bp video

[]) { DoubleHiddenin[hiddennode]; DoubleHiddenout[hiddennode]; DoubleOutin[outnode]; DoubleOutout[outnode]; DoubleX[innode]; for(inti =0; i ) {X[i]=P[i]; } for(inti =0; i ) {Hiddenin[i]=0.0; for(intj =0; J ) {Hiddenin[i]+ = w1[i][j] *X[j]; } Hiddenout[i]= Sigmod (Hiddenin[i] +B1[i]); } for(inti =0; i ) {Outin[i]=0.0; for(intj =0; J ) {Outin[i]+ = w2[i][j] *Hiddenout[j]; } Outout[i]= Sigmod (Outin[i] +B2[i]); } for(inti =0; i ) {cout"The prediction is"Endl; }}bpnet::bpnet () {e=

The contents of this article for I learn to understand, there is wrong place also please point out. The so-called BP neural Network (back propagation) is to use the known data set along the neural network forward to calculate the predicted value, so as to obtain the deviation between the predicted value and the actual value, and then use this deviation and the neural network deviation gradient descent direction to adjust the weight parameter between

BP Neural Network is a multi-layer feedforward neural network which is trained according to the error inverse propagation algorithm, and is the most widely used neural network at present.BP neural network error reverse propagation neural network: Initialization of the right and threshold values Given P training samples XP (p=1,2,..., p) and the corresponding ideal output DP (p=1,2,... p) Forward delivery of information:Compute the out

BP (backward propogation) neural networkSimple to understand, neural network is a high-end fitting technology. There are a lot of tutorials, but in fact, I think it is enough to look at Stanford's relevant learning materials, and there are better translations at home: Introduction to Artificial neural network, direct translation and Stanford Tutorial: "Neural network-UFLDL" BP principle, direct translation

. Inverse propagation algorithm called BP algorithm (back propagation)Now, we can build a multilayer neural network using the perceptron of the sigmoid function described above, for simplicity, here we use a three-layer network to analyze. Assume that network topology 2.1 shows.Figure 2.1 BP network extension structure [3]The operation of the network is as follows: When a sample is entered, the eigenvector

network into three layers: the input layer, the output layer, and the hidden layer in the middle. We cannot directly calculate the gradient for the hidden layer in the middle, and then modify the parameters. Therefore, you can only use the export chain rules to calculate the gradient from the output layer and then update the parameters. This is also the legendary BP neural network. My learning materials are: Professor Hagan's book: neural_network_des

"Fully connected BP neural network"This paper mainly describes the forward propagation and error reverse propagation of the fully connected BP neural network, all of which are used by Ng's machine learning. An all-connected neural network diagram is given.1 forward propagation 1.1 forward propagationThe input and output of the neurons in the l -layer were calculated.1.1.1 Paranoid 1 o'clockVector overall fo

ObjectiveThe training of multilayer networks requires a powerful learning algorithm, in which the BP (errorbackpropagation) algorithm is the representative of success, and it is the most successful neural network learning algorithm to date.Today, we will discuss the principle of BP algorithm and the derivation of the formula.Neural networkFirst of all, introduce the neural network briefly, introduce the bas

For the BP neural Network algorithm, because it has not been applied to the project, the occasional time of today's study,The basic idea of this algorithm is this: continuous iterative optimization of the network weights, so that the mapping between the input and output with the desired mapping relationship, using gradient descent method to update the weights of each layer, the objective function to minimize.1: Initialize the network weights and neuro

BP algorithm:　　1. is a supervised learning algorithm, often used to train multilayer perceptron.2. The excitation function required for each artificial neuron (i.e. node) must be micro-(Excitation function: the function relationship between the input and output of a single neuron is called the excitation function.) ）(If the excitation function is not used, each layer in the neural network is simply a linear transformation, and the multilayer input is

This paper is reproduced from http://blog.csdn.net/ironyoung/article/details/49455343 BP (backward propogation) neural networkSimple to understand, neural network is a high-end fitting technology. There are a lot of tutorials, but in fact, I think it is enough to look at Stanford's relevant learning materials, and there are better translations at home: Introduction to Artificial neural network, direct translation and Stanford Tutorial: "Neural netw

Introduction Neural network is the foundation of deep learning, and BP algorithm is the most basic algorithm in neural network training. Therefore, it is an effective method to understand the depth learning by combing the neural network structure and the BP algorithm. Reference UFLDL,BP derivation, neural network textbook. Neural network Structure Typical netwo

smaller the error on the validation set, and therefore the best training times. In the implementation, it is often possible to determine the change of validation set error as the standard for terminating training. For example, set on the verification set, the error after the training is 20% greater than the previous training error, it is considered that the result of the previous training is the best, then retain the network weights of the previous training as a result of training. Copyright N

) *delta_d*activationspooled'+lambda*wd;Bd_grad = (1./numimages) *sum (delta_d,2); %Note Here are the requirements and%The J to the right of reshape in the corresponding graph delta_s= Wd'*delta_d;delta_s=reshape (delta_s,outputdim,outputdim,numfilters,numimages);% corresponds en route 1/4, each component of the delta_s is expanded to 4 forI=1: Numimages forJ=1: Numfilters Delta_c (:,:, J,i)= (1./pooldim^2) *Kron (Squeeze (delta_s (:,:, J,i)), ones (Pooldim)); EndEnd%for the lower left, but at t

First Kind%%% Solving XOR problem with neural network clearclcclosems=4;% set 4 samples a=[0 0;0 1;1 0;1 1];% Set input vector y=[0,1,1,0];% set output vector n=2;% number of inputs m=3;% the number of hidden layers k=1;% the number of output layers W=rand (n,m);% is the value of the input layer to the hidden layer to assign the initial values V=rand (M,K); The weight value of the hidden layer to the output layer is weighted Yyuzhi=rand (1,m), the threshold value of the input layer to the hidden

1. Write data to the CSV file, you should be able to directly implement the Python code to write the dataset, but I read this piece of file is not very skilled, and so I succeeded, plus, here I write the dataset directly into Excel2. Then change the suffix to. csv and use Pandas to readImport Matplotlib.pyplot as Pltfile = ' bp_test.csv ' import pandas as Pddf = pd.read_csv (file, header=none) x = df.iloc[:,].v Aluesprint (x)Read results[ -1. -0.9602] [ -0.9 -0.577] [ -0.8 -0.0729] [ -

Ide:jupyterNow I know the source of the data set two, one is the CSV dataset file and the other is imported from sklearn.datasets1.1 Data set in CSV format (uploaded to Blog park----DataSet. rar)1.2 Data Set Read1 " Flower.csv " 2 Import Pandas as PD 3 df = pd.read_csv (file, header=None)4 df.head (10)1.3 Results2.1 Data sets in Sklearn1 from Import Load_iris # importing DataSet Iris2 iris = Load_iris () # load DataSet 3 iris.data[:10]2.2 Reading resultsPython Build

*samplelengthdoublematrix cost;//error Matrix: 1* Samplelengthdoublematrix accuracy;//accuracy Matrix: 1*samplelengthprivate listAnother class that implements the interface is minibatchpropagation. He propagates the samples internally in parallel, then synthesizes each minipatch result, using the Batchdataproviderfactory class and the Basepropagation class internally.TrainerThe trainer interface is defined as:Public interface Trainer {public void train (Net net,dataprovider provider);The simp

^m=f ' (s_j^m) (t_j^k-y_i^m) =y_j^m (1-y_j^m), (T_j^k-y_j^m to \text{function}) \]It is obtained by the difference between the actual output and the desired target value.5. Calculate the error value of the previous layer nodes (using the formula (2))\[\delta_j^{m-1}=f ' (s_j^{m-1}\sum_iw_{ji}\delta_i^m) \]The inverse error is calculated by layer, until the error value of each node in each layer is computed.6. Using weighted correction formula\[\delta w_{ij}^m = \eta \delta _j^m y_i^{m-1}\]and re

Neural network model is generally used for classification, regression prediction model is not common, this paper based on a classification of BP neural Network, modified it to achieve a regression model for indoor positioning. The main change of the model is to remove the non-linear transformation of the third layer, or replace the nonlinear activation function sigmoid with the f (x) =x function. The main reason for this is that the output range of th