I have a question regarding the Perceptron algorithm. As a mathematical concept it's very closely related to a simple linear regression, however the activation function is different between these two cases. Let's say I have a linearly separable dataset, and I want to find a hyperplane that separates the dataset into 2 sets. With a perceptron I can accomplish this. My question is, in many cases there may be more than 1 hyperplane that separates the dataset, does the perceptron always converge to the same hyperplane, or if I train the perceptron with the same dataset more than once with different initial conditions on the weights, will it always converge to the same weights or will it represent a different hyperplane each time?
Does a Perceptron always converge to the same weights for a given dataset
168 Views Asked by Riccardo Caiulo At
1
There are 1 best solutions below
Related Questions in NEURAL-NETWORK
- Influence of Unused FFN on Model Accuracy in PyTorch
- How to train a model with CSV files of multiple patients?
- Does tensorflow have a way of calculating input importance for simple neural networks
- My ICNN doesn't seem to work for any n_hidden
- a problem for save and load a pytorch model
- config QConfig in pytorch QAT
- How can I convert a flax.linen.Module to a torch.nn.Module?
- Spiking neural network on FPGA
- Error while loading .keras model: Layer node index out of bounds
- Matrix multiplication issue in a Bidirectional LSTM Model
- Recommended way to use Gymnasium with neural networks to avoid overheads in model.fit and model.predict
- Loss is not changing. Its remaining constant
- Relationship Between Neural Network Distances and Performance
- Mapping a higher dimension tensor into a lower one: (B, F, D) -> (B, F-n, D) in PyTorch
- jax: How do we solve the error: pmap was requested to map its argument along axis 0, which implies that its rank should be at least 1, but is only 0?
Related Questions in LINEAR-REGRESSION
- Batch Gradient Descent algorithm in python is returning huge values
- Error in running a multi-level mixed effects model on microbiome data
- How can I improve R2 score in my regression model? Predicting House Prices
- I have two dataframes representing two different time points. I want to run a linear regression model with data from both time points
- GMMAT model fit and AIC
- Fitting a curve using Linear regression - CLS and NMF
- Error with WLS estimation in R: missing or negative weights not allowed
- Fitted surface does not resemble the heatmap produced from the same data
- Beta coefficient of direct effect increases after controlling for mediator
- How to exclude abnormal data points and smooth the data before linear fitting
- Performing a simple ridge regression
- Why TukeyHSD test keeps returning NA for a linear model in R?
- Inquiry regarding a linear regression model using Python and pandas
- How to find the x-intercept of Weibull distribution
- PyTorch matrix multiplication shape error: "RuntimeError: mat1 and mat2 shapes cannot be multiplied"
Related Questions in PERCEPTRON
- My perceptron algorithm keeps giving me the wrong line for separating 2D linearly separable data
- How to find an Approximate Polynomial using Perceptron
- Perceptron algorithm not converging on linearly separable data
- How to write correctly a multiclass perceptron
- How can I fix my perceptron to recognize numbers?
- BrokenProcessPool while running k-fold cross-validation
- Does a Perceptron always converge to the same weights for a given dataset
- Difficulty speeding up pytorch code: training a MLP using a complicated many-to-one nonlinear function
- sklearn Perceptron not able to classify NAND function
- I need data for my ai (perceptron) in the form of 20x20 images.creating it by hand is to slow. how I train it?
- Can a network of linear activation perceptrons model non-linear functions?
- calculate AUC via decision_function for perceptron model
- Why does std::rand always generate 0?
- Accessing a portion of a numpy array that increases in length
- Perceptron Table Code in C++ Returning incorrect value in some instances
Trending Questions
- UIImageView Frame Doesn't Reflect Constraints
- Is it possible to use adb commands to click on a view by finding its ID?
- How to create a new web character symbol recognizable by html/javascript?
- Why isn't my CSS3 animation smooth in Google Chrome (but very smooth on other browsers)?
- Heap Gives Page Fault
- Connect ffmpeg to Visual Studio 2008
- Both Object- and ValueAnimator jumps when Duration is set above API LvL 24
- How to avoid default initialization of objects in std::vector?
- second argument of the command line arguments in a format other than char** argv or char* argv[]
- How to improve efficiency of algorithm which generates next lexicographic permutation?
- Navigating to the another actvity app getting crash in android
- How to read the particular message format in android and store in sqlite database?
- Resetting inventory status after order is cancelled
- Efficiently compute powers of X in SSE/AVX
- Insert into an external database using ajax and php : POST 500 (Internal Server Error)
Popular Questions
- How do I undo the most recent local commits in Git?
- How can I remove a specific item from an array in JavaScript?
- How do I delete a Git branch locally and remotely?
- Find all files containing a specific text (string) on Linux?
- How do I revert a Git repository to a previous commit?
- How do I create an HTML button that acts like a link?
- How do I check out a remote Git branch?
- How do I force "git pull" to overwrite local files?
- How do I list all files of a directory?
- How to check whether a string contains a substring in JavaScript?
- How do I redirect to another webpage?
- How can I iterate over rows in a Pandas DataFrame?
- How do I convert a String to an int in Java?
- Does Python have a string 'contains' substring method?
- How do I check if a string contains a specific word?
The short answer is no. Convergence point will depend on the initial set of values and the order in which you present samples. The proof is very simple: if you initialise the network in a solution (any hyperplane that already separates the data) perceptron algorithm does not move it. So for every separating hyperplane there exist conditions for the perceptron algorithm to end in it.
You can make similar argument for the ordering of points being presentend.