import numpy as npfrom sklearn.neural_network import MLPClassifierfrom sklearn.linear_model import LogisticRegressionfrom sklearn.tree import DecisionTreeClassifierfrom sklearn.model_selection import KFoldfrom sklearn.metrics import roc_auc_scorepath = '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 = 0clf = LogisticRegression()clf.fit(X_train,y_train)y_pred = clf.predict(X_test)sum=0.0for 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