Unsupervised learning, applied in one-class classification, aims to discover rules to separate normal and abnormal data in the absence of labels. One-Class SVM (OC-SVM) is a common unsupervised approach to detect outliers. It considers all the data points as positively labeled instances and builds around them a smooth boundary to detect ‘strange’ samples.
Recently, various approaches based on feature extraction models appear to be a valid instrument to use with OC-SVM. Following the amazing success of deep neural networks as feature extractors, different methods that exploit feature extraction, using deep-learning, and OC-SVM were introduced as multi-step one-class procedures.
In this post, we present a One-Class Convolutional Neural Network architecture (as introduced here) that merges the power of deep networks to extract meaningful data representations along with the one-class objective, all in one-step.
The structure of the proposed architecture is composed of two parts: a feature extractor and a multi-layer perceptron. A fundamental aspect of this approach is that any pre-trained deep-learning model can be used as the base network for feature extraction. The most common choices regard the adoption of standard network modules for feature extraction, for example, VGG, ResNet, or Inception can be good alternatives for this specific task. There are no fixed rules regarding the points where to cut the network in order to produce a meaningful feature representation. The multilayer perceptron part is located at the end, it receives the embedded representations and tries to classify them into 0 or 1. Here, 1 means that the input sample belongs to the real target class while 0 means that the input sample belongs to the noisy class. As before, the choice of a structure for this part is not fixed. It can be manipulated and tuned in order to achieve better performances.
Between the feature extractor module and the final multilayer perceptron network happens the magic. Exactly there we can find the core idea of the whole procedure, which permits us to join the feature extraction with the classification, all in one step and having at disposal only one set of label. The embedded data samples are ‘corrupted’ adding some zero-centered gaussian noise. These modified samples are then concatenated batch-wise with their original couples. The result of this approach is that we have a duplicated batch of images formed by original samples (class 1) and corrupted ones (class 0). Our aim is that our classification layers can understand this difference and discriminate real images from all the rest.
#data-science #machine-learning #keras #developer
Neural networks have been around for a long time, being developed in the 1960s as a way to simulate neural activity for the development of artificial intelligence systems. However, since then they have developed into a useful analytical tool often used in replace of, or in conjunction with, standard statistical models such as regression or classification as they can be used to predict or more a specific output. The main difference, and advantage, in this regard is that neural networks make no initial assumptions as to the form of the relationship or distribution that underlies the data, meaning they can be more flexible and capture non-standard and non-linear relationships between input and output variables, making them incredibly valuable in todays data rich environment.
In this sense, their use has took over the past decade or so, with the fall in costs and increase in ability of general computing power, the rise of large datasets allowing these models to be trained, and the development of frameworks such as TensforFlow and Keras that have allowed people with sufficient hardware (in some cases this is no longer even an requirement through cloud computing), the correct data and an understanding of a given coding language to implement them. This article therefore seeks to be provide a no code introduction to their architecture and how they work so that their implementation and benefits can be better understood.
Firstly, the way these models work is that there is an input layer, one or more hidden layers and an output layer, each of which are connected by layers of synaptic weights¹. The input layer (X) is used to take in scaled values of the input, usually within a standardised range of 0–1. The hidden layers (Z) are then used to define the relationship between the input and output using weights and activation functions. The output layer (Y) then transforms the results from the hidden layers into the predicted values, often also scaled to be within 0–1. The synaptic weights (W) connecting these layers are used in model training to determine the weights assigned to each input and prediction in order to get the best model fit. Visually, this is represented as:
#machine-learning #python #neural-networks #tensorflow #neural-network-algorithm #no code introduction to neural networks
Welcome to DataFlair Keras Tutorial. This tutorial will introduce you to everything you need to know to get started with Keras. You will discover the characteristics, features, and various other properties of Keras. This article also explains the different neural network layers and the pre-trained models available in Keras. You will get the idea of how Keras makes it easier to try and experiment with new architectures in neural networks. And how Keras empowers new ideas and its implementation in a faster, efficient way.
Keras is an open-source deep learning framework developed in python. Developers favor Keras because it is user-friendly, modular, and extensible. Keras allows developers for fast experimentation with neural networks.
Keras is a high-level API and uses Tensorflow, Theano, or CNTK as its backend. It provides a very clean and easy way to create deep learning models.
Keras has the following characteristics:
The following major benefits of using Keras over other deep learning frameworks are:
Before installing TensorFlow, you should have one of its backends. We prefer you to install Tensorflow. Install Tensorflow and Keras using pip python package installer.
The basic data structure of Keras is model, it defines how to organize layers. A simple type of model is the Sequential model, a sequential way of adding layers. For more flexible architecture, Keras provides a Functional API. Functional API allows you to take multiple inputs and produce outputs.
It allows you to define more complex models.
#keras tutorials #introduction to keras #keras models #keras tutorial #layers in keras #why learn keras
When constructing a neural network, there are several optimizers available in the Keras API in order to do so.
An optimizer is used to minimise the loss of a network by appropriately modifying the weights and learning rate.
For regression-based problems (where the response variable is in numerical format), the most frequently encountered optimizer is the **Adam **optimizer, which uses a stochastic gradient descent method that estimates first-order and second-order moments.
The available optimizers in the Keras API are as follows:
The purpose of choosing the most suitable optimiser is not necessarily to achieve the highest accuracy per se — but rather to minimise the training required by the neural network to achieve a given level of accuracy. After all, it is much more efficient if a neural network can be trained to achieve a certain level of accuracy after 10 epochs than after 50, for instance.
#machine-learning #neural-network-algorithm #data-science #keras #tensorflow #neural networks
Talking about inspiration in the networking industry, nothing more than Autonomous Driving Network (ADN). You may hear about this and wondering what this is about, and does it have anything to do with autonomous driving vehicles? Your guess is right; the ADN concept is derived from or inspired by the rapid development of the autonomous driving car in recent years.
Driverless Car of the Future, the advertisement for “America’s Electric Light and Power Companies,” Saturday Evening Post, the 1950s.
The vision of autonomous driving has been around for more than 70 years. But engineers continuously make attempts to achieve the idea without too much success. The concept stayed as a fiction for a long time. In 2004, the US Defense Advanced Research Projects Administration (DARPA) organized the Grand Challenge for autonomous vehicles for teams to compete for the grand prize of $1 million. I remembered watching TV and saw those competing vehicles, behaved like driven by drunk man, had a really tough time to drive by itself. I thought that autonomous driving vision would still have a long way to go. To my surprise, the next year, 2005, Stanford University’s vehicles autonomously drove 131 miles in California’s Mojave desert without a scratch and took the $1 million Grand Challenge prize. How was that possible? Later I learned that the secret ingredient to make this possible was using the latest ML (Machine Learning) enabled AI (Artificial Intelligent ) technology.
Since then, AI technologies advanced rapidly and been implemented in all verticals. Around the 2016 time frame, the concept of Autonomous Driving Network started to emerge by combining AI and network to achieve network operational autonomy. The automation concept is nothing new in the networking industry; network operations are continually being automated here and there. But this time, ADN is beyond automating mundane tasks; it reaches a whole new level. With the help of AI technologies and other critical ingredients advancement like SDN (Software Defined Network), autonomous networking has a great chance from a vision to future reality.
In this article, we will examine some critical components of the ADN, current landscape, and factors that are important for ADN to be a success.
At the current stage, there are different terminologies to describe ADN vision by various organizations.
Even though slightly different terminologies, the industry is moving towards some common terms and consensus called autonomous networks, e.g. TMF, ETSI, ITU-T, GSMA. The core vision includes business and network aspects. The autonomous network delivers the “hyper-loop” from business requirements all the way to network and device layers.
On the network layer, it contains the below critical aspects:
On top of those, these capabilities need to be across multiple services, multiple domains, and the entire lifecycle(TMF, 2019).
No doubt, this is the most ambitious goal that the networking industry has ever aimed at. It has been described as the “end-state” and“ultimate goal” of networking evolution. This is not just a vision on PPT, the networking industry already on the move toward the goal.
David Wang, Huawei’s Executive Director of the Board and President of Products & Solutions, said in his 2018 Ultra-Broadband Forum(UBBF) keynote speech. (David W. 2018):
“In a fully connected and intelligent era, autonomous driving is becoming a reality. Industries like automotive, aerospace, and manufacturing are modernizing and renewing themselves by introducing autonomous technologies. However, the telecom sector is facing a major structural problem: Networks are growing year by year, but OPEX is growing faster than revenue. What’s more, it takes 100 times more effort for telecom operators to maintain their networks than OTT players. Therefore, it’s imperative that telecom operators build autonomous driving networks.”
Juniper CEO Rami Rahim said in his keynote at the company’s virtual AI event: (CRN, 2020)
“The goal now is a self-driving network. The call to action is to embrace the change. We can all benefit from putting more time into higher-layer activities, like keeping distributors out of the business. The future, I truly believe, is about getting the network out of the way. It is time for the infrastructure to take a back seat to the self-driving network.”
If you asked me this question 15 years ago, my answer would be “no chance” as I could not imagine an autonomous driving vehicle was possible then. But now, the vision is not far-fetch anymore not only because of ML/AI technology rapid advancement but other key building blocks are made significant progress, just name a few key building blocks:
#network-automation #autonomous-network #ai-in-network #self-driving-network #neural-networks
Forward propagation is an important part of neural networks. Its not as hard as it sounds ;-)
So, to perform gradient descent or cost optimisation, we need to write a cost function which performs:
In this article, we are dealing with (1) forward propagation.
In figure 1, we can see our network diagram with much of the details removed. We will focus on one unit in level 2 and one unit in level 3. This understanding can then be copied to all units. (ps. one unit is one of the circles below)
Our goal in forward prop is to calculate A1, Z2, A2, Z3 & A3
Just so we can visualise the X features, see figure 2 and for some more info on the data, see part 1.
As it turns out, this is quite an important topic for gradient descent. If you have not dealt with gradient descent, then check this article first. We can see above that we need 2 sets of weights. (signified by ø). We often still calls these weights theta and they mean the same thing.
We need one set of thetas for level 2 and a 2nd set for level 3. Each theta is a matrix and is size(L) * size(L-1). Thus for above:
Theta1 = 6x4 matrix
Theta2 = 7x7 matrix
We have to now guess at which initial thetas should be our starting point. Here, epsilon comes to the rescue and below is the matlab code to easily generate some random small numbers for our initial weights.
function weights = initializeWeights(inSize, outSize) epsilon = 0.12; weights = rand(outSize, 1 + inSize) * 2 * epsilon - epsilon; end
After running above function with our sizes for each theta as mentioned above, we will get some good small random initial values as in figure 3
. For figure 1 above, the weights we mention would refer to rows 1 in below matrix’s.
Now, that we have our initial weights, we can go ahead and run gradient descent. However, this needs a cost function to help calculate the cost and gradients as it goes along. Before we can calculate the costs, we need to perform forward propagation to calculate our A1, Z2, A2, Z3 and A3 as per figure 1.
#machine-learning #machine-intelligence #neural-network-algorithm #neural-networks #networks