machine learning Neural networks why everybody has different approach with XOR

Connect and share knowledge within a single location that is structured and easy to search. The classic multiplication algorithm will have complexity as O(n3). Neural networks are now widespread and are used in practical tasks such as speech recognition, https://traderoom.info/xtrade-forex-broker-overview/ automatic text translation, image processing, analysis of complex processes and so on. A Deep Neural Network that is able to accurately mimic an XOR gate. The goal of the Deep Network is to classify the input patterns according to the XOR truth table.

• The activation function used for all neurons is the sigmoid function, which squashes the neuron’s output between 0 and 1.
• This repo also includes implementation of Logical functions AND, OR, XOR.
• If I’ll try to add just 1 more neuron in the hidden layer, network is successfully calculating XOR after ~ epochs.
• It is capable of learning the desired function without requiring a large amount of data.

There should be “0” as an answer, but answer is usually 0.5something. Currently I’m trying to learn how to work with neural networks by reading books, but mostly internet tutorials. If we imagine such a neural network in the form of matrix-vector operations, then we get this formula. XOR is an exclusive or (exclusive disjunction) logical operation that outputs true only when inputs differ. As we know that for XOR inputs 1,0 and 0,1 will give output 1 and inputs 1,1 and 0,0 will output 0. Following the creation of the activation function, various parameters of the ANN are defined in this block of code.

Neural networks – why everybody has different approach with XOR

In their book, Perceptrons, Minsky and Papert suggested that “simple ANNs” (referring to the single layer Perceptron) were not computationally complex enough to solve the XOR logic problem [5]. While there are many different activation functions, some functions are used more frequently in neural networks. Next, the function enters a loop that runs for n_epochs iterations. In each iteration, the neural network performs a forward pass, followed by a backward pass, and updates its weights and biases using the backpropagation algorithm. A two layer neural network is a powerful tool for representing complex functions. It is capable of representing the XOR function, which is a non-linear function that is not easily represented by other methods.

• Additionally, one can try using different activation functions, different architectures, or even building a deep neural network to improve the performance of the model.
• Now let’s build the simplest neural network with three neurons to solve the XOR problem and train it using gradient descent.
• Its derivate its also implemented through the _delsigmoid function.
• The analysis proves the absence of local minima, eliciting significant aspects of the structure of the error surface.
• The resulting values are then passed through the sigmoid activation function to produce the output of the hidden layer, a1.

Activation functions should be differentiable, so that a network’s parameters can be updated using backpropagation. Out of all the 2 input logic gates, the XOR and XNOR gates are the only ones that are not linearly-separable. You’ll notice that the training loop never terminates, since a perceptron can only converge on linearly separable data. Linearly separable data basically means that you can separate data with a point in 1D, a line in 2D, a plane in 3D and so on. Our 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.

Training algorithm

YET, it is simple enough for humans to understand, and more importantly, that a human can understand the neural network that can solve it. NN are very blackbox-y, it becomes hard to tell why they work really fast. Gradient descent is an iterative optimization algorithm for finding the minimum of a function.

Its absolutely unnecessary to use 2-3 hidden layers to solve it, but it sure helps speed up the process. The loss function we used in our MLP model is the Mean Squared loss function. Though this is a very popular loss function, it makes some assumptions on the data (like it being gaussian) and isn’t always convex when it comes to a classification problem. It was used here to make it easier to understand how a perceptron works, but for classification tasks, there are better alternatives, like binary cross-entropy loss. Remember that a perceptron must correctly classify the entire training data in one go. If we keep track of how many points it correctly classified consecutively, we get something like this.

Neural-Network-XOR

By using the np.random.random() function, random floats in the interval [0.0,1.0) are used to populate the weight matrices W1 and W2. In W1, the values of weight 1 to weight 9 (in Fig 6.) are defined and stored. That way, these matrixes can be used in both the forward pass and backward pass calculations. A clear non-linear decision boundary is created here with our generalized neural network, or MLP.

To train our perceptron, we must ensure that we correctly classify all of our train data. Note that this is different from how you would train a neural network, where you wouldn’t try and correctly classify your entire training data. These parameters are what we update when we talk about “training” a model. They are initialized to some random value or set to 0 and updated as the training progresses.

neural_nets

As a result, we will have the necessary values of weights and biases in the neural network and output values on the neurons will be the same as the training vector. To understand the importance of weights in the system, the ANN was also trained with various weight values instead of using the random function. As a result, the error of the system was extremely high in the beginning as all the inputs were simply passed into the activation function and moved forward into the output node. Even after 10,000 iterations, the gradient descent function was still not able to converge, and an error remained in the system. For those interested in further exploration and learning, we suggest experimenting with different parameters in the code to see how they affect the performance of the neural network.

The article provides a separate piece of TensorFlow code that shows the operation of the gradient descent. This facilitates the task of understanding neural network training. A slightly unexpected result is obtained using gradient descent since it took 100,000 iterations, but Adam’s optimizer copes with this task with 1000 iterations and gets a more accurate result. Following this, two functions were created using the previously explained equations for the forward pass and backward pass. In the forward pass function (based on equation 2), the input data is multiplied by the weights before the sigmoid activation function is applied. The output is multiplied by W2 before the error (equation 4) is calculated.