thal | Getting started with Puppeteer and Chrome Headless | Scraper library

You can use the following code

If check read_fwf:

Read a table of fixed-width formatted lines into DataFrame.

So if there is separator , use read_csv:

In your hyperparameters grid:

UITextView responds to both single tap gestures (which let you follow a link) and double tap gestures (which let you select text). Immediately after you tap the link once, it's unclear whether you've completed your gesture or whether a second tap is coming. Only after a short delay with no second tap can it be sure that you were in fact doing a single tap at which point textView(_:shouldInteractWith:in:interaction:) is called.

Unfortunately there's no standard way to make UITextView allow following links without allowing text selection. You might be able to hunt through the gesture recognizers registered on the view and find the one responsible for recognizing double taps and disable it, but doing so could have unintended side effects.

Try this :

I am very surprised with this recent fashion in Tensorflow tutorials to go with a linear activation function in the model last layer (Dense(1)) for classification problems, and then ask for from_logits=True in the loss function. I guess the reason is that it may lead to better numeric stability, as claimed in the documentation:

from_logits: Whether to interpret y_pred as a tensor of logit values. By default, we assume that y_pred contains probabilities (i.e., values in [0, 1]). Note: Using from_logits=True may be more numerically stable.

Where "by defaul" means that the default value of the loss function argument here is from_logits=False.

In any case, you have ended up with predictions of logits, and not probabilities as was normally the case so far in similar tutorials (and in practice). And the problem with logits is exactly that they lack an intuitive interpretation, in contrast with probabilistic predictions.

What you should do is pass your logits from a sigmoid function to convert them to probabilities:

This is because you apply the filter after the batch. Hence, in the lambda expression, x is a batch with shape (None,) (pass drop_reminder=True to batch to get shape of (20,)), and not a sample. To fix it, you have to call filter before batch.

There is a solution to "filter" after the batch, using a map instead. However as you can see, this has a side effect of making batches of variables size: you get a batch of 20 in the input and you remove elements not matching a specific criteria (trestbps < 135), not removing the same number of elements from each batch. Moreover this solution is performing very bad...

This is a very simple fix.

You need all the columns from df in x except the target column, for that, just do:

This worked for me: