标签：img 神经网络 tac 机器 range 方便 .data def python
import numpy as np from matplotlib import pyplot as plt from sklearn import neighbors, datasets from matplotlib.colors import ListedColormap from sklearn.neural_network import MLPClassifier ## 加载数据集 np.random.seed(0) # 使用 scikit-learn 自带的 iris 数据集 iris=datasets.load_iris() # 使用前两个特征，方便绘图 X=iris.data[:,0:2] # 标记值 Y=iris.target data=np.hstack((X,Y.reshape(Y.size,1))) # 混洗数据。因为默认的iris 数据集：前50个数据是类别0，中间50个数据是类别1，末尾50个数据是类别2.混洗将打乱这个顺序 np.random.shuffle(data) X=data[:,:-1] Y=data[:,-1] train_x=X[:-30] train_y=Y[:-30] # 最后30个样本作为测试集 test_x=X[-30:] test_y=Y[-30:] def plot_classifier_predict_meshgrid(ax,clf,x_min,x_max,y_min,y_max): ‘‘‘ 绘制 MLPClassifier 的分类结果 :param ax: Axes 实例，用于绘图 :param clf: MLPClassifier 实例 :param x_min: 第一维特征的最小值 :param x_max: 第一维特征的最大值 :param y_min: 第二维特征的最小值 :param y_max: 第二维特征的最大值 :return: None ‘‘‘ plot_step = 0.02 # 步长 xx, yy = np.meshgrid(np.arange(x_min, x_max, plot_step),np.arange(y_min, y_max, plot_step)) Z = clf.predict(np.c_[xx.ravel(), yy.ravel()]) Z = Z.reshape(xx.shape) # 绘图 ax.contourf(xx, yy, Z, cmap=plt.cm.Paired) def plot_samples(ax,x,y): ‘‘‘ 绘制二维数据集 :param ax: Axes 实例，用于绘图 :param x: 第一维特征 :param y: 第二维特征 :return: None ‘‘‘ n_classes = 3 # 颜色数组。每个类别的样本使用一种颜色 plot_colors = "bry" for i, color in zip(range(n_classes), plot_colors): idx = np.where(y == i) # 绘图 ax.scatter(x[idx, 0], x[idx, 1], c=color,label=iris.target_names[i], cmap=plt.cm.Paired) def mlpclassifier_iris(): ‘‘‘ 使用 MLPClassifier 预测调整后的 iris 数据集 ‘‘‘ fig=plt.figure() ax=fig.add_subplot(1,1,1) classifier=MLPClassifier(activation=‘logistic‘,max_iter=10000,hidden_layer_sizes=(30,)) classifier.fit(train_x,train_y) train_score=classifier.score(train_x,train_y) test_score=classifier.score(test_x,test_y) x_min, x_max = train_x[:, 0].min() - 1, train_x[:, 0].max() + 2 y_min, y_max = train_x[:, 1].min() - 1, train_x[:, 1].max() + 2 plot_classifier_predict_meshgrid(ax,classifier,x_min,x_max,y_min,y_max) plot_samples(ax,train_x,train_y) ax.legend(loc=‘best‘) ax.set_xlabel(iris.feature_names[0]) ax.set_ylabel(iris.feature_names[1]) ax.set_title("train score:%f;test score:%f"%(train_score,test_score)) plt.show() mlpclassifier_iris()
def mlpclassifier_iris_hidden_layer_sizes(): ‘‘‘ 使用 MLPClassifier 预测调整后的 iris 数据集。考察不同的 hidden_layer_sizes 的影响 :return: None ‘‘‘ fig=plt.figure() # 候选的 hidden_layer_sizes 参数值组成的数组 hidden_layer_sizes=[(10,),(30,),(100,),(5,5),(10,10),(30,30)] for itx,size in enumerate(hidden_layer_sizes): ax=fig.add_subplot(2,3,itx+1) classifier=MLPClassifier(activation=‘logistic‘,max_iter=10000,hidden_layer_sizes=size) classifier.fit(train_x,train_y) train_score=classifier.score(train_x,train_y) test_score=classifier.score(test_x,test_y) x_min, x_max = train_x[:, 0].min() - 1, train_x[:, 0].max() + 2 y_min, y_max = train_x[:, 1].min() - 1, train_x[:, 1].max() + 2 plot_classifier_predict_meshgrid(ax,classifier,x_min,x_max,y_min,y_max) plot_samples(ax,train_x,train_y) ax.legend(loc=‘best‘) ax.set_xlabel(iris.feature_names[0]) ax.set_ylabel(iris.feature_names[1]) ax.set_title("layer_size:%s;train score:%f;test score:%f"%(size,train_score,test_score)) plt.show() mlpclassifier_iris_hidden_layer_sizes()
def mlpclassifier_iris_ativations(): ‘‘‘ 使用 MLPClassifier 预测调整后的 iris 数据集。考察不同的 activation 的影响 ‘‘‘ fig=plt.figure() # 候选的激活函数字符串组成的列表 ativations=["logistic","tanh","relu"] for itx,act in enumerate(ativations): ax=fig.add_subplot(1,3,itx+1) classifier=MLPClassifier(activation=act,max_iter=10000,hidden_layer_sizes=(30,)) classifier.fit(train_x,train_y) train_score=classifier.score(train_x,train_y) test_score=classifier.score(test_x,test_y) x_min, x_max = train_x[:, 0].min() - 1, train_x[:, 0].max() + 2 y_min, y_max = train_x[:, 1].min() - 1, train_x[:, 1].max() + 2 plot_classifier_predict_meshgrid(ax,classifier,x_min,x_max,y_min,y_max) plot_samples(ax,train_x,train_y) ax.legend(loc=‘best‘) ax.set_xlabel(iris.feature_names[0]) ax.set_ylabel(iris.feature_names[1]) ax.set_title("activation:%s;train score:%f;test score:%f"%(act,train_score,test_score)) plt.show() mlpclassifier_iris_ativations()
def mlpclassifier_iris_algorithms(): ‘‘‘ 使用 MLPClassifier 预测调整后的 iris 数据集。考察不同的 algorithm 的影响 :return: None ‘‘‘ fig=plt.figure() algorithms=["lbfgs","sgd","adam"] # 候选的算法字符串组成的列表 for itx,algo in enumerate(algorithms): ax=fig.add_subplot(1,3,itx+1) classifier=MLPClassifier(activation="tanh",max_iter=10000,hidden_layer_sizes=(30,),solver=algo) classifier.fit(train_x,train_y) train_score=classifier.score(train_x,train_y) test_score=classifier.score(test_x,test_y) x_min, x_max = train_x[:, 0].min() - 1, train_x[:, 0].max() + 2 y_min, y_max = train_x[:, 1].min() - 1, train_x[:, 1].max() + 2 plot_classifier_predict_meshgrid(ax,classifier,x_min,x_max,y_min,y_max) plot_samples(ax,train_x,train_y) ax.legend(loc=‘best‘) ax.set_xlabel(iris.feature_names[0]) ax.set_ylabel(iris.feature_names[1]) ax.set_title("algorithm:%s;train score:%f;test score:%f"%(algo,train_score,test_score)) plt.show() mlpclassifier_iris_algorithms()
def mlpclassifier_iris_eta(): ‘‘‘ 使用 MLPClassifier 预测调整后的 iris 数据集。考察不同的学习率的影响 ‘‘‘ fig=plt.figure() etas=[0.1,0.01,0.001,0.0001] # 候选的学习率值组成的列表 for itx,eta in enumerate(etas): ax=fig.add_subplot(2,2,itx+1) classifier=MLPClassifier(activation="tanh",max_iter=1000000, hidden_layer_sizes=(30,),solver=‘sgd‘,learning_rate_init=eta) classifier.fit(train_x,train_y) iter_num=classifier.n_iter_ train_score=classifier.score(train_x,train_y) test_score=classifier.score(test_x,test_y) x_min, x_max = train_x[:, 0].min() - 1, train_x[:, 0].max() + 2 y_min, y_max = train_x[:, 1].min() - 1, train_x[:, 1].max() + 2 plot_classifier_predict_meshgrid(ax,classifier,x_min,x_max,y_min,y_max) plot_samples(ax,train_x,train_y) ax.legend(loc=‘best‘) ax.set_xlabel(iris.feature_names[0]) ax.set_ylabel(iris.feature_names[1]) ax.set_title("eta:%f;train score:%f;test score:%f;iter_num:%d"%(eta,train_score,test_score,iter_num)) plt.show() mlpclassifier_iris_eta()
吴裕雄 python 机器学习——人工神经网络感知机学习算法的应用
标签：img 神经网络 tac 机器 range 方便 .data def python
原文地址：https://www.cnblogs.com/tszr/p/10799600.html
