perceptron | 易学教程

plot decision boundary matplotlib

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问题 I am very new to matplotlib and am working on simple projects to get acquainted with it. I was wondering how I might plot the decision boundary which is the weight vector of the form [w1,w2], which basically separates the two classes lets say C1 and C2, using matplotlib. Is it as simple as plotting a line from (0,0) to the point (w1,w2) (since W is the weight "vector") if so, how do I extend this like in both directions if I need to? Right now all I am doing is : import matplotlib.pyplot as

Data Encoding for Training in Neural Network

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问题 I have converted 349,900 words from a dictionary file to md5 hash. Sample are below: 74b87337454200d4d33f80c4663dc5e5 594f803b380a41396ed63dca39503542 0b4e7a0e5fe84ad35fb5f95b9ceeac79 5d793fc5b00a2348c3fb9ab59e5ca98a 3dbe00a167653a1aaee01d93e77e730e ffc32e9606a34d09fca5d82e3448f71f 2fa9f0700f68f32d2d520302906e65ce 1c9b32ff1b53bd892b87578a11cbd333 26a10043bba821303408ebce568a2746 c3c32ff3481e9745e10defa7ce5b511e I want to train a neural network to decrypt a hash using just simple architecture

neural network code explanation

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问题 The following is an implementation of a simple Perceptron supplied in a blog. input = [0 0; 0 1; 1 0; 1 1]; numIn = 4; desired_out = [0;1;1;1]; bias = -1; coeff = 0.7; rand('state',sum(100*clock)); weights = -1*2.*rand(3,1); iterations = 10; for i = 1:iterations out = zeros(4,1); for j = 1:numIn y = bias*weights(1,1)+... input(j,1)*weights(2,1)+input(j,2)*weights(3,1); out(j) = 1/(1+exp(-y)); delta = desired_out(j)-out(j); weights(1,1) = weights(1,1)+coeff*bias*delta; weights(2,1) = weights(2

Neural Networks normalizing output data

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问题 I have a training data for NN along with expected outputs. Each input is 10 dimensional vector and has 1 expected output.I have normalised the training data using Gaussian but I don't know how to normalise the outputs since it only has single dimension. Any ideas? Example: Raw Input Vector: -128.91, 71.076, -100.75,4.2475, -98.811, 77.219, 4.4096, -15.382, -6.1477, -361.18 Normalised Input Vector: -0.6049, 1.0412, -0.3731, 0.4912, -0.3571, 1.0918, 0.4925, 0.3296, 0.4056, -2.5168 The raw

decision boundary of perceptron too small

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问题 I am trying to plot the decision boundary of a perceptron algorithm and am really confused about a few things. My input instances are in the form [(x1,x2),target_Value], basically a 2-d input instance and a 2 class target_value [1 or 0]. My weight vector hence is in the form: [w1,w2] Now I have to incorporate an additional bias parameter w0 and hence my weight vector becomes a 3x1 vector? is it 1x3 vector? I think it should be 1x3 since a vector has only 1 row and n columns. Now let's say I

Clarification on bias of a perceptron

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问题 Isn't it true that if a bias is not present, a line passing through origin should be able to linearly separate the two data sets?? But the most popular answer in this -->> question says y ^ | - + \\ + | - +\\ + + | - - \\ + | - - + \\ + ---------------------> x stuck like this I am confused about it. Do you mean the origins in figure above are somewhere in middle of x-axis and y-axis? Can somebody please help me and clarify this? 回答1: Alright, so perhaps the original ASCII graph was not 100%

Advice for algorithm choice

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问题 I have to do a project that tries to scan the shape of the vehicles and detect what type of vehicle it is , the scanning will performed with a sensors called “vehicle scanner” they are just 50 beams of lights, each beam with receptor and emission as it is shown in the picture: I get from the sensors the raw sate of each beam (block or unblock) and with that continuous scanning we can create a probably very low res image of the vehicle. My question is what algorithms/technique I can use to

Advice for algorithm choice

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I have to do a project that tries to scan the shape of the vehicles and detect what type of vehicle it is , the scanning will performed with a sensors called “vehicle scanner” they are just 50 beams of lights, each beam with receptor and emission as it is shown in the picture: I get from the sensors the raw sate of each beam (block or unblock) and with that continuous scanning we can create a probably very low res image of the vehicle. My question is what algorithms/technique I can use to detect and identify the shape of the vehicle, we want to count the wheels, and if we can, try to identify

Intuition for perceptron weight update rule

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问题 I am having trouble understanding the weight update rule for perceptrons: w(t + 1) = w(t) + y(t)x(t) . Assume we have a linearly separable data set. w is a set of weights [w0, w1, w2, ...] where w0 is a bias. x is a set of input parameters [x0, x1, x2, ...] where x0 is fixed at 1 to accommodate the bias. At iteration t , where t = 0, 1, 2, ..., w(t) is the set of weights at iteration t . x(t) is a misclassified training example. y(t) is the target output of x(t) (either -1 or 1). Why does

Parameter Tuning for Perceptron Learning Algorithm

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问题 I'm having sort of an issue trying to figure out how to tune the parameters for my perceptron algorithm so that it performs relatively well on unseen data. I've implemented a verified working perceptron algorithm and I'd like to figure out a method by which I can tune the numbers of iterations and the learning rate of the perceptron. These are the two parameters I'm interested in. I know that the learning rate of the perceptron doesn't affect whether or not the algorithm converges and