perceptron

问题 I tried to use a single perceptron to predict the XOR gate. However, the results seem to be completely random and I cannot find the error. What am I doing wrong here ? - Is my training method wrong? - or is there any error in the perceptron model ? - or a single perceptron cannot be used for this problem ? class Perceptron { constructor(input_nodes, learning_rate) { this.nodes = input_nodes; this.bias = Math.random() * 2 - 1; this.learning_rate = learning_rate; this.weights = []; for (let i =

问题 I've always heard that the XOR problem can not be solved by a single layer perceptron (not using a hidden layer) since it is not linearly separable. I understand that there is no linear function that can separate the classes. However, what if we use a non-monotonic activation function like sin() or cos() is this still the case? I would imagine these types of functions might be able to separate them. 回答1: Yes , a single layer neural network with a non-monotonic activation function can solve

问题 I've always heard that the XOR problem can not be solved by a single layer perceptron (not using a hidden layer) since it is not linearly separable. I understand that there is no linear function that can separate the classes. However, what if we use a non-monotonic activation function like sin() or cos() is this still the case? I would imagine these types of functions might be able to separate them. 回答1: Yes , a single layer neural network with a non-monotonic activation function can solve

问题 I've always heard that the XOR problem can not be solved by a single layer perceptron (not using a hidden layer) since it is not linearly separable. I understand that there is no linear function that can separate the classes. However, what if we use a non-monotonic activation function like sin() or cos() is this still the case? I would imagine these types of functions might be able to separate them. 回答1: Yes , a single layer neural network with a non-monotonic activation function can solve

问题 Here is my code: import pandas as pa from sklearn.linear_model import Perceptron from sklearn.metrics import accuracy_score def get_accuracy(X_train, y_train, y_test): perceptron = Perceptron(random_state=241) perceptron.fit(X_train, y_train) result = accuracy_score(y_train, y_test) return result test_data = pa.read_csv("C:/Users/Roman/Downloads/perceptron-test.csv") test_data.columns = ["class", "f1", "f2"] train_data = pa.read_csv("C:/Users/Roman/Downloads/perceptron-train.csv") train_data

问题 I got a problem in understending the difference between MLP and SLP. I know that in the first case the MLP has more than one layer (the hidden layers) and that the neurons got a non linear activation function, like the logistic function (needed for the gradient descent). But I have read that: "if all neurons in an MLP had a linear activation function, the MLP could be replaced by a single layer of perceptrons, which can only solve linearly separable problems" I don't understand why in the

问题 First of all, I'm new to this concept of DOD, and while new to it, I find it really exciting from a programmer perspective. I made a Multi-Layer Perceptron a while ago as an OO project for myself, and since I'm learning DOD now, I thought it would be nice to make it with this paradigm. struct Neuron { double bias; double error; }; struct Layer { Neuron* neurons; double* output; double** connections; unsigned numberNeurons; }; struct Network { unsigned numberInput; double* input; std::vector

问题 with single-layer perceptron it's easy to find the equation of the "separating line" (I don't know the professional term), the line that separate between 2 types of points, based on the perceptron's weights, after it was trained. How can I find in a similar way the equation of the curve (not straight line) that separate between 2 types of points, in a multi-layer perceptron? thanks. 回答1: This is only an attempt to get an approximation to the separating boundary or curve. Dataset Below I

问题 The bounty expires in 2 days . Answers to this question are eligible for a +100 reputation bounty. AlwaysLearning wants to draw more attention to this question. This question is totally re-written after I confirmed my results (the Python Notebook can be found here) with a piece of code written by someone else (can be found here). Here is that code instrumented by me to work with my data and to count epochs till convergence: import numpy as np from matplotlib import pyplot as plt class

问题 Hi I'm pretty new to Python and to NLP. I need to implement a perceptron classifier. I searched through some websites but didn't find enough information. For now I have a number of documents which I grouped according to category(sports, entertainment etc). I also have a list of the most used words in these documents along with their frequencies. On a particular website there was stated that I must have some sort of a decision function accepting arguments x and w. x apparently is some sort of