser not recognised

Question

I am using Baesian optimisation to tune my model however however I am getting an error for all the optimisers. here is my code:

# Create function
from keras.layers import BatchNormalization
from keras.optimizers import Ftrl as LegacyNadam 
def nn_cl_bo2(neurons, activation, optimizer, learning_rate, batch_size, epochs,
              layers1, layers2, normalization, dropout, dropout_rate):
    optimizerL = ['SGD', 'Adam', 'RMSprop', 'Adadelta', 'Adagrad', 'Adamax', 'Nadam', 'Ftrl','SGD']
    optimizerD= {'Adam':Adam(learning_rate=learning_rate), 'SGD':SGD(learning_rate=learning_rate),
                 'RMSprop':RMSprop(learning_rate=learning_rate), 'Adadelta':Adadelta(learning_rate=learning_rate),
                 'Adagrad':Adagrad(learning_rate=learning_rate), 'Adamax':Adamax(learning_rate=learning_rate),
                 'Nadam':Nadam(learning_rate=learning_rate), 'Ftrl':Ftrl(learning_rate=learning_rate)}
    activationL = ['relu', 'sigmoid', 'softplus', 'softsign', 'tanh', 'selu',
                   'elu', 'exponential','relu']
    neurons = round(neurons)
    activation = activationL[round(activation)]
    optimizer = optimizerD[optimizerL[round(optimizer)]]
    batch_size = round(batch_size)
    epochs = round(epochs)
    layers1 = round(layers1)
    layers2 = round(layers2)
    def nn_cl_fun():
        nn = Sequential()
        nn.add(Dense(neurons, input_dim=30, activation=activation))
        if normalization > 0.5:
            nn.add(BatchNormalization())
        for i in range(layers1):
            nn.add(Dense(neurons, activation=activation))
        if dropout > 0.5:
            nn.add(Dropout(dropout_rate, seed=123))
        for i in range(layers2):
            nn.add(Dense(neurons, activation=activation))
        nn.add(Dense(1, activation='sigmoid'))
        nn.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy'])
        return nn
    es = EarlyStopping(monitor='accuracy', mode='max', verbose=0, patience=20)
    nn = KerasClassifier(model=nn_cl_fun, epochs=epochs, batch_size=batch_size, verbose=0)
    kfold = StratifiedKFold(n_splits=5, shuffle=True, random_state=123)
    score = cross_val_score(nn, X_train, y_train, scoring=score_acc, cv=kfold, fit_params={'callbacks':[es]}).mean()
    return score

This is where I pass my parameters

params_nn2 ={
    'neurons': (10, 100),
    'activation':(0, 8),
    'optimizer':(0,6),
    'learning_rate':(0.01, 1),
    'batch_size':(200, 1000),
    'epochs':(20, 100),
    'layers1':(1,3),
    'layers2':(1,3),
    'normalization':(0,1),
    'dropout':(0,1),
    'dropout_rate':(0,0.3)
}
# Run Bayesian Optimization
nn_bo = BayesianOptimization(nn_cl_bo2, params_nn2, random_state=111)
nn_bo.maximize(init_points=25, n_iter=4)

this is my error message

KeyError: 'The optimizer cannot recognize variable dense_5585/kernel:0. This usually means you are trying to call the optimizer to update different parts of the model separately. Please call optimizer.build(variables) with the full list of trainable variables before the training loop or use legacy optimizer `tf.keras.optimizers.legacy.Nadam.'

I get this same error for all optimiser types that I have inputted above