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High-accuracy time syncing between Android (Java) and .NET DateTime?


High-accuracy time syncing between Android (Java) and .NET DateTime?

By : Nitin Tyagi
Date : November 22 2020, 02:59 PM
Hope this helps Yes, I believe GregorianCalendar does indeed observe the Julian/Gregorian cutover - although I note that you're setting oldUtcTime to the 1st of February in 1AD, which is probably why you're seeing a difference of 20 days instead of 10 days.
The simplest way to do this is to find out the Unix epoch in milliseconds using .NET - and then use that as a constant on the Java side:
code :
// Handy method in .NET 4.6...
var unixEpoch = DateTimeOffset.FromUnixTimeSeconds(0);
Console.WriteLine(unixEpoch.Ticks / 10000L);
private static final long BCL_MILLISECONDS_AT_UNIX_EPOCH = 62135596800000L;
private static final long TICKS_PER_MILLISECOND = 10000L;
...

long bclMillis = System.currentTimeMillis() + BCL_MILLISECONDS_AT_UNIX_EPOCH;
long bclTicks = TICKS_PER_MILLISECOND * bclMillis;


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Cross-platform high-accuracy time in python

Cross-platform high-accuracy time in python


By : sus
Date : March 29 2020, 07:55 AM
Hope that helps Since you are using Pygame, you should use pygame's function for abstracting that:
just call pygame.time.get_ticks()- it will return the microseconds since the call to pygame.init()
Time syncing vs high latency

Time syncing vs high latency


By : Travja
Date : March 29 2020, 07:55 AM
I wish did fix the issue. Adaptive intervals
First of all, I would suggest that you decrease the amount of polling. I don't know about your server implementation, but the current setup will create a lot of requests once you have a couple of users.
code :
pollingInterval = secondsLeft / 100
Train Accuracy is very high, Validation accuracy is very high but the test set accuracy is very low

Train Accuracy is very high, Validation accuracy is very high but the test set accuracy is very low


By : diksen
Date : March 29 2020, 07:55 AM
like below fixes the issue First off, you need to make sure that when you split in data, the relative size of every class in the new datasets is equal. It can be imbalanced if that is the distribution of your initial data, but it must have the same imbalance in all datasets after the split.
Now, regarding the split. If you need a train, validation and test sets, they must all be independent of each-other (no-shared samples). This is important if you don't want to cheat yourself with the results that you are getting.
Validation loss oscillates a lot, validation accuracy > learning accuracy, but test accuracy is high. Is my model ove

Validation loss oscillates a lot, validation accuracy > learning accuracy, but test accuracy is high. Is my model ove


By : user2578012
Date : March 29 2020, 07:55 AM
fixed the issue. Will look into that further The training loss at each epoch is usually computed on the entire training set.
The validation loss at each epoch is usually computed on one minibatch of the validation set, so it is normal for it to be more noisey.
Why does binary accuracy give high accuracy while categorical accuracy give low accuracy, in a multi-class classificatio

Why does binary accuracy give high accuracy while categorical accuracy give low accuracy, in a multi-class classificatio


By : seniorwebmaster
Date : March 29 2020, 07:55 AM
Does that help So you need to understand what happens when you apply a binary_crossentropy to a multiclass prediction.
Let's assume that your output from softmax is (0.1, 0.2, 0.3, 0.4) and one-hot encoded ground truth is (1, 0, 0, 0). binary_crossentropy masks all outputs which are higher than 0.5 so out of your network is turned to (0, 0, 0, 0) vector. (0, 0, 0, 0) matches ground truth (1, 0, 0, 0) on 3 out of 4 indexes - this makes resulting accuracy to be at the level of 75% for a completely wrong answer!
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