kaggle+mnist realize handwriting font recognition

Many current handwriting recognition codes are based on the existing mnist handwriting data set, and kaggle needs to use the data set presented on the website and generate the output of the test set for submission. Here, keras is selected to build a convolutional network for identification, which can directly generate the results of the test set. The final result recognition rate is about 97%.

# -*- coding: utf-8 -*-
"""
Created on Tue Jun 6 19:07:10 2017

@author: Administrator
"""

from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten 
from keras.layers import Convolution2D, MaxPooling2D 
from keras.utils import np_utils
import os
import pandas as pd
import numpy as np
from tensorflow.examples.tutorials.mnist import input_data
from keras import backend as K
import tensorflow as tf

#  The global variable  
batch_size = 100 
nb_classes = 10 
epochs = 20
# input image dimensions 
img_rows, img_cols = 28, 28 
# number of convolutional filters to use 
nb_filters = 32 
# size of pooling area for max pooling 
pool_size = (2, 2) 
# convolution kernel size 
kernel_size = (3, 3) 

inputfile='F:/data/kaggle/mnist/train.csv'
inputfile2= 'F:/data/kaggle/mnist/test.csv'
outputfile= 'F:/data/kaggle/mnist/test_label.csv'


pwd = os.getcwd()
os.chdir(os.path.dirname(inputfile)) 
train= pd.read_csv(os.path.basename(inputfile)) # Read data from the training data file 
os.chdir(pwd)

pwd = os.getcwd()
os.chdir(os.path.dirname(inputfile)) 
test= pd.read_csv(os.path.basename(inputfile2)) # Read data from the test data file 
os.chdir(pwd)

x_train=train.iloc[:,1:785] # Get characteristic data 
y_train=train['label']
y_train = np_utils.to_categorical(y_train, 10)

mnist=input_data.read_data_sets("MNIST_data/",one_hot=True) # Import data 
x_test=mnist.test.images
y_test=mnist.test.labels
#  According to backend Decide on a different format  
if K.image_dim_ordering() == 'th': 
 x_train=np.array(x_train)
 test=np.array(test)
 x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols) 
 x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols) 
 input_shape = (1, img_rows, img_cols) 
 test = test.reshape(test.shape[0], 1, img_rows, img_cols) 
else: 
 x_train=np.array(x_train)
 test=np.array(test)
 x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1) 
 X_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1) 
 test = test.reshape(test.shape[0], img_rows, img_cols, 1) 
 input_shape = (img_rows, img_cols, 1) 

x_train = x_train.astype('float32') 
x_test = X_test.astype('float32') 
test = test.astype('float32') 
x_train /= 255 
X_test /= 255
test/=255 
print('X_train shape:', x_train.shape) 
print(x_train.shape[0], 'train samples') 
print(x_test.shape[0], 'test samples') 
print(test.shape[0], 'testOuput samples') 

model=Sequential()#model initial
model.add(Convolution2D(nb_filters, (kernel_size[0], kernel_size[1]), 
      padding='same', 
      input_shape=input_shape)) #  Convolution layer 1 
model.add(Activation('relu')) # The activation layer  
model.add(Convolution2D(nb_filters, (kernel_size[0], kernel_size[1]))) # Convolution layer 2 
model.add(Activation('relu')) # The activation layer  
model.add(MaxPooling2D(pool_size=pool_size)) # Pooling layer  
model.add(Dropout(0.25)) # Random inactivation of neurons  
model.add(Flatten()) # Pulling into 1 D data  
model.add(Dense(128)) # The connection layer 1 
model.add(Activation('relu')) # The activation layer  
model.add(Dropout(0.5)) # Random inactivation  
model.add(Dense(nb_classes)) # The connection layer 2 
model.add(Activation('softmax')) #Softmax score  

# Compilation model  
model.compile(loss='categorical_crossentropy', 
    optimizer='adadelta', 
    metrics=['accuracy']) 
# Training model  

model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs,verbose=1) 
model.predict(x_test)
# Evaluation model  
score = model.evaluate(x_test, y_test, verbose=0) 
print('Test score:', score[0]) 
print('Test accuracy:', score[1]) 

y_test=model.predict(test)

sess=tf.InteractiveSession()
y_test=sess.run(tf.arg_max(y_test,1))
y_test=pd.DataFrame(y_test)
y_test.to_csv(outputfile)