convolutional neural network python code

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-8ImportMathImportSysImportOsImportN

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

ImportNumPy as NPImportMatplotlib.pyplot as PltX=np.array ([[1,3,3], [1,4,3], [1,1,1]]) Y=np.array ([1,1,-1]) W= (Np.random.random (3)-0.5) * *Print(W) LR=0.11N=0O=0defupdate ():Globalx,y,w,lr,n N+=1O=np.sign (Np.dot (x,w.t)) W_c=lr* (Y-o. T). dot (X))/Int (x.shape[0]) W=w+W_c for_inchRange (100): Update ()Print(W)Print(n) O=np.sign (Np.dot (x,w.t))if(o==y.t). All ():Print("Complete") BreakX1=[3,4]y1=[3,3]x2=[1]y2=[1]k=-w[1]/w[2]d=-w[0]/w[2]xdata=np.linspace (0,10) P

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

. v_state) **2) **0.5 the Self . Errors.append (RMSE) theSelf.epoch + = 1 the Print("Epoch%s:rmse =%s; | | w| |:%6.1f; Sum Update:%f"%(Self.epoch, RMSE, Numpy.sum (Numpy.abs (self). W)), Total_change)) - return Self in the defLearning_curve (self): the plt.ion () About #plt.figure () the plt.show () theE =Numpy.array (self. Errors) thePlt.plot (Pandas.rolling_mean (E, 50) [50:]) + - defActivate (self, X): the ifX.SHAPE[1]! =Self . W.sha

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

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

Optimization algorithm is an important part of machine learning, BP Neural network is the foundation of deep Learning, BP neural network principle is very simple, almost can be understood as a logistic regression of a set way, in the previous blog post, I use r language to achieve several optimization algorithms, Based

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

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 BP

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

Reference Pengliang Teacher's video tutorial: Reprint please indicate the source and Pengliang teacher OriginalVideo Tutorials: Http://pan.baidu.com/s/1kVNe5EJ 1. About the nonlinear transformation equation (non-linear transformation function)The sigmoid function (the S-curve) is used as activation functions:1.1 Hyperbolic function (TANH) 1.2 logical functions (logistic function) 2. Implement a simple neural netw