import pandas as pd
import numpy as np # linear algebra

# The competition datafiles are in the directory ../input
# Read competition data files:
train = pd.read_csv("../input/train.csv")
test  = pd.read_csv("../input/test.csv")

# Write to the log:
print("Training set has {0[0]} rows and {0[1]} columns".format(train.shape))
print("Test set has {0[0]} rows and {0[1]} columns".format(test.shape))
# Any files you write to the current directory get shown as outputs

from subprocess import check_output
print(check_output(["ls", "../input"]).decode("utf8"))

# Any results you write to the current directory are saved as output.
import matplotlib.pyplot as plt, matplotlib.image as mpimg
from sklearn.model_selection import train_test_split
from sklearn import svm
#%matplotlib inline

labeled_images = pd.read_csv('../input/train.csv')
images = labeled_images.iloc[:,1:]
labels = labeled_images.iloc[:,:1]
train_images, test_images,train_labels, test_labels = train_test_split(images, labels, train_size=0.8, random_state=0)