kaggle入门篇一【手写数字识别】

# -*- coding: utf-8 -*-
"""
Created on Fri Apr 21 11:10:17 2017

@author: Administrator
"""
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers import Dropout,Dense,Activation,Convolution2D, MaxPooling2D, Flatten
import numpy as np
import pandas as pd

test=pd.read_csv(r"F:\kaggle_file\shouxieshuzi\test.csv")
test=np.array(test)

data=pd.read_csv(r"F:\kaggle_file\shouxieshuzi\train.csv")
train_Labels=data['label']
del(data['label'])
train_Data=data/255
train_Labels = np_utils.to_categorical(train_Labels, num_classes=10)
data=np.array(train_Data)
train_Data=np.array(train_Data).reshape(42000,1,28,28)
test=test.reshape(28000,1,28,28)
###预处理

def Cmodel():
#create model
model = Sequential()
model.add(Convolution2D(nb_filter=32,nb_row=5,nb_col=5,border_mode='same',dim_ordering='th',input_shape=(1,28,28),activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2),strides=(2, 2),border_mode='same'))

model.add(Convolution2D(64,5,5,activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2),border_mode='same'))
model.add(Dropout(0.2))

model.add(Flatten())
model.add(Dense(1024,activation='relu'))

model.add(Dense(50,activation='relu'))

model.add(Dense(10,activation='softmax'))
#Compile model
model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy'])
return model

model = Cmodel()
model.fit(train_Data,train_Labels,nb_epoch=10,batch_size=100)

######新模型
predict=model.predict(test)
predict=predict.argmax(1)
predict=pd.DataFrame(predict)
predict.to_csv('result.csv')