Udana Taufer

In this article, we’ll cover what happens to texts when Do Not Disturb is on and whether you can still receive texts and calls.

Do Not Disturb (DND) is a mode that silences your iPhone or Android so you’re not distracted by incoming notifications. You will still receive notifications, texts, and calls, but your phone will not make a sound, and your screen will not turn on to display them. DND mode is now also included as one of the focus modes introduced in iOS 15 and Mac Monterey.

To turn on Do Not Disturb with an iPhone, open the Control Panel and tap the crescent moon icon. To access the customizable Control Panel with an iPhone X or later, swipe down from the top-right of the screen. If you’re using an earlier model, swipe up from the bottom of the screen.

To turn on Do Not Disturb on an Android, drag down from the top of your screen to access Quick Settings, then press the Do Not Disturb icon (it looks like a circle with a line through it).

You can also turn it on from the settings app on either operating system. On iPhone, select Settings > Focus > Do Not Disturb. For Android, open Settings > Notifications > Do not disturb.

Yes, you will continue receiving texts. Do Not Disturb mode on iOS and Android prevents notifications from making noise or appearing on screen. It does not block incoming text messages or phone calls.

If you aren’t receiving texts on your Android or iPhone, it’s likely caused by another problem.

When Do Not Disturb is on, texts go straight to your messages app (or iMessage for iPhone users) without pinging your lock screen or making your usual text notification sound.

On Apple devices, you can still see notifications from the Notification Center. This can be accessed from the Lock Screen by swiping up from the middle of the screen.

Likewise, on both iPhone and Android, icon number badges will still appear if these are enabled. These are the red numbers that indicate how many notifications you have in each app.

When Do Not Disturb mode is on, people can call you, but you will not hear your phone ring—unless you’ve added that person to a list of exceptions. For example, in the Do Not Disturb settings, you can allow calls from specific contacts (like emergency contacts) for which your phone will still display notifications.

Likewise, you can add other exceptions such as repeat callers (if there are repeated calls from the same phone number more than once within 15 minutes, a notification will appear).

However, your phone will still receive the incoming call. In other words, the call will not be sent straight to voicemail. But, you will only know about the missed call via your phone or contacts app after it has rung out.

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January 2019.

The first volume was Volume 34, number 1.

By James McCaffrey

It is possible to create neural networks from raw code.

There are many open source libraries you can use to speed up the process. The libraries include sci-kit-learn. Most of the neural network libraries are written in C++ for performance reasons, but also have a Python interface for convenience.

I will show you how to use the PyTorch library. PyTorch operates at a lower level of abstraction than other neural network libraries.

This will allow for easier customization at a cost of writing additional code.

Figure 1 is the best place to see where this article is going.

The program reads the data into the memory. The aim is to predict the species of an iris flower from four values: length, width, and width-to-length.

A leaf-like structure is a sepal.

The example in Figure 1 is a sample of an iris dataset.

The data set has 150 elements. 120 elements are used for learning and 30 for testing. The demo builds a neural network using PyTorch and then trains it with hundreds of iterations.

The model is evaluated against the test data after learning. The model is trained to predict the species of 27 out of 30 tests.

The species of a new, previously unseen iris flower that has the sepal and petals values is predicted by the proof. The predictions are mapped to a prediction of a color.

This article assumes that you have an intermediate or advanced knowledge of programming with a C family language, but not of PyTorch. The complete demo code is presented in this article. The source code and two data files used in the demo are also available in the download. The main ideas have been kept clear by removing normal bugchecks.

The installation of PyTorch requires two steps.

You need to install Python and several other packages first, then you need to install PyTorch as a package. It is better to install a distribution of Python than it is to install a package.

The Anaconda distribution of Python has all the packages you need to run PyTorch, plus many other useful packages. In this article, I explain how to install a Windows 10 machine. Installation on Linux and macOS is the same.

It's a non-trivial challenge to coordinate the supported versions of Python. Installation errors are related to version incompatibilities.

I am writing this article using Ananconda3 5.2.0, which includes Python 3.6.5, NumPy 1.14.3, andSciPy 1.1.0) and PyTorch 0.4.1. Since PyTorch is relatively new and under continuous development, a newer version may be available at the time you read this article.

I recommend that you uninstall any Python systems you have on your machine before you start.

You should create a C:PyTorch directory to house the installation and project files.

To install the distribution, you need to download the self-extracting executable from the repo.continuum.io/archive. If you click the link, you will get a dialog box with buttons to run or save.

The Run button is on the screen.

The installation is very easy to use. There are eight installation wizard screens that you will be presented with.

You can accept all of the defaults and just click the Next button on each screen. The default is off when you ask if you want to add Python to the system PATH environment variable. You don't have to manually edit the system PATH if you enable that option.

The Python interpreter and compatible packages will be placed in the C:Users user>AppDataLocal ContinuumAnaconda3 directory.

To install the PyTorch library, you have to go to pytorch.org and click on the "Previous versions of PyTorch" link.

There is a file named torch 0.4.1-cp36-cp36m-win_amd64.whl. This is a file in Python. You can think of a.whl file as a Windows.msi file. If you click on the link, you will be able to open or save.

The.whl file should be placed in your C:PyTorch directory. If you can't locate the PyTorch.whl file, you can look for it at bit.ly/2SUiAuj, which is where I wrote the article.

You can install PyTorch using the PythonPIP utility. You can open a Windows command shell and navigate to the directory where you saved the PyTorch.whl file.

Then type the command again.

Installation is quick, but there are many things that can go wrong. If the installation fails, read the error messages in the shell.

The problem may be due to version compatibility.

To start the Python interpreter, open a command shell and type "python" You will see the prompt. There are two consecutive underscores in the version command.

If you see the answers displayed here, that means you're ready to start writing neural network machine learning code.

You can find the raw data at bit.ly/1N5br3h. The data looks like this.

The petals length and width of a flower are the first four values of each line. The species is predicted. The raw data has 50 setosa, 50 versicolor, and 50 virginica. The test file is the last 10 of each species, and the learning file is the first 40 of each species. It is not possible to plot the data set since there are seven predictor variables.

You can see the structure of the data by looking at the graph in Figure 2.

Figure 2 shows partial iris data.

Neural networks only understand numbers, so species must be encoded. Setosa, versicolor, and virginica would be replaced with 1 and 0, respectively, with most neural network libraries. This is called 1-of-N or "one-hot"

PyTorch expects 0, 1, or 2 for all three classes. The PyTorch data looks similar to the others.

In most cases, you should scale the predictor variables so that all values are between 0.0 and 1.0, which is called min-max normalization.

I didn't change the data in order to make the demo simpler. When working with neural networks, I usually create a root folder for the problem, and then a subdirectory called Data to store the data files.

The demo program is presented in Figure 3. To save space, I use two spaces instead of four. There are dozens of Python editors that have advanced features, but I used Notepad to edit the demo program.

The "" character is used for line continuation.

The program in Figure 3 is called the iris Dataset demo program.

The structure of a PyTorch program is slightly different from other libraries. In the demo, there is a specified number of items for learning. The Net class is used to define the neural network.

The percentage of correct predictions is calculated using the function accuracy. The control logic is included in the main function.

You should include a comment about which version is used in PyTorch and Python.

Many programmers are surprised that base Python does not support arrays. You'll almost always need to import the NumPy package if PyTorch uses the arrays.

The neural network is defined by the following.

The first line of code shows that the class is a descendant of the T.nn.Module class. The function can be seen as a constructor of the class. The hidden layer of the network is called the fc1 and it has seven processing nodes.

The number of hidden nodes is defined by trial and error. The hidden layer weights are used in most other libraries.

The biases are zero.

The network output layer is defined.

The output layer expects seven inputs from the hidden layer and then outputs three values for each species. The output and hidden layers are not connected at this time. The required forward function is used to establish the connection.

The function accepts x as the input prediction values. The values are passed to the hidden layer and the results are passed to the activation function.

The final results are returned after that result is passed to the output layer. The learning loss function will automatically apply softmax, unlike many neural network libraries where softmax is applied.

Learning would be slower if you were to softmax at the output layer, since you would be softmaxing twice.

When using PyTorch, you can load data into the array and convert it to PyTorch objects.

A Tensor object can be thought of as a sophisticated array that can be managed by a graphics card.

There are many ways to load data.

The most common technique is to use the Python Pandas package. The demo program uses the loadtxt function to make it simpler because of the learning curve of Pandas. The learning data is loaded.

The predicted values are expected to be in an array-style array and the class values are expected to be in an array.

The array train_x will have 120 rows and four columns and the array train_y will have 120 values. float32 data is the default data type in most neural network libraries, as they do not bring the performance issues that float32 data does.

The demo creates a neural network, and prepares it for training.

Setting the network to learn mode is not necessary for the demo since the learn does not use dropout or batches, which have different execution flows for learn and test. The learning rate, the batches, and the maximum learning iteration are all hyperparameters. The demo uses iteration instead of an epoch, since an epoch typically refers to processing all the learning elements one at a time.

Only 12 of the learning items are processed in this case.

Multiclass classification problems can be measured using the Cross EntropyLoss function. It is a mistake to try and use it for classification. The demo uses the most rudimentary way to learn.

PyTorch supports a number of sophisticated algorithms, including adaptive moment estimation, adaptive gradient, and resistant mean-square propagation.

The simplest possible mechanism is implemented by the program-defined Batch class. Each call to the next_batch function returns 12 randomly selected training data indices. This approach does not mean that all learning elements will be used the same number of times. In a non-demo scenario, you would probably want to implement a more sophisticated batch generator that randomly selects the different indices until they've all been selected once, and then automatically resets.

The learning is done 600 times. The demo displays progress information every 600 / 10 iteration.