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Fast multilayer perceptron neural network library for iOS and Mac OS X. MLPNeuralNet predicts new examples by trained neural network. It is built on top of the Apple's Accelerate Framework, using vectorized operations and hardware acceleration if available.
Why to choose it?
Imagine that you created a prediction model in Matlab (Python or R) and want to use it in iOS app. https://texasfree.mystrikingly.com/blog/best-video-poker-in-vegas. If that's the case, MLPNeuralNet is exactly what you need. It is designed to load and run models in forward propagation mode only.
For example, if a connection is failing to work because you can’t find a specific networking device in Network preference panel of Mac OS X, you can check to see if something like a wifi card is found here, and if so, try to power-cycle it off and on again, which often resolves many of those simple detection problems. . The Neural Engine from Apple is a neural network hardware integrated within the A-Series line of microprocessors since the A11 Bionic. A neural network hardware is an artificial intelligence accelerator designed for AI applications to include machine learning, as well as data processing for a more specific image and speech processing. According to Wikipedia an Artificial Neural Network (ANN) is defined as follows: “In machine learning and cognitive science, artificial neural networks (ANNs) are a family of models inspired by biological neural networks (the central nervous systems of animals, in particular the brain) which are used to estimate or approximate functions that can depend on a large number of inputs.
- Neural nets has enabled significant breakthroughs in everything from computer vision, voice generation, voice recognition and self-driving cars. Game development is also a key area where these techniques are being applied. This book will give an in depth view of the potential of deep learning and neural networks in game development.
- We released Neural Network Libraries v. Its distinctive features are as following: It can be installed with pip on MacOS (CUDA extension is not available yet) C stand-alone mode is compatible with various OS (MacOS, Windows).
Features:
- classification, multiclass classification and regression output;
- vectorized implementaion;
- works with double precision;
- multiple hidden layers or none (in that case it's same as logistic/linear regression)
Quick Example
Let's deploy a model for the AND function (conjunction) that works as follows (of course in real world you don't have to use neural net for this :)
| X1 | X2 | Y |
|---|---|---|
| 0 | 0 | 0 |
| 1 | 0 | 0 |
| 0 | 1 | 0 |
| 1 | 1 | 1 |
Our model has the following weights and network configuration:
Getting started
This instruction describes on how to install MLPNeuralNet using the CocoaPods. It is written for Xcode 5, using the iOS 7 SDK. If you are familiar with 3rd-party library management, just clone MLPNeuralNet repo on Github and import it to XCode directly as a subproject.
Step 1. Install CocoaPods
CocoaPods is a dependency manager for Objective-C. Installing it is as easy as running the following commands in the terminal:
Step 2. Create Podfile
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List MLPNeuralNet as a dependenciy in a text file named Podfile in your Xcode project directory:
Step 3. Install MLPNeuralNet
Now you can install the dependencies in your project:
Make sure to always open the Xcode workspace (.xcworkspace) instead of the project file when building your project:
Step 4. Import MLPNeuralNet.h
#import 'MLPNeuralNet.h' to start working on your model. That's it!
Performance benchmark
In this test the neural net is grown layer by layer from 1 -> 1 configuration to 200 -> 200 -> 200 -> 1. At each step the output is calculated and benchmarked using random input vector and random weights. Total number of weights grows from 2 to 80601 accordingly. I understand the test is quite synthetic, but I hope it illustrates the performance. I will be happy if you can propse better one :)
Unit Tests
https://downrup192.weebly.com/sam-yammen-mac-os.html. MLPNeuralNet includes a suite of unit tests in the MLPNeuralNetTests subdirectory. You can execute them via the 'MLPNeuralNet' scheme within Xcode.
Credits
- MLPNeuralNet was inspired by Andrew Ng's course on Machine Learning.
- Neural Network image was taken from Wikipedia Commons
Contact
Maintainer: Mykola Pavlov (me@nikolaypavlov.com). https://guy-free.mystrikingly.com/blog/get-ms-excel-free.
Please let me know on how you use MLPNeuralNet for some real world problems.
License
MLPNeuralNet is available under the BSD license. See the LICENSE file for more info.
Written with StackEdit.
< Back to Our BlogApple has been using machine learning in their products for a long time: Siri answers our questions and entertains us, iPhoto recognizes faces in our photos, Mail app detects spam messages.
As app developers, we have access to some capabilities exposed by Apple’s APIs such as face detection, and starting with iOS 10, we’ll gain a high-level API for speech recognition and SiriKit.
Sometimes we may want to go beyond the narrow confines of the APIs that are built into the platform and create something unique. Many times, we roll our own machine learning capabilities, using one of a number of off-the-shelf libraries or building directly on top of fast computation capabilities of Accelerate or Metal.
For example, my colleagues built an entry system for our office that uses an iPad to detect a face, then posts a gif in Slack and allows users to unlock the door using a custom command.
But now we have first-party support for neural networks: at WWDC 2016, Apple introduced not one, but two neural network APIs, called Basic Neural Network Subroutines (BNNS) and Convolutional Neural Networks (CNN).
Machine Learning and Neural Networks
AI pioneer Arthur Samuel defined machine learning as a “field of study that gives computers the ability to learn without being explicitly programmed.” Machine learning systems are frequently used to make sense of the data that can’t easily be described using traditional models.
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For example, we can easily write a program that calculates the square footage (area) of the house, given the dimensions and shapes of all its rooms and other spaces, but calculating the value of the house is not something we can put in a formula. A machine learning system, on the other hand, is well suited for such problems. By supplying the known real-world data to the system, such as the market value, size of the house, number of bedrooms, etc., we can train it to be able to predict the price.
A neural network is one of the most common models to building machine learning system. While the mathematical underpinnings of neural networks have been developed over half a century ago in the 1940s, parallel computing made them more feasible in the 1980s and the interest in deep learning sparked a resurgence of neural networks in the 2000s.
A neural network is constructed of a number of layers, each of which consists of one or more nodes. The simplest neural network has three layers: input, hidden and output. The input layer nodes may represent individual pixels in an image or some other parameters. The output layer nodes are often the results of the classification, such as “dog” or “cat”, if we are trying to automatically detect the contents of a photo. The hidden layer nodes are configured to perform an operation on the inputs or apply the activation function.
Types of Layers
Three common types of layers are pooling, convolution and fully connected.
A pooling layer aggregates the data, reducing its size, typically by using the maximum or average value of its inputs. A series of convolution and pooling layers can be stringed together to gradually distill a photo into a collection of increasingly higher-level features.
A convolution layer transforms an image by applying a convolution matrix to each pixel of the image. If you’ve used Pixelmator or Photoshop filters, you’ve most likely used a convolution matrix. A convolution matrix is typically a 3×3 or 5×5 matrix that is applied to the input image pixels in order to calculate the new pixel values in the output image. To get the value of the output pixel, we would multiply the values of the pixels in the original image and calculate the average.
Casino floor bonus. For example, this convolution matrix would blur the image:
Whereas this one would sharpen the image:
The neural network’s convolution layer uses the convolution matrix to process the input and generate the data for the next layer, for example, to extract new features in an image, such as edges.
A fully connected layer can be thought of as a convolution layer where the filter has the same size as the original image. In other words, you can think of the fully connected layer as a function that assigns weights to individual pixels, averages the result, and gives a single output value.
Training and Inference
Each layer needs to be configured with appropriate parameters. For example, the convolution layer needs information about the input and output images (dimensions, number of channels, etc.), as well as convolution layer parameters (kernel size, matrix, etc.). The fully connected layer is defined by the input and output vectors, activation function, and weights.
To obtain these parameters, the neural network has to be trained. This is accomplished by passing the inputs through the neural network, determining the output, measuring the error (i.e., how far off the actual result was from the predicted result), and adjusting the weights via backpropagation. Training a neural network may require hundreds, thousands, or even millions of examples.
At the moment, Apple’s new machine learning APIs can be used for building neural networks that only do inference, but not training. Good thing that Big Nerd Ranch does.
Accelerate: BNNS
The first new API is part of the Accelerate framework and is called BNNS, which stands for Basic Neural Network Subroutines. BNNS complements the BLAS (Basic Linear Algebra Subroutines), which was used in some third-party machine learning applications.
BNNS defines layers in the BNNSFilter class. Accelerate supports three types of layers: convolution layer (created by the BNNSFilterCreateConvolutionLayer function), fully connected layer (BNNSFilterCreateFullyConnectedLayer), and pooling layer (BNNSFilterCreatePoolingLayer).
Neural Network Software
The MNIST database is a well-known data set containing tens of thousands of hand-written digits that were scanned and resized to fit a 20 by 20 pixel image.
One approach to processing image data is to convert an image into a vector and pass it through a fully connected layer. For the MNIST data, a single 20×20 image would become a vector of 400 values. Here’s how a hand-written digit “1” would get converted to a vector:
Below is sample code for configuring a fully connected layer that takes a vector of size 400 as an input, uses the sigmoid activation function and outputs a vector of size 25:
Metal!
Does it get any more metal than this? As a matter of fact, it does, because the second neural network API is part of Metal Performance Shaders (MPS) framework. While Accelerate is the framework for performing fast computing on the CPU, Metal pushes the GPU to its limit. Metal’s flavor is called CNN, the Convolution Neural Network.
MPS comes with a similar set of APIs. Creating a convolution layer requires use of MPSCNNConvolutionDescriptor and MPSCNNConvolution functions. For a pooling layer, MPSCNNPoolingMax would supply the parameters. A fully connected layer is created by the MPSCNNFullyConnected function.
The activation functions are defined by subclasses of MPSCNNNeuron: MPSCNNNeuronLinear, MPSCNNNeuronReLU, MPSCNNNeuronSigmoid, MPSCNNNeuronTanH, MPSCNNNeuronAbsolute.
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BNNS and CNN compared
This table presents the list of activation functions in Accelerate and Metal:
| Accelerate/BNNS | Metal Performance Shaders/CNN |
|---|---|
| BNNSActivationFunctionIdentity | |
| BNNSActivationFunctionRectifiedLinear | MPSCNNNeuronReLU |
| MPSCNNNeuronLinear | |
| BNNSActivationFunctionLeakyRectifiedLinear | |
| BNNSActivationFunctionSigmoid | MPSCNNNeuronSigmoid |
| BNNSActivationFunctionTanh | MPSCNNNeuronTanH |
| BNNSActivationFunctionScaledTanh | |
| BNNSActivationFunctionAbs | MPSCNNNeuronAbsolute |
Pooling functions:
| Accelerate/BNNS | Metal Performance Shaders/CNN |
|---|---|
| BNNSPoolingFunctionMax | MPSCNNPoolingMax |
| BNNSPoolingFunctionAverage | MPSCNNPoolingAverage |
Accelerate and Metal provide a very similar set of functionality for neural networks, so the choice of one or the other will depend on each application. While GPUs are typically preferred for the kinds of computations required in machine learning, data locality may cause the Metal CNN to perform poorer than the Accelerate BNNS version. If the neural network operates on images that have been loaded into the GPU, for example, using MPSImage and the new MPSTemporaryImage, Metal is the clear winner.
Want more info on machine learning? Check out this post on getting started with Core ML, a new framework announced at WWDC 2017.
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Neural Network Cars Mac OS