solver对比实验

from sklearn.datasets import load_iris
from sklearn.neural_network import MLPClassifier
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import accuracy_score
iris = load_iris()
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.2, random_state=42)
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
for solver in ['adam', 'lbfgs', 'sgd']:
    clf = MLPClassifier(hidden_layer_sizes=(10,), max_iter=500, solver=solver, random_state=42)
    clf.fit(X_train, y_train)
    print(f'{solver}: 准确率={accuracy_score(y_test, clf.predict(X_test)):.4f}')