1 from sklearn.neural_network import MLPClassifier 2 from sklearn.datasets import load_wine 3 from sklearn.model_selection import train_test_split 4 wine=load_wine() 5 X=wine.data[:,:2] 6 y=wine.target 7 X_train,X_test,y_train,y_test=train_test_split(X,y,random_state=8) 8 mlp=MLPClassifier(solver='lbfgs',max_iter=1000) 9 mlp.fit(X_train,y_train) 10 print("the score of this model:{}".format(mlp.score(X_test,y_test)))
1 import matplotlib.pyplot as plt 2 from matplotlib.colors import ListedColormap 3 import numpy as np 4 cmap_light=ListedColormap(['#FFAAAA','#AAFFAA','#AAAAFF']) 5 cmap_bold=ListedColormap(['#FF0000','#00FF00','0000FF']) 6 x_min=X_train[:,0].min()-1 7 x_max=X_train[:,0].max()+1 8 y_min=X_train[:,1].min()-1 9 y_max=X_train[:,1].max()+1 10 xx,yy=np.meshgrid(np.arange(x_min,x_max,.02), 11 np.arange(y_min,y_max,.02)) 12 z=mlp.predict(np.c_[xx.ravel(),yy.ravel()]) 13 z=z.reshape(xx.shape) 14 plt.figure() 15 plt.pcolormesh(xx,yy,z,cmap=cmap_light) 16 plt.scatter(X[:,0],X[:,1],c=y,edgecolors='k',s=60) 17 plt.xlim(xx.min(),xx.max()) 18 plt.ylim(yy.min(),yy.max()) 19 plt.title("MLPClassifier:solver=lbfgs") 20 plt.show()
1 mlp=MLPClassifier(solver='lbfgs',max_iter=10000,hidden_layer_sizes=[10,10],activation='tanh',alpha=1) 2 mlp.fit(X_train,y_train) 3 print(mlp.score(X_test,y_test))
1 z=mlp.predict(np.c_[xx.ravel(),yy.ravel()]) 2 z=z.reshape(xx.shape) 3 plt.figure() 4 plt.pcolormesh(xx,yy,z,cmap=cmap_light) 5 plt.scatter(X[:,0],X[:,1],c=y,edgecolors='k',s=60) 6 plt.xlim(xx.min(),xx.max()) 7 plt.ylim(yy.min(),yy.max()) 8 plt.title("MLPClassifier:solver=lbfgs") 9 plt.show()