Implementation of BP Neural network recognition mnist data set by Python

Title: "Python realizes BP neural network recognition mnist data Set"
date:2018-06-18t14:01:49+08:00
Tags: [""]
Categories: ["Python"]

Objective

The training set read in the. MAT format when testing the correct rate with a PNG-formatted picture

Code

#!/usr/bin/env Python3# Coding=utf-8ImportMathImportSysImportOsImportNumPy asNp fromPILImportImageImportScipy.io asSiodefSigmoid (x):returnNp.array (List(Map(LambdaI1 /(1 +Math.exp (-i)) (x)))defGet_train_pattern ():# Return the features and tags of the training set    # Current_dir = OS.GETCWD ()Current_dir= "/home/lxp/f/developing_folder/intelligence_system/bpneuralnet/"Train=Sio.loadmat (Current_dir+ "Mnist_train.mat")["Mnist_train"] Train_label=Sio.loadmat (Current_dir+ "Mnist_train_labels.mat")["Mnist_train_labels"] Train=Np.where (Train>  the,1,0)# Binary Value    returnTrain, Train_labeldefGet_test_pattern ():# Return to test set    # Base_url = OS.GETCWD () + "/test/"Base_url= "/home/lxp/f/developing_folder/intelligence_system/bpneuralnet/mnist_test/"Test_img_pattern=[] forIinch Range(Ten): Img_url=Os.listdir (Base_url+ Str(i)) t=[] forUrlinchImg_url:img=Image.Open(Base_url+ Str(i)+ "/" +URL) img=Img.convert (' 1 ')# Binary ValueImg_array=Np.asarray (IMG,' I ')# Convert to int arrayImg_vector=Img_array.reshape (img_array.shape[0]*img_array.shape[1])# Expand into a one-dimensional arrayT.append (Img_vector) test_img_pattern.append (t)returnTest_img_patternclassBpnetwork:# Neural Network class    def __init__( Self, In_count, Hiden_count, Out_count, In_rate, hiden_rate):""":p Aram In_count: Number of input layers:p Aram Hiden_count: Number of hidden layers:p Aram Out_count: Number of output layers:p Aram In_rate: Input layer Learning rate:p Aram Hiden_rate: Hidden layer Learning rate        """        # Number of nodes in each layer         Self. in_count=In_count Self. hiden_count=Hiden_count Self. out_count=Out_count# Random initialization of weights for input layer to hidden layer lines         Self. W1= 0.2 * \Np.random.random (( Self. In_count, Self. Hiden_count))- 0.1        # Random initialization of weights for hidden layer to output layer lines         Self. W2= 0.2 * \Np.random.random (( Self. Hiden_count, Self. Out_count))- 0.1        # hidden layer bias vector         Self. hiden_offset=Np.zeros ( Self. Hiden_count)# output layer bias vector         Self. out_offset=Np.zeros ( Self. Out_count)# Input Layer Learning rate         Self. in_rate=In_rate# Hidden Layer Learning rate         Self. hiden_rate=Hiden_ratedefTrain Self, Train_img_pattern, Train_label):if  Self. in_count!= Len(train_img_pattern[0]): Sys.exit ("The input layer dimension is not equal to the number of sample dimensions")# for NUM in range:        # for NUM in range:         forIinch Range(Len(Train_img_pattern)):ifI%  the == 0:Print(i)# generate target vectorsTarget=[0]* Tentarget[train_label[i][0]]= 1            # for T in range (len (Train_img_pattern[num])):            # Forward Propagation            # hidden layer value equals input layer *w1+ hidden layer biasHiden_value=Np.dot (Train_img_pattern[i], Self. W1)+  Self. Hiden_offset Hiden_value=Sigmoid (Hiden_value)# Calculate output layer outputOut_value=Np.dot (Hiden_value, Self. W2)+  Self. Out_offset Out_value=Sigmoid (Out_value)# Reverse UpdateError=Target-Out_value# Calculate output layer ErrorOut_error=Out_value*(1 -Out_value)*Error# Calculating hidden layer errorsHiden_error=Hiden_value* \(1 -Hiden_value)*Np.dot ( Self. W2, Out_error)# Update W2,W2 is a J-row K-column matrix that stores the weights of the hidden layer to the output layer             forKinch Range( Self. Out_count):# Update the value of W2 k column, connecting all nodes in the hidden layer to the side of the K node of the output layer                # Hidden Layer Learning rate x input layer error x hidden layer output value                 Self. w2[:, K]+=  Self. hiden_rate*OUT_ERROR[K]*Hiden_value# Update W1             forJinch Range( Self. Hiden_count): Self. w1[:, J]+=  Self. in_rate* \HIDEN_ERROR[J]*Train_img_pattern[i]# update offset vector             Self. out_offset+=  Self. hiden_rate*Out_error Self. hiden_offset+=  Self. in_rate*Hiden_errordefTest Self, Test_img_pattern):"""testing the correct rate of neural networks:p Aram Test_img_pattern[num][t] represents the T-picture of digital num: return:        """Right=Np.zeros (Ten) test_sum= 0         forNuminch Range(Ten):# 10 Digits            # Print ("Recognizing", num)Num_count= Len(Test_img_pattern[num]) test_sum+=Num_count forTinch Range(Num_count):# T-picture of number numHiden_value=Np.dot (Test_img_pattern[num][t], Self. W1)+  Self. Hiden_offset Hiden_value=Sigmoid (Hiden_value) out_value=Np.dot (Hiden_value, Self. W2)+  Self. Out_offset Out_value=Sigmoid (Out_value)# Print (out_value)                ifNp.argmax (Out_value)==Num:# Correct identificationRight[num]+= 1            Print("Digital%dthe correct rate of recognition%f" %(Num, Right[num]/Num_count))# Average recognition rate        Print("Average recognition rate is:",sum(right)/Test_sum)"""def test1:    """defRun ():# Read in training setTrain, Train_label=Get_train_pattern ()# Read in test pictureTest_pattern=Get_test_pattern ()# Neural Network configuration ParametersIn_count=  - *  -Hiden_count= 6Out_count= TenIn_rate= 0.1Hiden_rate= 0.1Bpnn=Bpnetwork (In_count, Hiden_count, Out_count, In_rate, hiden_rate) Bpnn.train (Train, Train_label) Bpnn.test (Test_patte Tno# Single-sheet test    # Identify a single picture and return the recognition result    """While True:img_name = input ("Enter a picture to recognize \ n")base_url = "/home/lxp/f/developing_folder/intelligence_system/bpneuralnet/"Img_url = base_url + img_nameimg = image.open (img_url)img = Img.convert (' 1 ') # binaryImg_array = Np.asarray (img, ' I ') # converted to an int array# Get the feature vector of the pictureImg_v = Img_array.reshape (img_array.shape[0] * img_array.shape[1]) # expands into a one-dimensional arrayBpnn.test1 (Img_v)    """if __name__ == "__main__": Run ()# train, Train_label = Get_train_pattern ()    # Print (train_label[5][0])# test = Get_test_pattern ()

Data Set Download:
Http://ot0ucj3at.bkt.clouddn.com/o_1cg8o6k59muv3fg1kel9qg1i4ca.zip

Implementation of BP Neural network recognition mnist data set by Python