بدء التعلم الآلي بايثون سريع (33)

class sklearn.neural_network.MLPRegressor(hidden_layer_sizes=100, activation='relu', *, solver='adam', alpha=0.0001, batch_size='auto', learning_rate='constant', learning_rate_init=0.001, power_t=0.5, max_iter=200, shuffle=True, random_state=None, tol=0.0001, verbose=False, warm_start=False, momentum=0.9, nesterovs_momentum=True, early_stopping=False, validation_fraction=0.1, beta_1=0.9, beta_2=0.999, epsilon=1e-08, n_iter_no_change=10, max_fun=15000)

def MLPRegressor_make_regression():       warnings.filterwarnings("ignore")       myutil = util()       X,y = datasets.make_regression(n_samples=100,n_features=1,n_informative=2,noise=50,random_state=8)       X_train,X_test,y_train,y_test = train_test_split(X, y, random_state=8,test_size=0.3)       clf = MLPRegressor(max_iter=20000).fit(X,y)       title = "MLPRegressor make_regression数据集(有噪音)"       myutil.draw_line(X[:,0],y,clf,title)

def My_MLPRegressor(solver,hidden_layer_sizes,activation,level,alpha,mydata,title):    warnings.filterwarnings("ignore")    myutil = util()    X,y = mydata.data,mydata.target    X_train,X_test,y_train,y_test = train_test_split(X, y, random_state=8,test_size=0.3)   clf = MLPRegressor(solver=solver,hidden_layer_sizes=hidden_layer_sizes,activation=activation,alpha=alpha,max_iter=10000).fit(X_train,y_train)   mytitle = "MLPRegressor("+title+"):solver:"+solver+",node:"+str(hidden_layer_sizes)+",activation:"+activation+",level="+str(level)+",alpha="+str(alpha)   myutil.print_scores(clf,X_train,y_train,X_test,y_test,mytitle)def MLPRegressor_base():    mydatas = [datasets.load_diabetes(), datasets.load_boston()]    titles = ["糖尿病数据","波士顿房价数据"]    for (mydata,title) in zip(mydatas, titles):        ten = [10]        hundred = [100]        two_ten = [10,10]        Parameters = [['lbfgs',hundred,'relu',1,0.0001], ['lbfgs',ten,'relu',1,0.0001], ['lbfgs',two_ten,'relu',2,0.0001],['lbfgs',two_ten,'tanh',2,0.0001],['lbfgs',two_ten,'tanh',2,1]]       for Parameter in Parameters:              My_MLPRegressor(Parameter[0],Parameter[1],Parameter[2],Parameter[3],Parameter[4],mydata,title)

MLPRegressor(糖尿病数据):solver:lbfgs,node:[100],activation:relu,level=1,alpha=0.0001:68.83%MLPRegressor(糖尿病数据):solver:lbfgs,node:[100],activation:relu,level=1,alpha=0.0001:28.78%MLPRegressor(糖尿病数据):solver:lbfgs,node:[10],activation:relu,level=1,alpha=0.0001:53.50%MLPRegressor(糖尿病数据):solver:lbfgs,node:[10],activation:relu,level=1,alpha=0.0001:45.41%MLPRegressor(糖尿病数据):solver:lbfgs,node:[10, 10],activation:relu,level=2,alpha=0.0001:68.39%MLPRegressor(糖尿病数据):solver:lbfgs,node:[10, 10],activation:relu,level=2,alpha=0.0001:31.62%MLPRegressor(糖尿病数据):solver:lbfgs,node:[10, 10],activation:tanh,level=2,alpha=0.0001:64.18%MLPRegressor(糖尿病数据):solver:lbfgs,node:[10, 10],activation:tanh,level=2,alpha=0.0001:31.46%MLPRegressor(糖尿病数据):solver:lbfgs,node:[10, 10],activation:tanh,level=2,alpha=1:-0.00%MLPRegressor(糖尿病数据):solver:lbfgs,node:[10, 10],activation:tanh,level=2,alpha=1:-0.01%MLPRegressor(波士顿房价数据):solver:lbfgs,node:[100],activation:relu,level=1,alpha=0.0001:90.04%MLPRegressor(波士顿房价数据):solver:lbfgs,node:[100],activation:relu,level=1,alpha=0.0001:63.90%MLPRegressor(波士顿房价数据):solver:lbfgs,node:[10],activation:relu,level=1,alpha=0.0001:85.23%MLPRegressor(波士顿房价数据):solver:lbfgs,node:[10],activation:relu,level=1,alpha=0.0001:68.49%MLPRegressor(波士顿房价数据):solver:lbfgs,node:[10, 10],activation:relu,level=2,alpha=0.0001:90.12%MLPRegressor(波士顿房价数据):solver:lbfgs,node:[10, 10],activation:relu,level=2,alpha=0.0001:63.48%MLPRegressor(波士顿房价数据):solver:lbfgs,node:[10, 10],activation:tanh,level=2,alpha=0.0001:18.19%MLPRegressor(波士顿房价数据):solver:lbfgs,node:[10, 10],activation:tanh,level=2,alpha=0.0001:18.25%MLPRegressor(波士顿房价数据):solver:lbfgs,node:[10, 10],activation:tanh,level=2,alpha=1:85.37%MLPRegressor(波士顿房价数据):solver:lbfgs,node:[10, 10],activation:tanh,level=2,alpha=1:

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