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Source channel @githubtrending · Post #15009 · Jul 31

#python#adb#airtest#cv#fate_grand_order#fgo#qt6 This program automates playing Fate/Grand Order on Android in multiple languages (Chinese, Japanese, English, Taiwanese). It can run on Windows, Linux, Mac, Android, and Docker, requiring minimal setup. It smartly controls battles by choosing skills, cards, and support servants without needing manual input or special equipment. It also automates weekly missions, friend support selection, and item management, saving you time and effort. You can run it on your phone or PC, even using tools like AidLux or AzurLaneAutoScript. It helps you farm efficiently without worrying about complicated setups or "best" cards, making the game easier and less time-consuming[5]. https://github.com/hgjazhgj/FGO-py

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djangoproject

@djangoproject · Post #274 · 03/18/2017, 01:48 AM

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https://github.com/riga/tfdeploy Google's TensorFlow framework is taking off big-time now that it's at a full 1.0 release. One common question about it: How can I make use of the models I train in TensorFlow without using TensorFlow itself? #Tfdeploy is a partial answer to that question. It exports a trained TensorFlow model to "a simple #NumPy-based callable," meaning the model can be used in Python with Tfdeploy and the the NumPy math-and-stats library as the only dependencies. Most of the operations you can perform in TensorFlow can also be performed in Tfdeploy, and you can extend the behaviors of the library by way of standard Python metaphors (such as overloading a class). Now the bad news: Tfdeploy doesn't support GPU acceleration, if only because NumPy doesn't do that. Tfdeploy's creator suggests using the gNumPy project as a possible replacement. #Machine_learning

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