《 大数据竞赛平台——Kaggle 入门篇》的补充

博客原文链接：
[ http://blog.csdn.net/u012162613/article/details/41929171 ]

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knn_benchmark.csv下载

github的连接
https://github.com/clytwynec/digit_recognition/blob/master/data/knn_benchmark.csv

将得到result.csv转换为提交文件格式：

Python代码

#!/usr/bin/env python2# -*- coding: utf-8 -*-"""Created on Tue Feb 21 21:35:48 2017@author: xyz"""from numpy import *import csvdef toInt(array):    array=mat(array)    m,n=shape(array)    newArray=zeros((m,n))    for i in xrange(m):        for j in xrange(n):                newArray[i,j]=int(float(label[i,j]))#string类型要先转换为float再转换为int    return newArraydef loadPredictResult():    l=[]    with open('sklearn_knn_Result.csv') as file:         lines=csv.reader(file)         for line in lines:             l.append(line)#28001*2    #l.remove(l[0])    label=array(l)    return toInt(label)  #  label 28000*1def saveResult(result,csvName):    with open(csvName,'wb') as myFile:            myWriter=csv.writer(myFile)        myWriter.writerow(['ImageId','Label'])        indexid=1        for i in result:            #tmp=[]            #tmp.append(i)            myWriter.writerow([indexid,int(i[0])])#每个i是一个array            indexid=indexid+1resultlabel=loadPredictResult()saveResult(resultlabel,'processed-result.csv')

利用Kears构建ANN模型：

参考博客《【Python与机器学习】：利用Keras进行多类分类》

[ http://www.cnblogs.com/arkenstone/p/5943489.html?utm_source=itdadao&utm_medium=referral ]

输入特征为784，利用one-hot encoder对0-9这10个分类标签编码，综上，输入神经元个数为784，输出神经元个数为10，隐藏节点数为输入节点数和输出节点数乘积的平方根，

hidnodes=784×10−−−−−−−√=88

神经网络结构如图：

Python代码

#!/usr/bin/python# -*- coding: utf-8 -*-"""Created on Tue Dec 16 21:59:00 2014@author: wepon@blog:http://blog.csdn.net/u012162613"""import numpy as npimport pandas as pdfrom keras.models import Sequentialfrom keras.layers import Dense, Dropoutfrom keras.wrappers.scikit_learn import KerasClassifierfrom keras.utils import np_utils#from sklearn.model_selection import train_test_split, KFold, cross_val_scorefrom sklearn.preprocessing import LabelEncoder# load train dataset X_train:data Y:labeldataframe = pd.read_csv("train.csv")dataset = dataframe.values#X = dataset[:, 1:].astype(float)X_train= dataset[:, 1:].astype(int)Y = dataset[:, 0].astype(int)# load test dataset X_test:datatestdataframe = pd.read_csv("test.csv")testdataset = testdataframe.values#X = dataset[:, 1:].astype(float)X_test= testdataset.astype(int)# load validation datasetvaliddataframe = pd.read_csv("knn_benchmark.csv")validdataset = validdataframe.values#X = dataset[:, 1:].astype(float)# encode class values as integers ???label encoders are 10 calatorgry?????????????encoder = LabelEncoder()def labelencode(Y):    encoded_Y = encoder.fit_transform(Y)# convert integers to dummy variables (one hot encoding)    dummy_y = np_utils.to_categorical(encoded_Y)    return dummy_y# define model structure 隐藏节点数为输入节点数和输出节点数乘积的开平方def baseline_model():    model = Sequential()    model.add(Dense(output_dim=88, input_dim=784, activation='relu'))    model.add(Dropout(0.2))    model.add(Dense(output_dim=10, input_dim=88, activation='softmax'))    # Compile model    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])    return modelestimator = KerasClassifier(build_fn=baseline_model, nb_epoch=40, batch_size=256)#指定每个batch的大小batch_size，指定训练轮数nb_epoch# splitting data into training set and test set. If random_state is set to an integer, the split datasets are fixed.#X_train, X_test, Y_train, Y_test = train_test_split(X, dummy_y, test_size=0.3, random_state=0)Y_train=labelencode(Y)estimator.fit(X_train, Y_train)# make predictionspred = estimator.predict(X_test)# inverse numeric variables to initial categorical labelsinit_lables = encoder.inverse_transform(pred)#print init_lables[0]init_lables=pd.DataFrame({'label':init_lables})#array convert to DataFramevaliddataframe['prelabel']=init_lables['label']validdataframe['accuracy']=validdataframe['prelabel']==validdataframe['Label']totalcount=validdataframe['accuracy'].count()print 'ANN accuarcy:',validdataframe['accuracy'].sum()/1.0/totalcount#write predict results to submision csvvaliddataframe.to_csv('submision.csv',columns=['ImageId','Label'],index=False)        

[在Kaggle上的排名]

比较不同模型的分类效果，看看CNN的效果如何：

如何用卷积神经网络CNN识别手写数字集？

http://blog.csdn.net/zdy0_2004/article/details/51945932

先给出CNN网络结构模型

代码

#!/usr/bin/python# -*- coding: utf-8 -*-"""Created on Tue Dec 16 21:59:00 2014@author: wepon@blog:http://blog.csdn.net/u012162613"""import numpy as npimport pandas as pdfrom keras.models import Sequentialfrom keras.layers import Dense, Dropout,Convolution2D,Reshape,AveragePooling2D, Flattenfrom keras.wrappers.scikit_learn import KerasClassifierfrom keras.optimizers import SGDfrom keras.utils import np_utils#from sklearn.model_selection import train_test_split, KFold, cross_val_scorefrom sklearn.preprocessing import LabelEncoder# load train dataset X_train:data Y:label#dataframe = pd.read_csv("train.csv", header=None)dataframe = pd.read_csv("train.csv")dataset = dataframe.values#X = dataset[:, 1:].astype(float)X_train= dataset[:, 1:].astype(int)Y = dataset[:, 0].astype(int)# load test dataset X_test:data#testdataframe = pd.read_csv("test.csv", header=None)testdataframe = pd.read_csv("test.csv")testdataset = testdataframe.values#X = dataset[:, 1:].astype(float)X_test= testdataset.astype(int)# load validation datasetvaliddataframe = pd.read_csv("knn_benchmark.csv")validdataset = validdataframe.values#X = dataset[:, 1:].astype(float)# encode class values as integersencoder = LabelEncoder()def labelencode(Y):    encoded_Y = encoder.fit_transform(Y)# convert integers to dummy variables (one hot encoding)    dummy_y = np_utils.to_categorical(encoded_Y)    return dummy_yY_train=labelencode(Y)# define model structure 784-88-10def baseline_model():    model = Sequential()    model.add(Dense(output_dim=85, input_dim=784, activation='relu'))    model.add(Dropout(0.2))    model.add(Dense(output_dim=10, input_dim=85, activation='softmax'))    # Compile model    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])    return model# define cnn modeldef cnn_model():    model = Sequential()    #Reshape层用来将输入shape转换为特定的shape    model.add(Reshape(target_shape=(1,28,28),input_shape=(784,)))    model.add(Convolution2D(nb_filter=32,nb_row=3,nb_col=3,dim_ordering='th',border_mode='same',bias=False,init='uniform'))        #model.add(Convolution2D(32, 3, 3,border_mode='same',input_shape=(1, 96, 96)))      model.add(AveragePooling2D(pool_size=(2,2),dim_ordering='th'))    model.add(Convolution2D(nb_filter=64,nb_row=3,nb_col=3,dim_ordering='th',border_mode='same',bias=False,init='uniform'))        model.add(AveragePooling2D(pool_size=(2,2),dim_ordering='th'))    # the model so far outputs 3D feature maps (height, width, features)    model.add(Flatten()) # this converts our 3D feature maps to 1D feature vectors    #Dense就是常用的全连接层    model.add(Dense(output_dim=1000,activation='sigmoid'))    model.add(Dense(output_dim=1000,activation='sigmoid'))    model.add(Dense(output_dim=10,activation='linear'))    sgd = SGD(lr='0.01',decay=1e-6,momentum=0.9, nesterov=True)    model.compile(loss='mean_squared_error', optimizer=sgd,metrics=['accuracy'])    return modelmodel_name='cnn'estimator = KerasClassifier(build_fn=cnn_model, nb_epoch=40, batch_size=256)# splitting data into training set and test set. If random_state is set to an integer, the split datasets are fixed.#X_train, X_test, Y_train, Y_test = train_test_split(X, dummy_y, test_size=0.3, random_state=0)estimator.fit(X_train, Y_train)# make predictionspred = estimator.predict(X_test)# inverse numeric variables to initial categorical labelsinit_lables = encoder.inverse_transform(pred)#print init_lables[0]#init_lables=pd.DataFrame({'label':init_lables[1:]})init_lables=pd.DataFrame({'label':init_lables})validdataframe['prelabel']=init_lables['label']validdataframe['accuracy']=validdataframe['prelabel']==validdataframe['Label']totalcount=validdataframe['accuracy'].count()print '%s accuarcy:'%model_name,validdataframe['accuracy'].sum()/1.0/totalcount#write to submision csvvaliddataframe.to_csv('submision_%s.csv'%model_name,columns=['ImageId','Label'],index=False)