Some believe that the Extreme Learning Machine is one of the smartest neural network inventions ever created — so much so that there’s even a conference dedicated exclusively to the study of ELM neural network architectures. Proponents of ELMs argue that it can perform standard tasks at exponentially faster training times, with few training examples. On the other hand, besides from the fact that it’s not big in the machine learning community, it’s got plenty of criticism from experts in deep learning, including Yann LeCun, who argue that it’s gotten far more publicity and credibility than it deserves.
Mostly, people seem to think it’s an interesting concept.
An ELM architecture is composed of two layers; the first is randomly initialized and fixed, whereas the second is trainable. Essentially, the network randomly projects the data into a new space and performs a multiple regression (then passing it through an output activation function, of course). The random projection entails a dimensionality reduction (or enlargement) method, which multiplies random matrices by the input — although the idea may sound odd, pulling randomly from strategic distributions can actually work very well (as we’ll see with an intuitive analogy later). It applies a random distortion of sorts that applies noise — in a good way, if done correctly — and lets the remainder of the network to adapt, opening up new doors in terms of learning opportunities.
In fact, it is because of this randomness that Extreme Learning Machines have been shown to possess Universal Approximation Theorem powers with relatively small nodes in the hidden layer.
In fact, the idea of random projections have been explored early in the field of neural network development, in the 1980s and 1990s, under that name — which is the reasoning behind one critique that ELMs are nothing new; just old research packaged under a new name. Many other architectures like echo state machines and liquid state machines also utilize random skip connections and other sources of randomness.
Perhaps the largest difference, however, between ELMs and other neural network architectures is that it doesn’t use backpropagation. Instead, since the trainable half of the network is simply a multiple regression, the parameters are trained in roughly the same way coefficients are fitted in regression. This represents a fundamental shift in the way neural networks are thought to be trained.
Almost every neural network developed since the vanilla artificial neural network has been optimized using iterative updating (or call it tuning, if you’d like) by bouncing information signals forward and backward throughout the network. Because this method has been around for so long, one must assume that it’s been tried and tested as the best one, but researchers acknowledge that standard backpropagation has many issues, like being very slow to train or falling into very luring local minima.
On the other hand, ELM uses a much more mathematically involved formula to set weights, and without going too deeply into the math, one can think of using the random layer as compensating for more computationally expensive details that it would otherwise be replaced with. If it helps, technically, the wildly successful Dropout layer is a sort of random projection.
Because ELMs employ both randomness and a no-backpropagation algorithm, they are exponentially faster to train than standard neural networks.
Whether they perform better or not, on the other hand, is another question.
#artificial-intelligence #ai #machine-learning #data-science #deep-learning
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
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
We will develop the model and we will feed it with input. What we expect from the algorithm is to learn the underlying relationship of the data.
Here’s how we are going to approach the problem.
First, we will import the relevant Python libraries for the problem at hand.
Second, we will generate random data to train with, with random data, as rerunning the code will always yield different inputs. However, the model will remain the same in this way.
It is more important to see how it works and to prove that it works than acquire deep insights.
Third, we will create the targets. These are the correct values; essentially, we will use big targets to be sure there is a linear relationship.
In this way, when we treat the algorithm we will be certain about the dependence it has to learn. It’s a good way to prove the optimization is actually working.
Fourth we will plot the training data so you can visually see it.
We will define the variables we need. We must create weights, biases, and set a learning rate. In the end, we will conclude with an actual regression along the way each line of code will be explained to make sure everything is understood.
Let’s begin: The relevant Python libraries are NumPy and matplotlib.pyplot. NumPy contains all the mathematical operations you will need. Moreover, it is extremely fast; for these two reasons, it is heavily used in data science. Let’s import numpy as np.
matplotlib is a library used for plotting data. The module pyplot provides a nice interface and requires very few arguments. Let’s import matplotlib.pyplot as plt.
#neural-networks #artificial-intelligence #data-science #programming #machine-learning #neural networks
Recurrent neural networks, also known as RNNs, are a class of neural networks that allow previous outputs to be used as inputs while having hidden states. RNN models are mostly used in the fields of natural language processing and speech recognition.
The vanishing and exploding gradient phenomena are often encountered in the context of RNNs. The reason why they happen is that it is difficult to capture long term dependencies because of multiplicative gradient that can be exponentially decreasing/increasing with respect to the number of layers.
Gated Recurrent Unit (GRU) and Long Short-Term Memory units (LSTM) deal with the vanishing gradient problem encountered by traditional RNNs, with LSTM being a generalization of GRU.
1D Convolution_ layer_ creates a convolution kernel that is convolved with the layer input over a single spatial (or temporal) dimension to produce a tensor of outputs. It is very effective for deriving features from a fixed-length segment of the overall dataset. A 1D CNN works well for natural language processing (NLP).
TensorFlow Datasets is a collection of datasets ready to use, with TensorFlow or other Python ML frameworks, such as Jax. All datasets are exposed as
[_tf.data.Datasets_](https://www.tensorflow.org/api_docs/python/tf/data/Dataset), enabling easy-to-use and high-performance input pipelines.
This is a dataset for binary sentiment classification containing substantially more data than previous benchmark datasets. It provides a set of 25,000 highly polar movie reviews for training, and 25,000 for testing.
import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns %matplotlib inline
import tensorflow as tf import tensorflow_datasets imdb, info=tensorflow_datasets.load("imdb_reviews", with_info=True, as_supervised=True) imdb
train_data, test_data=imdb['train'], imdb['test'] training_sentences= training_label= testing_sentences= testing_label= for s,l in train_data: training_sentences.append(str(s.numpy())) training_label.append(l.numpy()) for s,l in test_data: testing_sentences.append(str(s.numpy())) testing_label.append(l.numpy()) training_label_final=np.array(training_label) testing_label_final=np.array(testing_label)
vocab_size=10000 embedding_dim=16 max_length=120 trunc_type='post' oov_tok='<oov>' from tensorflow.keras.preprocessing.text import Tokenizer from tensorflow.keras.preprocessing.sequence import pad_sequences tokenizer= Tokenizer(num_words=vocab_size, oov_token=oov_tok) tokenizer.fit_on_texts(training_sentences) word_index=tokenizer.word_index sequences=tokenizer.texts_to_sequences(training_sentences) padded=pad_sequences(sequences, maxlen=max_length, truncating=trunc_type) testing_sequences=tokenizer.texts_to_sequences(testing_sentences) testing_padded=pad_sequences(testing_sequences, maxlen=max_length) from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense, Dropout, Embedding
#imdb #convolutional-network #long-short-term-memory #recurrent-neural-network #gated-recurrent-unit #neural networks