关于MLP的一个问题-这条线是什么意思？

Question

我对NN很陌生。我正在尝试调整我的MLP REGRESSOR模型。我不明白这一行是什么意思：( 100，)，(50,100，)，(50,75,100 )“这是否意味着如果模型只有一个隐藏层和100个隐藏点，或者两个隐藏层(有多少个神经元？)或者三个隐藏层，那么模型是否会表现得更好？

from sklearn.neural_network import MLPRegressor
from sklearn.model_selection import GridSearchCV
X, y = scaled_df[[ "Part's Z-Height (mm)","Part's Weight (N)","Part's Volume (cm^3)","Part's Surface Area (cm^2)","Part's Orientation (Support's height) (mm)","Part's Orientation (Support's volume) (cm^3)","Layer Height (mm)","Printing/Scanning Speed (mm/s)","Infill Density (%)"]], scaled_df [["Climate change (kg CO2 eq.)","Climate change, incl biogenic carbon (kg CO2 eq.)","Fine Particulate Matter Formation (kg PM2.5 eq.)","Fossil depletion (kg oil eq.)","Freshwater Consumption (m^3)","Freshwater ecotoxicity (kg 1,4-DB eq.)","Freshwater Eutrophication (kg P eq.)","Human toxicity, cancer (kg 1,4-DB eq.)","Human toxicity, non-cancer (kg 1,4-DB eq.)","Ionizing Radiation (Bq. C-60 eq. to air)","Land use (Annual crop eq. yr)","Marine ecotoxicity (kg 1,4-DB eq.)","Marine Eutrophication (kg N eq.)","Metal depletion (kg Cu eq.)","Photochemical Ozone Formation, Ecosystem (kg NOx eq.)","Photochemical Ozone Formation, Human Health (kg NOx eq.)","Stratospheric Ozone Depletion (kg CFC-11 eq.)","Terrestrial Acidification (kg SO2 eq.)","Terrestrial ecotoxicity (kg 1,4-DB eq.)"]]

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)

mlp = MLPRegressor()
param_grid = {'hidden_layer_sizes': [(100,), (50,100,), (50,75,100,)],
              'activation': ['tanh','relu','lbfgs'],
              'solver': ['sgd', 'adam'],
              'learning_rate': ['constant','adaptive','invscaling'],
              'alpha': [0.0001, 0.05],
              'max_iter': [10000000000],
              'early_stopping': [False],
              'warm_start': [False]}
GS = GridSearchCV(mlp, param_grid=param_grid,n_jobs= -1,cv=5, scoring='r2')
                  
                  
GS.fit(X_train, y_train)

print(GS.best_params_)

Answer 1

是的，您正在执行网格搜索超参数优化。你正在尝试：

• 只有一层100单位的隐藏层
• 两个隐藏的50,100层
• 3层隐藏的50，75,100层

您的代码将提取超参数的所有组合，并告诉您哪一个性能最好。如果您只想提取这些组合中的一些，可以查找RandomSearch。否则，我建议超选择库使用贝叶斯方法(例如TPE)对超视距进行适当优化。