Questions tagged [perceptron]
Perceptron is a basic linear classifier that outputs binary labels.
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Perceptron - Which step function to choose
I'm studying Perceptron algorithm. Some books use this step function
1 if x>=0 else -1
where x is a dot product between the weights w and a sample x.
Other ...
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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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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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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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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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linear perceptron algorithm
Linear Classification
Consider a labeled training set shown in figure below:
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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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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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(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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Optimizing an averaged perceptron algorithm using numpy and scipy instead of dictionaries
So I'm trying to write an averaged perceptron algorithm (page 48 here for the equation) in python. Instead of storing the historical weights, I simply accumulate the weights and then multiply ...
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Why is a perceptron initialized with a random line?
I'm setting up a single perceptron for doing linear classification.
Why is the perceptron initialized with random weights and a random bias instead of just having all of the weights set to zero ...
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Machine Learning Perceptron Algorithm
I'm studying Machine Learning using Sebastian Raschka's book.
Wonder if someone could please help me to confirm if I have the following steps correct if I apply Perceptron Algorithm to Iris dataset ...
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Generalization error of a simple perceptron for a student-teacher network
I have been asked to prove the following expression given the following density probability function for a student-teacher
$ P(x,y) = \frac{1}{2\pi\sqrt{Q-R^2}} \cdot \exp\left(-\frac{1}{2}\left(\frac{...
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what happens when weghts in perceptron algorithm is first initialized with some random values which is very distant from the correct values?
take this example of a small dataset
so here there was a question that instead of initializing weight vector as zeroes what if we initialize to [1000 , -1000] (there is no offset i.e classifiers ...
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Is it possible to train a single input->neuron->relu->neuron->relu for input > 0.5?
The neural network is simply:
y=max(max(x*w+b,0)*v+d,0)
w,b is weight and bias of first neuron.
v,d is weight and bias of second neuron.
If data is for example:
<...
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Calculating weight and bias of linear perceptron on convergence given number mistakes for each sample
A linear perceptron has been trained with a set of n points (∈ ℝ²) and their corresponding labels ...
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Classical Perceptron Algorithm taking too long to evaluate on un-normalized data
I need to implement classical perceptron algorithm from scratch using numpy and pandas for an assignments.
I have done so using this algorithm:
I have a linearly seperable dataset of 568 rows and 30 ...
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Understanding perceptron learning algorithm
I was revisiting perceptron learning algorithm. The wikipedia page gives the algorithm as follows:
Initialize the weights to 0 or a small random value.
For each example $j$ in our training set $D$, ...
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OR gate perceptron in plain python - Loss won't converge
I am coding a perceptron from scratch just out of curiosity in plain python for OR gate, but a loss won't converge.
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Is bias nothing but perceptron threhold value?
I was revisiting neural network basics from this post. The perceptron follows below equation:
$$\begin{align}
y & = 1 & \text{if } \sum_{i=1}^n w_i\times x_i \geq \theta \\
& = 0 & \...
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How is calculated the error with multiple output neurons in neural network?
Machine Learning books generally explains that the error calculated for a given sample $i$ is:
$e_i = y_i - \hat{y_i}$
Where $\hat{y}$ is the target output and $y$ is the actual output given by the ...
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Similarity of perceptron criterion and SVM
In the book "Neural Networks and Deep Learning" by Aggarwal there is an exercise 2.10.1:
Consider the following loss function for training pair $(\overline{X},y)$:
$$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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