SVM进行手写数字识别

使用了TensorFlow中的mnist数据集

from sklearn import svm
import numpy as np
from sklearn.metrics import classification_report
from tensorflow.keras.datasets.mnist import load_data

(x_train, y_train), (x_test, y_test) = load_data()
x_train = x_train.reshape(x_train.shape[0], -1)
x_test = x_test.reshape(x_test.shape[0], -1)

# 获取一个支持向量机模型
predictor = svm.SVC(gamma='scale', C=1.0, decision_function_shape='ovr', kernel='rbf')
# 把数据丢进去
predictor.fit(x_train, y_train)

y_pred = predictor.predict(x_test)
print(classification_report(y_test, y_pred))

准确率打印

              precision    recall  f1-score   support

           0       0.98      0.99      0.99       980
           1       0.99      0.99      0.99      1135
           2       0.98      0.97      0.98      1032
           3       0.97      0.99      0.98      1010
           4       0.98      0.98      0.98       982
           5       0.99      0.98      0.98       892
           6       0.99      0.99      0.99       958
           7       0.98      0.97      0.97      1028
           8       0.97      0.98      0.97       974
           9       0.97      0.96      0.97      1009

    accuracy                           0.98     10000
   macro avg       0.98      0.98      0.98     10000
weighted avg       0.98      0.98      0.98     10000