java neural network example code

at the same time. We pass in a matrix (instead of a vector) at the input, and the columns of this matrix represent the vectors in this batch. In forward propagation, each node multiplies the input by multiplying the weight matrix, adding a bias matrix, and applying sigmoid functions to get the output, which is also calculated in a similar way when it is transmitted in reverse. Explicitly write this method of reverse propagation and modify network.py it so that it is calculated using this comple

"Proficient in MATLAB neural network" in the book example 10-16, when creating a BP network, the original wording is:　　NET = NEWFF (Minmax (alphabet), [S1 s2],{' Logsig ' Logsig '}, ' Traingdx ');Because there are hints in the process of operation, naturally want to change to a new way of writing (refer to the previous

fromSklearn.metricsImportConfusion_matrix, Classification_report fromSklearn.preprocessingImportLabelbinarizer#From neuralnetwork import neuralnetwork fromSklearn.cross_validationImporttrain_test_splitdigits=load_digits () X=Digits.datay=Digits.targetx-= X.min ()#normalize the values to bring them into the range 0-1X/=X.max () nn= Neuralnetwork ([64,100,10],'Logistic') X_train, X_test, Y_train, Y_test=Train_test_split (X, y) labels_train=Labelbinarizer (). Fit_transform (y_train) labels_test=La

Python-based radial basis function (RBF) neural network example, pythonrbf This article describes the radial basis function (RBF) neural network implemented by Python. We will share this with you for your reference. The details are as follows: from numpy import array, append

Recurrent neural network language modeling toolkit source code (8), recurrentneuralReferences: RNNLM-Recurrent Neural Network Language Modeling Toolkit (Click here to read) Recurrent neural

simple momentum-adaptive learning rate algorithm.Its iterative formula is as follows:$ $W (t+1) =w (t) +\delta W (t) $$$$\delta W (t) =rate (t) (1-moment (t)) G (t+1) +moment (t) \delta W (t-1) $$$ $rate (t+1) =\begin{cases} rate (t) \times 1.05 \mbox{if} cost (t) $ $moment (t+1) =\begin{cases} 0.9 \mbox{if} cost (t) The sample code is as follows:public class Momentadaptlearner implements learner {Net net;double moment = 0.9;double LMD = 1.05;doubl

Recursive neural Network language Model tool address: http://www.fit.vutbr.cz/~imikolov/rnnlm/1. Simple use of toolsTools are: rnnlm-0.3eStep1. Files extracted, extracted after the file is:Figure 1.rnnlm-0.3e the extracted fileStep2. Compiling toolsCommand:Make cleanMakeCould be an error saying this x86_64-linux-g++-4.6 command can't be found.If the above error occurs, simply change the first line of the ma

In the It seems that I have found some shortcomings in the original version modified by netizens, And I have improved it accordingly. 1. The training sample result is not saved, so that the training data is re-trained every time you use it, which takes time: Although methods for saving arrays W, V, B1, and b2 are provided in the class, the constructor is faulty, and the data used in the following sections are not saved: in_rate innum hidenum outnum. The author has made the following improvement

*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

Learning materials: Related code for TF 2017 built new visual instructional Code machine learning-Introduction series what is RNN machine learning-Introduction series What is Lstm RNN this code sets RNN parameters based on this code on the Web This time we will use RNN to classify the training (classification). will co

LeNet5 convolution neural network forward propagation # TensorFlow actual combat Google Depth Learning Framework 06 image recognition and convolution neural network # WIN10 Tensorflow1.0.1 python3.5.3 # CUDA v8.0 cudnn-8.0-windows10-x64-v5.1 # filename:LeNet5_infernece.py # LeNet5 forward propagate import TensorFlow

the design of BP Neural network should pay attention to the following several questions: 1. Number of layers of the network. The general three-layer network structure can approximate any rational function. Although the increase of network layer can improve the precision of c

Sample Code for caffe feature Visualization Many readers read the previous two articles Summarize the research process of using caffe to run image data. Summary of deep learning practical experience 2-accuracy improved again, reaching 0.8. Then, I want to know how to implement feature visualization. To put it simply, it is to let the neural network spread forwa

activation functions of neural networks (Activation function) This blog is only for the author to record the use of notes, there are many details of the wrong place. Also hope that you crossing can forgive, welcome criticism correct. More related blog please poke: http://blog.csdn.net/cyh_24 If you want to reprint, please attach this article link: http://blog.csdn.net/cyh_24/article/details/50593400 In daily coding, we will naturally use some activat

manual setting in the network are macroDefinition.h, including the number of hidden neurons, the dimension of eigenvector and so on. The accompanying code here only shows the core code of the Code, namely Cinput, Chidden, Coutput, Calgothrim.network manually set parameters in MacroDefinition.h, defined as macros, the

Recurrent neural Network Language Modeling Toolkit tool use Click to open linkFollow the training schedule to learn the code:Structure in Trainnet ():Step1.learnvocabfromtrainfile () Statistics all the word information in the training file, and organize the statistic good informationThe data structures involved:Vocab_wordOcab_hash *intThe functions involved:Addwordtovocab ()For a word w, the information is

Neural networks:learning Last week's course learned the neural network forward propagation algorithm, this week's course mainly lies in the neural network reverse renewal process. 1.1 Cost function Let's recall the value function of logistic regression.J (θ) =1m[∑mi=1y (i)

01 Simple Convolution network # "TensorFlow Combat" TensorFlow realize convolution neural network # WIN10 Tensorflow1.0.1 python3.5.3 # CUDA v8.0 cudnn-8.0-windows10-x64-v5.1 # Filen ame:sz05.01.py # Simple convolution network from tensorflow.examples.tutorials.mnist import input_data import tensorflow as tf mnist =

The Network format is defined by reading a file. The file format is as follows: Input Image length input image width hidden layer neuron count output neuron countNumber of different network structures[Number of hidden layer neurons connected at different locations][Position table of input neurons connected by hidden layer neurons] The following is an example: 24