python neural network library

}; output[0] =New Double[] {0};input[1] =New Double[] {0,1}; output[1] =New Double[] {0};input[2] =New Double[] {1,0}; output[2] =New Double[] {0};input[3] =New Double[] {1,1}; output[3] =New Double[] {1};2. Selecting incentive functions and learning rulesThe excitation function in aforge.net needs to implement the Iactivationfunction interface, which realizes 3 kinds of aforge.net:BipolarsigmoidfunctionSigmoidfunctionThresholdfunction (Threshold function)Our excitation functions (activation fun

to the learning objective function in the input instanceThe inverse propagation algorithm for training neurons is as follows:C + + Simple implementation and testingThe following C + + code implements the BP network, through 8 3-bit binary samples corresponding to an expected output, training BP network, the last trained network can be the input three binary numb

The introduction of convolution neural network Original address : http://blog.csdn.net/hjimce/article/details/47323463 Author : HJIMCE Convolution neural network algorithm is the algorithm of n years ago, in recent years, because the depth learning correlation algorithm for multi-layer

common theory of neural network structure and working principle, simple and good understanding, recommended to watch2, the mathematical derivation of the inverse propagation algorithm, if it is too complicated to temporarily skip3,matlab Code and Image Library(1) Plain English explain the traditional neural networkFir

The significance of Gevent library lies in the concurrent high-performance network programming support. here we will explain how to install and use Gevent in the Python network programming library. let's take a look at the multi-process programming supported by Gevent: Insta

learning process in the network of neurons in the connection right to change the basis of a certain adjustment rules, (Bp algorithm in the weight adjustment is a gradient descent strategy, the following will be described in detail)The learning process of the BP network is shown in the following illustration:(Baidu Library Search, can explain the problem on the l

same as the Urllib library, so in this case the use of the process is completely meaningless. So what?One way is to use the socket module under Gevent, which we can import through the "from gevent import socket". But the more common approach is to use monkey pudding (Monkey patching): fromGeventImportmonkey; Monkey.patch_socket ()ImportgeventImportSocket URLs= ['www.baidu.com','www.gevent.org','www.python.org']jobs= [Gevent.spawn (socket.gethostbynam

is unroll into a vector, then using the existing gradient descent algorithm in the library to find the optimal parameters, and finally reshape into a matrix form; The reason for this is that the parameters of the ready-made gradient descent algorithm, the Inittheta requirement, must be in the form of a vector.3,gradient CheckingThis is a mathematical method to seek partial derivative.It can be used to verify that the gradient descent algorithm is imp

), (3,6), (6,7= g.to_ undirected () Nx.draw (G) plt.savefig ("wuxiangtu.png") plt.show ()Weighted graphBoth the graph and the graph can give the edge weight, the method used is Add_weighted_edges_from, it accepts 1 or more triples [u,v,w] as parameters, where U is the starting point, V is the end point, W is the weight.Example 1:#!-*-coding:utf8-*- ImportNetworkx as NXImportMatplotlib.pyplot as PLTG= NX. Graph ()#Create an empty graph GG.add_edge (2,3)#Add an edge 2-3 (implicitly adding two node

inactive activation function to set different learning rates .The number of hidden layer nodes has little effect on the recognition rate, but the number of nodes increases the computational capacity and makes training slow.The activation function has a significant effect on the recognition rate or the rate of convergence. The precision of S-shape function is much higher than that of linear function in the approximation of the higher curve, but the computational amount is much larger. The learni

Installation (with CentOS as an example)Gevent relies on libevent and Greenlet: 1. Installing LibeventDirect Yum Install Libevent Then configure the Python installation 2. Installing Easy_install(1) Wget-q http://peak.telecommunity.com/dist/ez_setup.py (2) Use Python ez_setup.py (3) Use Easy_install to see if the command is available, and if it is not available, add the path3. Installing Greenlet(1) Yu

Eventlet is an open-source highly scalable Python network programming library. According to the official introduction, the features are as follows: The non-blocking I/O model uses epoll or libevent. For the advantages of epoll, see epoll model and epoll essence in Linux. Coroutines allows developers to adopt a blocking development style, but can achieve no

ilsvrc champion? In the vggnet, 2014 ilsvrc competition model, image recognition is slightly inferior to googlenet, but it has a great effect in many image conversion learning problems (such as object detection ). Fine-tuning of Convolutional Neural Networks What is fine-tuning?Fine-tuning is to use the weights or partial weights that have been used for other targets, pre-trained models, and start training as the initial values. So why don't we rando