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I want to fine tuning my L2 parameter in my last keras layer using a for loop approach. My target is build a Extreme Machine Learning model. Now, I'm using the code below:

#possible values for L2... 
k = 2^(seq(-20,-1,1)) 

#vectors with metrics 
acc_vector = vector('numeric',length(k)) 
loss_vector = vector('numeric',length(k))

for(i in seq_along(k))

model0 = keras_model_sequential() %>% 
 layer_dense(units = 500,activation = 'relu',input_shape = c(784),
 trainable = F,name = 'dense1') %>% 
 layer_dense(units = 10, activation = 'softmax',
 kernel_regularizer = regularizer_l2(k[i]),name='dense2') %>% 
 compile(loss = 'categorical_crossentropy',optimizer = optimizer_rmsprop(),
 metrics = c('accuracy'))

model0 %>% fit(
 x_train, y_train, 
 epochs = 5, batch_size = 512, 
 validation_split = 0.2,verbose=0)

eval = model0 %>% evaluate(x_test, y_test)

acc_vector[i] = eval$acc
loss_vector[i] = eval$loss

#I don't know why, but without the next 2 lines, my memory usage increase 2 times
rm(model0,eval)
gc()

So, here is my problem. With this aproach (run fast, at least), my weights start by random in each loop and the value of L2 doesn't make any sense. I tried other approachs like include weights = "weights" in the first layer and worked fine, except by the process time... it increased a lot! After this, I tried to pop the last layer and add a new layer with the new L2 as follow:

model0 = keras_model_sequential() %>% 
 layer_dense(units = 500,activation = 'relu',input_shape = c(784),
 trainable = F,name = 'dense1') %>% 
 layer_dense(units = 10, activation = 'softmax',
 kernel_regularizer = regularizer_l2('any value'),name='dense2')

for(i in seq_along(k))
 model0 %>% pop_layer() %>% 
 layer_dense(units = 10, activation = 'softmax',
 kernel_regularizer = regularizer_l2(k[i]),name='dense2')

But doesn't work. The behavior of the last approach makes the models have just the first layer. I just want change the value of L2 to retrain the last layer of my model. How can I do that in a simple way?