Questions tagged [perceptron]

Perceptron is a basic linear classifier that outputs binary labels.

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Perceptron with two or more outputs

I've done perceptron with one output already, but now i want to try doing it with at least two outputs. I can't find any example in google. Is it possible? Can you give me a minimal example? I want to ...
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Why we use an activation function for introducing nonlinearity instead of a polynomial Perceptron implementation?

I perceive a single perceptron as a single linear function $y = a_1x_1 + a_2x_2 + ... + a_nx_n + b_0$ with a goal to calculate the best weights combination $ w_1, w_2, ..., w_n $ that minimizes the ...
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Visualizing a Perceptron

I wanted to visualize how a perceptron learns, so I made a class that performs gradient descent. To show the decision, I plot a plane showing where positive examples and negative examples are, ...
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What issue is there, when training this network with gradient descent? [closed]

Suppose we have the following fully connected network made of perceptrons with a sign function as the activation unit, what issue arises, when trying to train this network with gradient descent?
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why use one regularisation technique over another?

why should I prefer L1 over L2, in fully-connected-layer or convolution? why use dropout between 2 layers, when there is the option of regularising a layer(or both) with something like L1 or L2? and ...
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How do I include the Bias term in the Pegasos algorithm?

I have been asked to implement the Pegasos algorithm as below. It is similar to the Peceptron algorithm but includes eta and lambda terms. However, there is no bias term below and I don't know how ...
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Why there is only one type of artificial neuron?

I find it strange that so many deep learning tricks and improvements have been invented in the past decade but I never heard about someone trying out different models of the artificial neuron other ...
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predict multiple letters in pixels matrix

I have a multilayer perceptron model that is trained to recognize handwritten English letters from an image. In the training set each image matrix had 784 pixel values. The labels of these images ...
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Choosing a learning rate interval for linear separation with a perceptron

I'm having an issue with an exercise regarding the use of a perceptron for the linear separation of two sets M0 and M1. The question at hand is, how to go about finding an interval for a reliable ...
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Should a bias neuron be connected to previous neurons?

I'm trying to add a bias neuron to my neural network that uses the backpropagation algorithm. I'm trying to figure out how I should go about this, should I treat the bias neuron as a regular neuron? ...
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Do we use the same threshold as in training when classifying using a linear classifier?

I've got a binary linear classifier and while training I am using 0 as my threshold. What I was wondering is when we change our threshold to be 1 let's say. So our function becomes, $$ f(X) = \left\{ ...
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I'm worried that I'm training my model wrong

So I'm trying to classify some fashion mnist like photos into either boots or sneakers. I'm using a perception from sklearn to do so. The data set is a CSV containing pixel values. The model is ...
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Threshold logic unit (McCulloch-Pitts 1943) vs. Perceptron (Rosenblatt 1958)

I have searched various sources to find out what distinguishes the McCulloch-Pitts neuron from the perceptron invented by Rosenblatt. In most sources only one of these elements is considered, in ...
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Calculation of VC dimension of simple neural network

Suppose I have a perceptron with one-hidden layer, with the input - one real number $x \in \mathbb{R}$, and the activation function of the output layers - threshold functions: $$ \theta(x) = \begin{...
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Performing a linear regression with Perceptron

I was wondering about the link between the linear regression and the perceptron! Perceptrons were used as binary classifiers i.e to classify binary labels ( 0 or 1 ). My question is How can you ...
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How is calculated the error with multiple output neurons in neural network?

Machine Learning books generally explains that the error calculated for a given sample $i$ is: $e_i = y_i - \hat{y_i}$ Where $\hat{y}$ is the target output and $y$ is the actual output given by the ...
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What does n means in neural network neuron output?

I've found this equation that explains the output of a neuron in a MLP network: $y(n) = f(\mathbf{w}^T \mathbf{x}(n) + b)$ I can understand the general context, but since i have no background with ...
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Equation of a Multi-Layer Perceptron Network

I'm writing an article about business management of wine companies where I use a Multi-Layer Perceptron Network. My teacher then asked me to write an equation that lets me calculate the output of the ...
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What's wrong with my implementation of the Adaline algorithm?

I'm working through the textbook called Learning From Data and one of the problems from the first chapter has the reader implement the Adaline algorithm from scratch and I chose to do so using Python. ...
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SciKit Learn: Multilayer perceptron early stopping, restore best weights

In the SciKit documentation of the MLP classifier, there is the early_stopping flag which allows to stop the learning if there is not any improvement in several ...
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linear perceptron algorithm

Linear Classification Consider a labeled training set shown in figure below: ...
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Understanding computations of Perceptron and Multi-Layer Perceptrons on Geometric level

I am currently watching amazing Deep Learning lecture series from Carnegie Melllon University, but I am having little bit of trouble understanding how Perceptrons and MLP are making their decisions on ...
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How do we define a linearly separable problem?

When we talk about Perceptrons, we say that they are limited for approximating functions that are linearly separable, while Neural Networks that use non-linear transformations are not. I am having ...
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difference between empirical risk minimization and structural risk minimization?

I understand the meaning of empirical risk minimization as separate topic and was reading about structural risk minimization, it is hard for me to understand the difference between these two. I read ...
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Why must a CNN have a fixed input size?

Right now I'm studying Convolutional Neural Networks. Why must a CNN have a fixed input size? I know that it is possible to overcome this problem (with fully convolutional neural networks etc...), and ...
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Convergence speed of perceptron algorithm

I was reading the convergence proof for the perceptron algorithm. It says under the assumption that there are some $R$, $\theta^*$ with $|\theta^*| = 1$ and $\gamma > 0$, such that $y_t(x_t\cdot \...
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Multilayer Perceptron: What is the value used to update the weights in the hidden layers?

As i understand for the output layer the error rate is used with the mean squared error function to update the weights. For the hidden layers as well? Does that make sense?
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About different structures of neural network

https://www.mathworks.com/help/deeplearning/ref/fitnet.html is the tutorial that I am following to understand fitting data to a function. I have few doubts regarding structure and terminologies which ...
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Conceptual questions on MLP and Perceptrons

I am facing some confusion regarding the terminologies assocaiated to classification and regression problems esp. using the MLP and Perceptron models. These are the following: 1) When the data is ...
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How to find bias for perceptron algorithm?

My question is very basic. I am starting with ML and am working on the perceptron algorithm. I successfully computed the weights for this input data: ...
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Error while writing perceptron algorithm binary classifier

I am a beginner and I am designing an binary classifier using Perceptron algorithm using FASHION-MNIST dataset. While designing the same I have written the following code: ...
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Simple perceptron on python

I want to create a simple perceptron with three inputs. $ \sigma(w_1x_1 + w_2x_2 + w_3x_3) $, where $ \sigma = \frac{1}{1 + e^{-x}.} $ $$ L(w) = \sum_i^3(y_i -\sigma(w_1x_1 + w_2x_2 + w_3x_3)); $$ $ \...
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Similarity of perceptron criterion and SVM

In the book "Neural Networks and Deep Learning" by Aggarwal there is an exercise 2.10.1: Consider the following loss function for training pair $(\overline{X},y)$: $$L=max(0, a -y(\overline{W} \...
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What are contexts where a perceptron model could be defined?

per this post, a perceptron could use the logit function as the activation function. per wiki In the context of neural networks, a perceptron is an artificial neuron using the Heaviside step ...
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What is the hypothesis space used by this AND gate Perceptron?

Per this post The hypothesis space used by a machine learning system is the set of all hypotheses that might possibly be returned by it. Per this post, the Perceptron algorithm makes prediction ...
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How does permutation of training data improve convergence time when training a perceptron or neural network model? [duplicate]

I'm currently studying some basic concepts regarding Deep Learning and Neural Networks with this material. When discussing the training algorithm for a perceptron, the author states that looping ...
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Version of Perceptron

If we change the $ywx<0$ condition (for performing update) to $ywx<1$ like in SVM (but without adding regularization to maximize the margin), is there any difference from the basic perceptron (...
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Geometric interpretation of MLP output

I am really interested in the geometric interpretation of perceptron outputs, mainly as a way to better understand what the network is really doing, but I can't seem to find much information on this ...
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Abstraction in Neural Networks

In my nonlinear dynamics class in college, we discussed a simple perceptron with two input neurons and one output neuron that is trained on the patterns ...
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Perceptron algorithm

I am trying to build a simple linear classifier. I have two classes A and B each with two features [x, y] and hence a 2d dataset. Now, I need to find the equation of the line that separates the two ...
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Ambiguity in Perceptron loss function (C. Bishop vs F. Rosenblatt)

Bishop's Perceptron loss On one hand, it is stated in equation 4.54 of Chris Bishop's book (pattern recognition and machine learning) that the loss function of perceptron algorithm is given by: $${...
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(Almost) linearely separable dataset, where can I find one?

I'm implementing the perceptron algorithm and the voted perceptron algorithm for an assignment for university. For that I need to find some decent datasets.. I've tried the UCI repos and I've come up ...
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neural networks error function: is global minimum desirable?

In "Elements of statistical learning" page 395 the authors state that, relative to R(θ), the regression/classification error function in a neural network such as a multi layer perceptron: Typically ...
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How do two perceptrons produce different linear decision boundaries?

I'm trying to visualize how two perceptrons converge to two different decision boundaries (which is ultimately used to create the classifier for the non-linearly separable data). Source: https://tdb-...
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Interpreting MLP output

I just wrote an MLP in Python. After having trained it, I pass in some test data to see the result, and I get an array of decimal numbers at the output, rather than the desired binary output. For ...
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Coding MLP: good practices?

I recently finished coding my own MLP neural network in Python. To make my code easier to read, I separated the MLP, into classes; the network class, the layers class and the neuron class, where the ...
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Is the prediction algorithm absolutely the same for all linear classifiers?

Is the prediction algorithm absolutely the same for all linear classifiers and linear regression algorithms? As known, any linear classifier can be described as: ...
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Confusion on Delta Rule and Error

I'm currently reading Mitchell's book for Machine Learning, and he just started gradient descent. There's one part that's really confusing me. At one point, he gives this equation for the error of a ...
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Perceptron Primitive Boolean Functions

Thanks for reading. I'm currently reading Tom Mitchell's Machine Learning (I'm a beginner into ML), and I'm on chapter 4 about perceptrons. I'm really confused about this paragraph: I understand the ...
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Why can't the XOR linear inseparability problem be solved with one perceptron - like this?

Consider a perceptron where $w_0=1$ and $w_1=1$: Now, say we use an activation function $f(x)=1,~for~x=1$$~~~~~~~~~~~~~0, otherwise$ The output is then summarised as: $x_0~~~~~x_1~~~~~w_0*x_0 + w_1*...