mnist手写数字识别(SVM)

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
from sklearn.svm import LinearSVC


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)

lsvc = LinearSVC()
lsvc.fit(X_train,y_train)
y_pred = lsvc.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: 0.917900