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The calculation process of BP algorithm When adding the $k$ input, the input weights of the hidden layer $h$ nodes are:\[s_h^k = \sum\limits_i {w_{ih} x_i^k}\]Output of the corresponding point:\[y_h^k = f (s_h^k) = f (\sum\limits_i {w_{ih} x_i^k}) \]Similarly, the input weights of the output layer $j$ nodes are:\[s_j^k = \sum\limits_h {w_{hj} y_h^k} = \sum\limits_h {w_{hj} F (\sum\limits_i {w_{ih} x_i^k})}\]Output of the corresponding point:\[y_j^k =

Scene:1) IMediaPlayer.cppBp, BnSpInterface_castReply->writeint32 (Setvideosurfacetexture (bufferproducer));Use in Mediaplayerservicesp2) Pass the BP object in the Mediaplayerservice process to another process using theVirtual status_t Connect (const sp{Parcel data, reply;Data.writeinterfacetoken (Iremotedisplay::getinterfacedescriptor ());Data.writestrongbinder (Iinterface::asbinder (bufferproduce));Data.writestrongbinder (Iinterface::asbinder (sinkcl

Since there was no previous contact with AI, so many articles are quoted,The links are as follows:http://blog.csdn.net/zhongkejingwang/article/details/44514073Https://www.cnblogs.com/daniel-D/archive/2013/06/03/3116278.htmlhttp://blog.csdn.net/fernchen/article/details/51861242The BP algorithm is composed of two processes, which are the forward propagation of the signal and the reverse propagation of the error by the learning process. Because the train

Single-layer perceptron does not solve the XOR problem Artificial Neural Networks (Artificial neural netwroks) have also fallen into low ebb due to this problem, but the multilayer Perceptron presented later has made the artificial neural network (Artificial neural netwroks) again high. The BP network is the most extensive one. The specific principle introduction can be obtained through the network here only describes the algorithm flow The basic

BP algorithm First, we used the Aforge.neuro Library of ASP.Function: Input a set of parameters, automatically predict the conclusionProcess: Build Network---- Training Network The construction of the network needs three parts, input node layer points, the number of hidden layer nodes, the number of output layer nodes, with these three numbers can be built a BP neural network.So how do these three numbers

Title: "Python realizes BP neural network recognition mnist data Set"date:2018-06-18t14:01:49+08:00Tags: [""]Categories: ["Python"] ObjectiveThe training set read in the. MAT format when testing the correct rate with a PNG-formatted pictureCode#!/usr/bin/env Python3# Coding=utf-8ImportMathImportSysImportOsImportNumPy asNp fromPILImportImageImportScipy.io asSiodefSigmoid (x):returnNp.array (List(Map(LambdaI1 /(1 +Math.exp (-i)) (x)))defGet_train_

Through this article, we will see that in the original framework, neural network Topology (topology), computational flow (back propagation) based on the addition of new mathematical tools, to improve, do not need to the framework of the basic structure of the big move, just a small fine-tuning, so, Applying the new mathematical mechanism to the original network structure, the computational framework does not have to be formidable enemy, as we have seen before:

BP algorithm. CPP: Defines the entry point of the console application. #include "stdafx.h" #include "iostream" #include % import data, then run the following code% input 3-dimensional training vector. has been normalized. TrainData.txt. Import dataP=traindata (:,:);P=p ';% transpose% Output 1-d expected output vector. has been normalized. Expectation.txtT=expectation (:);T=t ';% transposeThe value range of the% input vector [0,1]. Remember, it's 4 di

(costfunction, Initial_nn_params, options);% Obtain Theta1 and Theta2 back from nn_paramstheta1 = Reshape (Nn_params (1:hidden_layer_size * (input_layer_size + 1)), ... . Hidden_layer_size, (InpuT_layer_size + 1)); Theta2 = Reshape (Nn_params ((1 + (Hidden_layer_size * (input_layer_size + 1)): End), ... num_labels, (hidden_layer_size + 1) ); fprintf (' program paused. Press ENTER to continue.\n ');p ause;%% ================= part 9:visualize Weights =================% You can now "Visualiz E "W

189 #另外一个例子, can be To comment out the above example and run it again: 191 # training_sets = [192 # [[0, 0], [0]],193 # [[0, 1], [1]],194 # [[1], 0], [1]],195 # [[1, 1], [0]]196 #]197 198 # nn = neuralnetwork (len (Training_sets[0][0]), 5, Len (Training_sets[0][1])) 199 # for I in R Ange (10000): $ # TRAining_inputs, training_outputs = Random.choice (training_sets) 201 # Nn.train (training_inputs, training_outputs) 202 # Print (I, Nn.calculate_total_error (training_sets))　　The end of the la

The author says: Before having studied once, but after a period of time, many details place already blurred. Recently deduced again, in order to retain as far as possible the derivation idea, specially writes this blog post. On the one hand for their future memories, on the other hand to communicate with you to learn.For this blog post, the following description:1. This blog does not guarantee that the derivation process is completely correct, if there is a problem, please correct me.2. If neces

#-*-Coding:utf-8-*-ImportMatplotlib asOp.ImportNumPy asNpImportMatplotlib.pyplot asPLT #BP神经网络实现异或问题 X=Np.array ([[1,0,0],[1,0,1],[1,1,0],[1,1,1]]#输入层3个节点, the hidden layer is 4 nodes, so we need to 3*4 the right value V=Np.random.random ((3,4))*2-The range of the value of the value is -1~1 W=Np.random.random ((4,1))*2-The range of the value of the right to be -1~1PrintVPrint(W) Y=Np.array ([[0,1,1,0]]) LR= 0.11 #学习率 N=0 #计算迭代次数 O=0# Neural Network Ou

There are two forms of data prediction using BP neural network: 1. Predict future data based on your own data. For example, the value of GDP for 2013 is predicted by the value of the known GDP in 2000-2012. Solution: Use 2000,2001,2002 value as input, 2003 as output, and so on, 2001,2002,2003 as input, 2004 as output; ...... 2009,2010,2011 as input, 2012 as output. Prediction: The value of 2013 is predicted according to 2010,2011,2012 as input.

In the classical increment PID algorithm, the parameter that needs to be debugged is KP,KI,KD. The three parameters are adjusted by BP Neural Network, with X (i) as the input layer and the middle layer as the Simoid function: f (x) = Tanh (x) = (exp (x)-exp (-X))/(exp (x) +exp (-X)). and modify parameters by gradient descent method Key code:%output LayerFor J=1:1:outDK (j) =2/(exp (j)) +exp (K (j)) ^2;EndFor L=1:1:outDELTA3 (L) =error (k) *dyu (k)

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