In [3]:

    

from sklearn.preprocessing import LabelEncoder
from sklearn.cross_validation import train_test_split
from keras.layers import Activation,Dense
from keras.utils.visualize_util import plot
from keras.utils import np_utils
from keras.models import Sequential

import pandas as pd


    

In [4]:

    

inputX = pd.read_csv('train_data.csv',header=None).values
inputY = pd.read_csv('train_label.csv',header=None).values.ravel()
le = LabelEncoder()
inputY = le.fit_transform(inputY)
inputY = np_utils.to_categorical(inputY)
inputY.shape


    

    
Out[4]:





(25000, 2)

In [5]:

    

inputX = inputX/255.0
trainX,testX,trainY,testY = train_test_split(inputX,inputY,random_state=4,test_size=0.20)


    

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