正文
mnist手写数字识别(决策树)
小程序:扫一扫查出行
【扫一扫了解最新限行尾号】
复制小程序
【扫一扫了解最新限行尾号】
复制小程序
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_Decision = 0
tree = DecisionTreeClassifier()
tree.fit(X_train,y_train)
y_pred = tree.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: 0.877100