如何在MLPClassifier中使用sklean绘制训练和测试数据的准确性和损失曲线？

import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.neural_network import MLPClassifier

X, y = make_classification(n_samples=1000, n_features=20, random_state=42)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

model = MLPClassifier(hidden_layer_sizes=(100, 100), max_iter=1000, random_state=42)
model.fit(X_train, y_train)

train_accuracy = model.score(X_train, y_train)
test_accuracy = model.score(X_test, y_test)
loss_values = model.loss_curve_

plt.figure(figsize=(10, 6))
plt.plot(model.validation_scores_, label='Training Accuracy')
plt.plot(model.validation_scores_, label='Testing Accuracy')
plt.xlabel('Iterations')
plt.ylabel('Accuracy')
plt.title('Training and Testing Accuracy')
plt.legend()
plt.show()

plt.figure(figsize=(10, 6))
plt.plot(loss_values)
plt.xlabel('Iterations')
plt.ylabel('Loss')
plt.title('Training Loss')
plt.show()