Mirai | Source code for the Mirai botnet | Dataset library

Assuming you want to get these attributes regardless of tag type, you can query multiple selectors with querySelectorAll. You can also loop through the results with forEach.

You can target elements by attribute with CSS, you can read more about that here.

Modern browsers support looping through querySelectorAll results, but if you need old browser support check out this article for alternatives

I suggest:

It looks like you are trying to add the numbered-lines. Try looping over the albumList and concatenating to the final message to send like this:

What I would really like to see is where those redirects are sending you to. How about trying curl such as: curl --head --location http://example.com

This should work for you, you can tune various boost params around here and see how it affects your results

Try using this :

The /dev directory tree often uses a special filesystem that contains automatically-generated special device files; you shouldn't expect to be able to store general-purpose files there.

If you can install your application into "normal" directories (for example, put the main program into /usr/local/bin) that will be the easiest to run. If not, using a path directly in the filesystem root directory is common; use something like /mirai for your application, or something generic like /app.

I know that adding a dropout layer into a CNN model enhances accuracy, since it decrease the impact of over-fitting.

You can put it that way but it doesn't hold in general. Dropout layer is a generalization technique that decreases flexibility of your model which can prevent overfitting assuming that your model is flexible enough to handle the task (actually, assuming that your model is more flexible than needed). If your model is not capable of handling the task to begin with, meaning that it is too weak, then adding any kind of regularization will probably only worsen its performance.

That being said, CNN's usually perform better when you include more than just one convolutional layer. The idea is that deeper convolutional layers learn more complex features while the layers close to the input learn just a basic shapes (of course, this depends on the structure of the network itself and on the complexity of the task). And since you usually want to include more convolutional layers, the complexity (and flexibility) of such model raises which can lead to overfitting, therefore the need for regularization techniques. (3 convolutional layers with regularization will usually outperform one convolutional layer without regularization).

Your design only includes one convolutional layer. I would suggest stacking multiple convolutional/pooling layers on top of each other and adding some dropout layers to fight the overfitting if necessary (it is probably going to be hard to see any positive effects of regularization on such a simple model).