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Design Perceptron to Learn AND, OR and XOR Logic Gates


It was shown that the developed ANN model can approximate the outcome to high precision with only a small sampling of the data. Using the developed model, pre-optimization was run to find the optimum condition for electrical sensitivity and responsivity of the device. We found that the light source with central wavelength of 735 nm and FWHM of 70 nm can simultaneously satisfy the optimum conditions for sensitivity and responsivity. We get our new weights by simply incrementing our original weights with the computed gradients multiplied by the learning rate. Change in the outer layer weightsNote that for Xo is nothing but the output from the hidden layer nodes.

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We will call the function that gives the error of a single sample output the loss function, and the function that gives the total error of our network across all samples the cost function. A typical choice for multiclass classification is the cross-entropy loss, also known as the negative log likelihood. We are now gong to develop an example based on the MNIST data base. This is a classification problem and we need to use our cross-entropy function we discussed in connection with logistic regression. The cross-entropy defines our cost function for the classificaton problems with neural networks. Artificial neural networks are computational systems that can learn to perform tasks by considering examples, generally without being programmed with any task-specific rules.


As I will show below it is very easy to implement the model as described above and train it using a package like keras. However, since I wanted to get a better understanding of the backpropagation algorithm I decided to first implement this algorithm. Matrix multiplication is one of the basic linear algebra operations that is used almost everywhere. Surprisingly, DeepMind developed a neural network that found a new multiplication algorithm that outperforms current, the best algorithm. In this article, we will discuss the research in more detail. And, in my case, in iteration number 107 the accuracy rate increases to 75%, 3 out of 4, and in iteration number 169 it produces almost 100% correct results and it keeps like that ‘till the end.

  • Networks with one or more hidden layers approximate systems with more complex boundaries.
  • The superiority and effectiveness of the proposed method is validated by bearing and gearbox experiments with a few labeled fault samples.
  • Some of these earliest work in AI were using networks or circuits of connected units to simulate intelligent behavior.
  • Apart from the input and output layers, MLP( short form of Multi-layer perceptron) has hidden layers in between the input and output layers.

It is supposed to mimic a biological system, wherein neurons interact by sending signals in the form of mathematical functions between layers. All layers can contain an arbitrary number of neurons, and each connection is represented by a weight variable. The XOR gate neural network implemention uses a two layer perceptron with sigmoid activation function. This portion of the notebook is a modified fork of the neural network implementation in numpy by Milo Harper. We also need to initialise the weights and bias of every link and neuron.

Understanding XOR with Keras and TensorFlow

This a network with both hidden layer and the possibility of having multiple nodes at the output layer. The hidden layer performs non-linear transformations of the inputs and helps in learning complex relations. We will use 16 neurons and ReLu as an activation function for this layer. So we have to define the input and output matrix’s dimension (a.k.a. shape). X’s shape will be because one input set has two values, and the shape of Y will be . In this case, there can be a mismatch between the training and test data.


The network in the book (Figure 6.10) is shown in the figures below, followed by my MATLAB code. The first two params are training and target data, the third one is the number of epochs and the last one tells keras how much info to print out during the training. We also added another layer with an output dimension of 1 and without an explicit input dimension.

We also set the number of iterations and the learning rate for the gradient descent method. The overall components of an MLP like input and output nodes, activation function and weights and biases are the same as those we just discussed in a perceptron. This completes a single forward pass, where our predicted_output needs to be compared with the expected_output. Based on this comparison, the weights for both the hidden layers and the output layers are changed using backpropagation. Backpropagation is done using the Gradient Descent algorithm. The empty list ‘errorlist’ is created to store the error calculated by the forward pass function as the ANN iterates through the epoch.

Plotting output of the model that failed to learn, given a set of hyper-parameters:

In this case, it is often difficult to cast these ideas in a supervised learning setting. While the problems are related, it is possible to make good predictions with a wrong model. The model might or might not be useful for understanding the underlying science. Here we list some of the important limitations of supervised neural network based models. The hyperparameter \( p \) is called the dropout rate, and it is typically set to 50%.

As we can see, the Perceptron predicted the correct output for logical OR. Similarly, we can train our Perceptron to predict for AND and XOR operators. But there is a catch while the Perceptron learns the correct mapping for AND and OR.

This function allows us to fit the output in a way that makes more sense. For example, in the case of a simple classifier, an output of say -2.5 or 8 doesn’t make much sense with regards to classification. If we use something called a sigmoidal activation function, we can fit that within a range of 0 to 1, which can be interpreted directly as a probability of a datapoint belonging to a particular class.

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Interestingly, we observe that the MSE first drops rapidly, but the MSE does not converge to zero. In other words, the training as described above does not lead to a perfect XOR gate; it can only classify 3 pair of inputs correctly. Then you can model this problem as a neural network, a model that will learn and will calibrate itself to provide accurate solutions. Here, the model predicted output () for each of the test inputs are exactly matched with the XOR logic gate conventional output () according to the truth table. Hence, it is verified that the perceptron algorithm for XOR logic gate is correctly implemented.

We are now ready to set up the algorithm for back propagation and learning the weights and biases. Another interesting feature is that is when the activation function, represented by the sigmoid function here, is rather flat when we move towards its end values \( 0 \) and \( 1 \) . In these cases, the derivatives of the activation function will also be close to zero, meaning again that the gradients will be small and the network learns slowly again. Furthermore, in a MLP with only linear activation functions, each layer simply performs a linear transformation of its inputs. The COVID-19 pandemic had brought changes to individuals, especially in consumer behavior.


With the added hidden layers, more complex problems that require more computation can be solved. However, the number of nodes in the output layer remains at 1. To truly create an ANN, the MLP is combined with a Multiple Output Perceptron .

optical logic gates

So the Class 0 region would be filled with the colour assigned to points belonging to that class. The XOR output plot — Image by Author using draw.ioOur algorithm —regardless of how it works — must correctly output the XOR value for each of the 4 points. We’ll be modelling this as a classification problem, so Class 1 would represent an XOR value of 1, while Class 0 would represent a value of 0. The perceptron basically works as a threshold function — non-negative outputs are put into one class while negative ones are put into the other class. We’ll initialize our weights and expected outputs as per the truth table of XOR.

The higher levels of abstraction are simpler to use, but less flexible, and our choice of implementation should reflect the problems we are trying to solve. Now we want to build on the experience gained from our neural network implementation in NumPy and scikit-learn and use it to construct a neural network in Tensorflow. Once we have constructed a neural network in NumPy and Tensorflow, building one in Keras is really quite trivial, though the performance may suffer.

In the forward pass xor neural network , the input data is multiplied by the weights before the sigmoid activation function is applied. The output is multiplied by W2 before the error is calculated. For the backward pass, the new weights are calculated based on the error that is found. While there are many different activation functions, some functions are used more frequently in neural networks. With the structure inspired by the biological neural network, the ANN is comprised of multiple layers — the input layer, hidden layer, and output layer — of nodes that send signals to each other. The sequential model depicts that data flow sequentially from one layer to the next.

The learning rate determines how much weight and bias will be changed after every iteration so that the loss will be minimized, and we have set it to 0.1. Designing a neural network in terms of writing code will be very hectic and unreadable to the users. Escaping all the complexities, data professionals use python libraries and frameworks to implement models. But we are designing an elementary neural network, so we will build it without using any framework like TensorFlow and PyTorch. We will take the help of NumPy, a python library famous for its mathematical operations and multidimensional arrays.

Life Cycle Assessment quantifies the multi-dimensional impact of goods and services and can be handled by Multi-Criteria Decision Analysis. In Multi-Criteria Decision Analysis, Robust Ordinal Regression manages all the compatible preference functions at once when assessing a set of alternatives and a group of preferences on reference alternatives. Now we can start making changes to our model and see how it affects the performance. Let’s try to increase the size of our hidden layer from 16 to 32.


In this case the input dimension is implicitly bound to be 16 since that’s the output dimension of the previous layer. One of the most popular libraries is numpy which makes working with arrays a joy. Keras also uses numpy internally and expects numpy arrays as inputs. We import numpy and alias it as np which is pretty common thing to do when writing this kind of code. # Initialise weights and activation and weight vectors as None. # First we propagate forward through the network to obtain activation levels and z.

The algorithm provided acceptable results for both uniform and non-uniform irradiation conditions. The results showed that the proposed approach is effective in tracking the maximum power point under partial shading conditions (Punitha, Devaraj, & Sakthivel, 2013). Na Dong et al. established a neural network-based model for prediction of solar irradiance. Several benchmark tests verified well the executability and accuracy of the proposed framework (Dong, Chang, Wu, & Gao, 2020). A single-layer perceptron contains an input layer with neurons equal to the number of features in the dataset and then an output layer with neurons equal to the target class.

  • In the book, to deceive unsuspecting readers, Minsky defined a perceptron as a two-layer machine that can handle only linearly separable problems and, for example, cannot solve the exclusive-OR problem.
  • Here, the loss function is calculated using the mean squared error .
  • The classification problem can be summarized as creating a boundary between the red and the blue dots.

You don’t want to train your model on measurements taken from the hours 00.00 to 12.00, and then test it on data collected from 12.00 to 24.00. As a convention it is normal to call a network with one layer of input units, one layer of hidden units and one layer of output units as a two-layer network. A network with two layers of hidden units is called a three-layer network etc etc. He then went to Cornell Aeronautical Laboratory in Buffalo, New York, where he was successively a research psychologist, senior psychologist, and head of the cognitive systems section. This is also where he conducted the early work on perceptrons, which culminated in the development and hardware construction of the Mark I Perceptron in 1960. This was essentially the first computer that could learn new skills by trial and error, using a type of neural network that simulates human thought processes.

Like all statistical methods, supervised learning using neural networks has important limitations. This is especially important when one seeks to apply these methods, especially to physics problems. Often, the same or better performance on a task can be achieved by using a few hand-engineered features . Scikit-learn implements a few improvements from our neural network, such as early stopping, a varying learning rate, different optimization methods, etc. It is common to add an extra term to the cost function, proportional to the size of the weights. This is equivalent to constraining the size of the weights, so that they do not grow out of control.

Each neuron accumulates its incoming signals, which must exceed an activation threshold to yield an output. If the threshold is not overcome, the neuron remains inactive, i.e. has zero output. This is a hands-on workshop notebook on deep-learning using python 3. In this notebook, we will learn how to implement a neural network from scratch using numpy. Once we have implemented this network, we will visualize the predictions generated by the neural network and compare it with a logistic regression model, in the form of classification boundaries. This workshop aims to provide an intuitive understanding of neural networks.

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Then, a review of the literature applications of ANN in electrical engineering and function approximation is carried out. Some machine learning algorithms like neural networks are already a black box, we enter input in them and expect magic to happen. Still, it is important to understand what is happening behind the scenes in a neural network. Coding a simple neural network from scratch acts as a Proof of Concept in this regard and further strengthens our understanding of neural networks. Backpropagation is a way to update the weights and biases of a model starting from the output layer all the way to the beginning.

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