.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/neural_networks/plot_mlp_alpha.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code. or to run this example in your browser via Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_neural_networks_plot_mlp_alpha.py: ================================================ 多层感知器中的正则化变化 ================================================ 在合成数据集上比较不同正则化参数 'alpha' 的值。图表显示，不同的 alpha 会产生不同的决策函数。 Alpha 是正则化项（也称为惩罚项）的参数，通过限制权重的大小来对抗过拟合。增加 alpha 可能通过鼓励较小的权重来修正高方差（过拟合的迹象），从而导致决策边界图的曲率较小。 同样，减少 alpha 可能通过鼓励较大的权重来修正高偏差（欠拟合的迹象），可能导致更复杂的决策边界。 .. GENERATED FROM PYTHON SOURCE LINES 12-140 .. image-sg:: /auto_examples/neural_networks/images/sphx_glr_plot_mlp_alpha_001.png :alt: alpha 0.10, alpha 0.32, alpha 1.00, alpha 3.16, alpha 10.00, alpha 0.10, alpha 0.32, alpha 1.00, alpha 3.16, alpha 10.00, alpha 0.10, alpha 0.32, alpha 1.00, alpha 3.16, alpha 10.00 :srcset: /auto_examples/neural_networks/images/sphx_glr_plot_mlp_alpha_001.png :class: sphx-glr-single-img .. code-block:: Python # 作者：scikit-learn 开发者 # SPDX-License-Identifier: BSD-3-Clause import numpy as np from matplotlib import pyplot as plt from matplotlib.colors import ListedColormap from sklearn.datasets import make_circles, make_classification, make_moons from sklearn.model_selection import train_test_split from sklearn.neural_network import MLPClassifier from sklearn.pipeline import make_pipeline from sklearn.preprocessing import StandardScaler h = 0.02 # step size in the mesh alphas = np.logspace(-1, 1, 5) classifiers = [] names = [] for alpha in alphas: classifiers.append( make_pipeline( StandardScaler(), MLPClassifier( solver="lbfgs", alpha=alpha, random_state=1, max_iter=2000, early_stopping=True, hidden_layer_sizes=[10, 10], ), ) ) names.append(f"alpha {alpha:.2f}") 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 # 遍历数据集 for X, y in datasets: # 划分为训练部分和测试部分 X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.4, random_state=42 ) x_min, x_max = X[:, 0].min() - 0.5, X[:, 0].max() + 0.5 y_min, y_max = X[:, 1].min() - 0.5, X[:, 1].max() + 0.5 xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h)) # 先绘制数据集 cm = plt.cm.RdBu cm_bright = ListedColormap(["#FF0000", "#0000FF"]) ax = plt.subplot(len(datasets), len(classifiers) + 1, i) # Plot the training points ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright) # 和测试点 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 # 遍历分类器 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颜色。 if hasattr(clf, "decision_function"): Z = clf.decision_function(np.column_stack([xx.ravel(), yy.ravel()])) else: Z = clf.predict_proba(np.column_stack([xx.ravel(), yy.ravel()]))[:, 1] # 将结果放入彩色图中 Z = Z.reshape(xx.shape) ax.contourf(xx, yy, Z, cmap=cm, alpha=0.8) # Plot also the training points ax.scatter( X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright, edgecolors="black", s=25, ) # 和测试点 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() - 0.3, yy.min() + 0.3, f"{score:.3f}".lstrip("0"), size=15, horizontalalignment="right", ) i += 1 figure.subplots_adjust(left=0.02, right=0.98) plt.show() .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 0.801 seconds) .. _sphx_glr_download_auto_examples_neural_networks_plot_mlp_alpha.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: binder-badge .. image:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/neural_networks/plot_mlp_alpha.ipynb :alt: Launch binder :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_mlp_alpha.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_mlp_alpha.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_mlp_alpha.zip ` .. include:: plot_mlp_alpha.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_