MLPClassifier_机器学习_模型训练_MLPClassifier_源码

print(__doc__) import numpy as np from matplotlib import pyplot as plt from matplotlib.colors import ListedColormap from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.datasets import make_moons, make_circles, make_classification from sklearn.neural_network import MLPClassifier h = .02 # 网格中的步长 alphas = np.logspace(-5, 3, 5) names = ['alpha ' + str(i) for i in alphas] classifiers = [] for i in alphas: classifiers.append(MLPClassifier(solver='lbfgs', alpha=i, random_state=1, hidden_layer_sizes=[100, 100])) X, y = make_classification(n_features=2, n_redundant=0, n_informative=2, random_state=0, n_clusters_per_class=1) rng = np.random.RandomState(2) X += 2 * rng.uniform(size=X.shape) linearly_separable = (X, y) datasets = [make_moons(noise=0.3, random_state=0), make_circles(noise=0.2, factor=0.5, random_state=1), linearly_separable] figure = plt.figure(figsize=(17, 9)) i = 1 # 迭代数据集 for X, y in datasets: # 预处理数据集，分成训练和测试部分 X = StandardScaler().fit_transform(X) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.4) x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5 y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5 xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h)) # 只需先绘制数据集 cm = plt.cm.RdBu cm_bright = ListedColormap(['#FF0000', '#0000FF']) ax = plt.subplot(len(datasets), len(classifiers) + 1, i) # 回执训练点 ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright) # 并测试点 ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright, alpha=0.6) ax.set_xlim(xx.min(), xx.max()) ax.set_ylim(yy.min(), yy.max()) ax.set_xticks(()) ax.set_yticks(()) i += 1 # 迭代分类器 for name, clf in zip(names, classifiers): ax = plt.subplot(len(datasets), len(classifiers) + 1, i) clf.fit(X_train, y_train) score = clf.score(X_test, y_test) # 绘制决策边界。为此，我们将为网格[x_min, x_max]x[y_min, y_max]中的每个点 # 分配一个角色 if hasattr(clf, "decision_function"): Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()]) else: Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1] # 将结果放入一个颜色图 Z = Z.reshape(xx.shape) ax.contourf(xx, yy, Z, cmap=cm, alpha=.8) # 绘制训练点 ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright, edgecolors='black', s=25) # 并测试点 ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright, alpha=0.6, edgecolors='black', s=25) ax.set_xlim(xx.min(), xx.max()) ax.set_ylim(yy.min(), yy.max()) ax.set_xticks(()) ax.set_yticks(()) ax.set_title(name) ax.text(xx.max() - .3, yy.min() + .3, ('%.2f' % score).lstrip('0'), size=15, horizontalalignment='right') i += 1 figure.subplots_adjust(left=.02, right=.98) plt.show()