resemblance | trying shingling / resemblance / simhash / sketching to do

How can I migrate away from GoogleCredential to GoogleCredentials while still using the various APIs such as People API?

In other words: How can I avoid using any of those deprecated classes (GoogleCredential and GoogleApiClient), on Android, while still being able to use the various APIs?

Although you can make GoogleCredentials work directly, it will be better to use a class derived from GoogleCredentials such as UserCredentials which will accommodate token refresh as GoogleCredential does. GoogleCredentials is more of a foundational class.

The following code makes use of UserCredentials. This is mostly what you have presented, but I have changed some credential storing logic for the purpose of the demo. This code has no deprecated methods except startActivityForResult().

serverAuthCode is available from GoogleSignInAccount. Take a look at Moving Past GoogleApiClient on how to remove the dependency on GoogleApiClient. I have update my public gist of RestApiActivity from the Google signin quickstart which shows how to use GoogleOauthHelper as well as GoogleApi.

GoogleOauthHelper.kt

is it possible to do

No (without compiler extensions).

I am wondering about the standard / common practice in C to achieve this.

The common practice is to pass user context to some function using a void* argument, like in thrd_create(..., void *arg); or pthread_create(...., void *arg) or fopencookie(void *cookie, ...) or pthread_cleanup_push(..., void *arg) or on_exit(..., void *arg) etc. Maybe we will see the day when qsort_r will be integrated to the standard:

So it turns out that the issue is one of my own making in not keeping DAXStudio up to date.

Installing the latest version of DAXStudio shows much more sensible Dictionary sizes and a considerably smaller overall model size.

Ah well, there are worse ways to spend a couple hours...

The angle brackets are typically used to denote part of the output that is related not to values themselves but rather to metadata related to the values.

In your case - there is no good way to represent a function in Python without providing its definition. However, is a good way to print information referring to the function by providing its name and parameters.

There is no relation to C code other than maybe some convention but I am not aware of any.

Printing in the console of the representation of values of more complex objects in Python is handled by special ("magic") function __repr__ or __str__ depending on the situation.

Personally, I don't see the resemblance between my code and the sample code in the core guidelines

You have a single variable that is accessible to every thread, hidden from users of Example. The only difference to an ordinary global variable is that it is private, i.e. you can't use the name Example::examples_ to refer to it outside of Example.

Note

The rule is "avoid", not "don't use."

The "correct" way of implementing this functionality might be how you have it, but I strongly suggest you rework "each object needs access to information from all the other objects to perform some calculations" so that you pass a std::vector to where it is needed, having kept track of all the relevant (and especially alive) Examples where they are used.

Alternative: [...] Another solution is to define the data as the state of some object and the operations as member functions.

Warning: Beware of data races: If one thread can access non-local data (or data passed by reference) while another thread executes the callee, we can have a data race. Every pointer or reference to mutable data is a potential data race.

You can remove the white spaces from the names in the split regex.

So your code will become:

It achieves similar results.

I just received an e-mail confirming the error from Alex Alemi, Senior Research Scientist at Google and original publisher of the blog post regarding the release of the code for InceptionResnetV2. It seems that during internal experiments, the STEM blocks were switched and the release just kept like that.

Cite:

Dani Azemar,

It seems you're right. Not entirely sure what happened but the code is obviously the source of truth in the sense that the released checkpoint is for the code that is also released. When we were developing the architecture we did a whole slew of internal experiments and I imagine at some point the stems were switched. Not sure I have the time to dig deeper at the moment, but like I said, the released checkpoint is a checkpoint for the released code as you can verify yourself by running the evaluation pipeline. I agree with you that it seems like this is using the original Inception V1 stem. Best Regards,

Alex Alemi

I'll update this post with changes regarding this subject.

UPDATE: Christian Szegedy, also publisher of the original paper, just tweeted me:

The original experiments and model was created in DistBelief, a completely different framework pre-dating Tensorflow.

The TF version was added a year later and might have had discrepancies from the original model, however it was made sure to achieve similar results.

So, since it achieves similar results, your experiments would be roughly the same.

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