import numpy as np

from sklearn.neural_network import MLPClassifier

from sklearn.linear_model import LogisticRegression

from sklearn.tree import DecisionTreeClassifier

from sklearn.model_selection import KFold

from sklearn.metrics import roc_auc_score

path = 'mnist.npz'

f = np.load(path)

X_train , y_train = f['x_train'], f['y_train']

X_test , y_test = f['x_test'], f['y_test']

X_train = X_train.astype('float32')

X_test = X_test.astype('float32')

X_train /= 255.

X_test /= 255.

X_train = X_train.reshape(60000,784)

X_test = X_test.reshape(10000,784)

roc_logistcis = 0

clf = LogisticRegression()

clf.fit(X_train,y_train)

y_pred = clf.predict(X_test)

sum=0.0

for i in range(10000):

    if(y_pred[i] == y_test[i]):

        sum = sum+1

print('Test set score: %f' % (sum/10000.))

# Test set score: 0.920200