Python Unlocked

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It seems to me like the code might be shortened or functions might be joined together. But I haven't been able to modify the code and still keep it working as intended.


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I have absolutely no idea what the purpose of this code is. Something to do with locking and unlocking files, but why? Your code doesn't say, and neither does your post. It's hard to review code when I have no idea what it is supposed to do. Running shell commands and parsing their output is complex and slow.

Instead, get the flags for a file by calling os. Use the constants in the stat module, for example stat. Since you don't say what the purpose of this code is, I can't tell if it is a good implementation or not. If you are planning to use this mechanism for mutual exclusion e. Sign up to join this community.

The best answers are voted up and rise to the top. Home Questions Tags Users Unanswered. Python program to lock and unlock files Ask Question.

Python Unlocked | BUKU - Studieboeken voor een vast bedrag per maand, online

You will also find out how to work with a range of design patterns including abstract factory, singleton, strategy pattern, all of which will help make programming with Python much more efficient. Finally, as the process of writing a program is never complete without testing it, you will learn to test threaded applications and run parallel tests.

If you want the edge when it comes to Python, use this book to unlock the secrets of smarter Python programming. Arun Tigeraniya has a BE in electronics and communication. After his graduation, he worked at various companies as a Python developer. I focused on how to make this process work using deep learning, and how to optimize each step.

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I will explain the various architectural decision that I took, and show some final experiments, done using a Kinect , a very popular RGB and depth camera, that has a very similar output to iPhone X front facing cameras but on a much bigger device. Their white paper can help us understand the basic mechanisms of FaceID. After around 15—20 different touches, the smartphone completed the registration, and TouchID was ready to go. This blazingly fast registration procedure can tell us a lot about the underlying learning algorithms.


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This would require a lot of time , energy consumption , and impractical availability of training data of different faces to have negative examples little would change in case of transfer learning and fine tuning of an already trained network. What happens is that you get an architecture capable of doing one shot learning, as they very briefly mentioned at their Keynote.

Reverse engineering iPhone X’s new unlocking mechanism.

I know, there are some names that could not be familiar to many readers: keep reading, and I will explain step by step what I mean. A siamese neural network is basically composed by two identical neural networks that also share all the weights. This architecture can learn to compute distances between particular kind of data, such as images.

The idea is that you pass couples of data through the siamese networks or simply pass the data in two different steps through the same network , the network maps it in a low dimensional feature space , like a n-dimensional array, and then you train the network to make this mapping so that data points from different classes are as far as possible , while data points from the same class are as close as possible.

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In the long run, the network will learn to extract the most meaningful features from data, and compress it into an array, creating an meaningful mapping. To have an intuitive understanding of this, imagine how you would describe dog breeds using a small vector, so that similar dogs have closer vectors. You would probably use a number to encode the fur color of the dog, another one to denote the size of the dog, another one for the length of fur, and so on.

In this way, dogs that are similar to each other will have vectors that are similar to each other. Quite smart, right? Well, a siamese neural network can learn to do this for you, similarly to what an autoencoder does. With this technique, one can use a great amount of faces to train such an architecture to recognize which faces are most similar.

Having the right budget and computing power as Apple does , one can also use harder and harder examples to make the network robust to things such as twins, adversarial attacks masks and so on.

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