tape | Tasks Assessing Protein Embeddings , a set | Machine Learning library

I had a similar problem and was solved when I turned of Automatically refresh canvas

I regard this as a major bug in String(localizable:). If we were using NSLocalizedString, we would have individual key: and value: parameters. String(localizable:) needs that.

I can think of two workarounds. One is: don't use String(localizable:). Just keep on using NSLocalizedString.

The other is to localize explicitly for English. Instead of entering the English user-facing text as the localized: parameter, enter a key string. Then, to prevent the keys from appearing in the user interface, export the English localization and "translate" the keys into the desired English user-facing text. Now import the localization to generate the correct English .strings files.

(If your development language isn't English, substitute the development language into those instructions.)

Now when you export a different localization, such as French, the element's id value is the key, to which the translator pays no attention, and the is the English, which the translator duly translates.

To change the English user-facing text later on, edit the English Localizable.strings file — not the code. Nothing will break because the key remains constant.

We may consider to paste/unite the columns and then convert to JSON with toJSON - as the columns have different case, it may be be better to standardize by converting to lower case (tolower - it becomes easier to get the corresponding column value from the _units column). Loop across the columns (length:height), paste (str_c), the corresponding '_units' column values when the value in the column is non-NA (using case_when), then unite those column to a single column, select the columns of interest and convert to JSON (toJSON)

Wrap with list on the output of fromJSON, then use unnest_wider to create separate columns from the named list, join the columns to 'option_2' by coalesceing the 'bubble_height' and 'color'

Since the result of filter method is an array, you can map over your filtered array and extract just username property, like this:

First, you can directly access the dictionary fields as members of the Quote objects - i.e. you can just do q.ask_price to get the price from the received Quotes.

In your quote_callback function you get every Quote object as an argument. So, if you always only want to access the last received Quote object which has symbol = "HB", you could define a global dictionary named e.g. quotes which will contain one key == symbol and value == last Quote with that symbol per symbol type.

Now, in get_quote you can use a simple dictionary lookup for the desired key in the quotes dictionary. In the end, you need to repeatedly call get_quote until a quote object with the desired symbol value is received. The whole code could look like this:

but what is the purpose of scanf then?

An excellent question.

Is it simply an useless broken function that should never be used?

It is almost useless. It is, arguably, quite broken. It should almost never be used.

Why is it in the libraries to begin with then?

My personal belief is that it was an experiment. It tries to be the opposite of printf. But that turned out not to be such a good idea in practice, and the function never got used very much, and pretty much fell out of favor, except for one particular use case...

This seems really absurd, especially considering all beginners are taught to use scanf...

You're absolutely right. It is really quite absurd.

There's a reason all beginners are taught to use scanf. During week 1 of your first C programming class, you might write the little program

Gradient tape triggers automatic differentiation which requires tracking gradients on all your weights and activations. Autodiff requires multiple more memory. This is normal. You'll have to manually tune your batch size until you find one that works, then tune your LR. Usually, the tune just means guess & check or grid search. (I am working on a product to do all of that for you but I'm not here to plug it).

Interesting question! I think the error originates from retracing, which causes the tf.function to evaluate the python snippets in energy more than once. See this issue. Also, this could be related to a bug.

A couple observations:

1. Removing the tf.function decorator from calc_sw3 works and is consistent with the docs:

[...] tf.function applies to a function and all other functions it calls.

So if you apply tf.function explicitly to calc_sw3 again, you may trigger a retracing, but then you may wonder why calc_sw3_noerr works? That is, it must have something to do with the variable gamma.

2. Adding input signatures to the tf.function above the energy function, while leaving the rest of the code the way it is, also works: