Elastic-Net | fast version of elastic net r

I have a beautiful mlr3 ensemble model (combined glmnet and glm) for binary prediction, see details here

I'm trying to set up a local Kibana instance with ActiveMQ for testing purposes. I've created a docker network called elastic-network. I have 3 containers in my network: elasticsearch, kibana and finally activemq. In my kibana container, I downloaded metric beats using the following shell command

I'm pretty new to dash and I'm trying to figure out how do I place names above my dropdown menus and sliders and provide some gap between the them. I'm geeting these names "Dataset","model types" on the side instead of on the top of the dropdowns.This is the code I have been using :

I'm trying to use the Elastic-Net algorithm implemented in Cleverhans to generate adversarial samples in a classification task. The main problem is that i'm trying to use it in a way to obtain an higher confidence at classification time on a target class (different from the original one) but i'm not able to reach good results. The system that i'm trying to fool is a DNN with a softmax output on 10 classes.

For instance:

1. Given a sample of class 3 i want to generate an adversarial sample of class 0.
2. Using the default hyperparameters implemented in the ElasticNetMethod of cleverhans i'm able to obtain a succesful attack, so the class assigned to the adversarial sample became the class 0, but the confidence is quite low(about 30%). This also happens trying different values for the hyperparameters.
3. My purpose is to obtain a quite higher confidence (at least 90%).
4. For other algorithm like "FGSM" or "MadryEtAl" i'm able to reach this purpose creating a loop in which the algorithm is applied until the sample is classified as the target class with a confidence greater than 90%, but i can't to apply this iteration on the EAD algorithm because at each step of the iteration it yields the adversarial sample generated at the first step, and in the following iterations it remains unchanged. (I know that this may happens because the algorithm is different from the other two metioned, but i'm trying to find a solution to reach my purpose).

This is the code that i'm actually using to generate adversarial samples.

I am running elastic net regularization in caret using glmnet.

I pass sequence of values to trainControl for alpha and lambda, then I perform repeatedcv to get the optimal tunings of alpha and lambda.

Here is an example where the optimal tunings for alpha and lambda are 0.7 and 0.5 respectively:

I'm try to code Elastic-Net. It's look likes:

And I want to use this loss function into Keras:

Is Lasso regression or Elastic-net regression always better than the ridge regression?

I'm a newbie in machine learning. I've conducted these regressions on a few data sets and I've always got the same result that the mean squared error is the least in lasso regression. Is this a mere coincidence or is this true in any case?