Trax training loop stops after 1 epoch even after setting n_steps to 10 - trax
Please run the linked notebook. My trax training loop stops after 1 epoch without any error messages. Please advice where am I making a mistake.
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How can I make a complex ifelse algorithm which comprehend dates and time?

I have got a data-management problem. I have a database where "EDSS.1","EDSS.2",... represent a numeric variable, scaled from 0 to 10 (0.5 scale), where higher number stand for higher disability. For each EDSS variable, I have a "VISITDATE.1", "VISITDATE.2",...
Now I am interested in assessing the CONFIRMED DISABILITY PROGRESSION (CDP), which is an increased i 1 poin on the EDSS. To make things more difficult, this increment need to be confirmed in the subsequent visit (e.g. EDSS.3) which has to be >= 6 months (which is, VISITDATE.3 - VISITDATE.2 > 6 months.
To do so, I am creating a nested ifelse statement, as showed below.
prova <- prova %>% mutate(
CDP = ifelse(EDSS.2 > EDSS.1 & EDSS.3>=EDSS.2 & difftime(VISITDATE.3,VISITDATE.2,
units = "weeks") > 48,
However, I am facing the following main problems:
How can I print the VISIT.DATE of my interest instead of 1 or 0?
How can I shift my code to the EDSS.2,EDSS.3, and so on? I am interested in finding all the confirmed disability progressions (CDPs).
Many thanks to everyone who find the time to answer me.

Why this command isn't possible in RiscV?

In my textbook I saw the following question:
Can we implement the following operation (changes are allowed):
dam rd ,rs, imm # reg[rd]=mem[mem[rs+imm]]
In Riscv-Single Cycle
In RiscV Multi Cycle - Pipeline
The answer for both was wrong, can someone explain why?
For 1 the book wrote because we can't read twice from memory in single cycle but what's the problem? I know we can't write twice because each time we want to write the clock should be up.

How to set compare value in AVR CTC mode

I'm trying to learn how to use the CTC mode in AVR and I'm trying to figure out why I should minus 1 when I set the comparing value.
For instance,now I'm working to send the working time of the AVR every 200 ms and with the calculation I find out the value should be 50000,however in a video about this program,the man claims that it should be 50000-1,why is that?
It's because the underlying counter starts at zero and ends at "TOP" as it's referred to in the various AVR datasheets. Without know the exact part you're using, I can't refer you to a datasheet, but they all use basically the same terminology in their matrix of timer modes.
So if you want the counter to count exactly 50000 times, you have to subtract one. CTC means "timer on compare match" and starting at zero means you need to be "off by one." Simple as that.
Really good discussion of this and other AVR topics at AVRGeeks

My Doc2Vec code, after many loops/epochs of training, isn't giving good results. What might be wrong?

I'm training a Doc2Vec model using the below code, where tagged_data is a list of TaggedDocument instances I set up before:
max_epochs = 40
model = Doc2Vec(alpha=0.025,
for epoch in range(max_epochs):
print('iteration {0}'.format(epoch))
# decrease the learning rate
model.alpha -= 0.001
# fix the learning rate, no decay
model.min_alpha = model.alpha"d2v.model")
print("Model Saved")
When I later check the model results, they're not good. What might have gone wrong?
Do not call .train() multiple times in your own loop that tries to do alpha arithmetic.
It's unnecessary, and it's error-prone.
Specifically, in the above code, decrementing the original 0.025 alpha by 0.001 forty times results in (0.025 - 40*0.001) -0.015 final alpha, which would also have been negative for many of the training epochs. But a negative alpha learning-rate is nonsensical: it essentially asks the model to nudge its predictions a little bit in the wrong direction, rather than a little bit in the right direction, on every bulk training update. (Further, since model.iter is by default 5, the above code actually performs 40 * 5 training passes – 200 – which probably isn't the conscious intent. But that will just confuse readers of the code & slow training, not totally sabotage results, like the alpha mishandling.)
There are other variants of error that are common here, as well. If the alpha were instead decremented by 0.0001, the 40 decrements would only reduce the final alpha to 0.021 – whereas the proper practice for this style of SGD (Stochastic Gradient Descent) with linear learning-rate decay is for the value to end "very close to 0.000"). If users start tinkering with max_epochs – it is, after all, a parameter pulled out on top! – but don't also adjust the decrement every time, they are likely to far-undershoot or far-overshoot 0.000.
So don't use this pattern.
Unfortunately, many bad online examples have copied this anti-pattern from each other, and make serious errors in their own epochs and alpha handling. Please don't copy their error, and please let their authors know they're misleading people wherever this problem appears.
The above code can be improved with the much-simpler replacement:
max_epochs = 40
model = Doc2Vec() # of course, if non-default parameters needed, use them here
# most users won't need to change alpha/min_alpha at all
# but many will want to use more than default `epochs=5`
model.train(tagged_data, total_examples=model.corpus_count, epochs=max_epochs)"d2v.model")
Here, the .train() method will do exactly the requested number of epochs, smoothly reducing the internal effective alpha from its default starting value to near-zero. (It's rare to need to change the starting alpha, but even if you wanted to, just setting a new non-default value at initial model-creation is enough.)
Also: note that later calls to infer_vector() will reuse the epochs specified at the time of model-creation. If nothing is specified, the default epochs=5 will be used - which is often smaller than is best for training or inference. So if you find a larger number of epochs (such as 10, 20 or more) is better for training, remember to also use at least the same number of epochs for inference. (.infer_vector() takes an optional epochs parameter whihc can override any value set at model-contruction.

How to benchmark Matlab processes?

Searching for an idea how to avoid using loop in my Matlab code, I found following comments under one question on SE:
The statement "for loops are slow in Matlab" is no longer generally true since Matlab...euhm, R2008a?
Have you tried to benchmark a for loop vs what you already have? sometimes it is faster than vectorized code...
So I would like to ask, is there commonly used way to test the speed of a process in Matlab? Can user see somewhere how much time the process takes or the only way is to extend the processes for several minutes in order to compare the times between each other?
The best tool for testing the performance of MATLAB code is Steve Eddins' timeit function, available here from the MATLAB Central File Exchange.
It handles many subtle issues related to benchmarking MATLAB code for you, such as:
ensuring that JIT compilation is used by wrapping the benchmarked code in a function
warming up the code
running the code several times and averaging
Update: As of release R2013b, timeit is part of core MATLAB.
Update: As of release R2016a, MATLAB also includes a performance testing framework that handles the above issues for you in a similar way to timeit.
You can use the profiler to assess how much time your functions, and the blocks of code within them, are taking.
>> profile on; % Starts the profiler
>> myfunctiontorun( ); % This can be a function, script or block of code
>> profile viewer; % Opens the viewer showing you how much time everything took
Viewer also clears the current profile data for next time.
Bear in mind, profile does tend to slow execution a bit, but I believe it does so in a uniform way across everything.
Obviously if your function is very quick, you might find you don't get reliable results so if you can run it many times or extend the computation that would improve matters.
If it's really simple stuff you're testing, you can also just time it using tic and toc:
>> tic; % Start the timer
>> myfunctionname( );
>> toc; % End the timer and display elapsed time
Also if you want multiple timers, you can assign them to variables:
>> mytimer = tic;
>> myfunctionname( );
>> toc(mytimer);
Finally, if you want to store the elapsed time instead of display it:
>> myresult = toc;
I think that I am right to state that many of us time Matlab by wrapping the block of code we're interested in between tic and toc. Furthermore, we take care to ensure that the total time is of the order of 10s of seconds (rather than 1s of seconds or 100s of seconds) and repeat it 3 - 5 times and take some measure of central tendency (such as the mean) and draw our conclusions from that.
If the piece of code takes less than, say 10s, then repeat it as many times as necessary to bring it into the range, being careful to avoid any impact of one iteration on the next. And if the code naturally takes 100s of seconds or longer, either spend longer on the testing or try it with artificially small input data to run more quickly.
In my experience it's not necessary to run programs for minutes to get data on average run time with acceptably low variance. If I run a program 5 times and one (or two) of the results is wildly different from the mean I'll re-run it.
Of course, if the code has any features which make its run time non-deterministic then it's a different matter.