以手写数字识别为例
"""
# @Time : 2020/9/7
# @Author : Jimou Chen
"""
from sklearn.neural_network import MLPClassifier
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_digits
from sklearn.preprocessing import StandardScaler # 减去平均值再除以方差
from sklearn.metrics import classification_report, confusion_matrix
if __name__ == '__main__':
digits_data = load_digits()
x_data = digits_data.data
y_data = digits_data.target
# 对数据进行标准化
sc = StandardScaler()
x_data = sc.fit_transform(x_data)
# 切分数据
x_train, x_test, y_train, y_test = train_test_split(x_data, y_data)
# 建模
model = MLPClassifier(hidden_layer_sizes=(100, 50), max_iter=100)
model.fit(x_train, y_train)
# 预测
prediction = model.predict(x_test)
# 评估
print(classification_report(prediction, y_test))
print(confusion_matrix(y_test, prediction))
precision recall f1-score support
0 1.00 1.00 1.00 44
1 1.00 0.96 0.98 50
2 0.98 0.98 0.98 48
3 0.97 0.97 0.97 35
4 0.98 0.98 0.98 42
5 0.96 1.00 0.98 48
6 1.00 1.00 1.00 43
7 1.00 0.98 0.99 45
8 0.96 1.00 0.98 49
9 0.98 0.96 0.97 46
accuracy 0.98 450
macro avg 0.98 0.98 0.98 450
weighted avg 0.98 0.98 0.98 450
[[44 0 0 0 0 0 0 0 0 0]
[ 0 48 0 0 0 0 0 0 0 0]
[ 0 1 47 0 0 0 0 0 0 0]
[ 0 0 0 34 0 0 0 1 0 0]
[ 0 0 0 0 41 0 0 0 0 1]
[ 0 0 0 0 1 48 0 0 0 1]
[ 0 0 0 0 0 0 43 0 0 0]
[ 0 0 0 0 0 0 0 44 0 0]
[ 0 1 1 0 0 0 0 0 49 0]
[ 0 0 0 1 0 0 0 0 0 44]]
Process finished with exit code 0
confusion_matrix理解
[[44 0 0 0 0 0 0 0 0 0]
[ 0 48 0 0 0 0 0 0 0 0]
[ 0 1 47 0 0 0 0 0 0 0]
[ 0 0 0 34 0 0 0 1 0 0]
[ 0 0 0 0 41 0 0 0 0 1]
[ 0 0 0 0 1 48 0 0 0 1]
[ 0 0 0 0 0 0 43 0 0 0]
[ 0 0 0 0 0 0 0 44 0 0]
[ 0 1 1 0 0 0 0 0 49 0]
[ 0 0 0 1 0 0 0 0 0 44]]
- 对角线越大越好， 最理想的情况是只有对角线有值
- 其他地方出现值代表该分类被识别成其他的类别
- 比如上面第0、1行很完美，说明都识别正确了
- 但是第2行对角线有个1，说明有一个图片本来是2的，却识别成了1
- 其他同理
