如何为RandomSearchCV定义具有两个隐藏层的MLPR
如何为RandomSearchCV定义具有两个隐藏层的MLPR
提问于 2019-02-18 01:21:50
我正在尝试弄清楚如何定义一个具有两个隐藏层的MLPR的参数网格，以便在SkLearn中输入到RandomSearchCV？
下面是我一直在尝试的。那么，如何对RandomSearchCV的hidden_layer_sizes进行随机化呢？
import numpy as np
import pandas as pd
from sklearn.neural_network import MLPRegressor
from sklearn.datasets import load_boston
from sklearn.model_selection import RandomizedSearchCV
boston = load_boston()
X = boston.data
y = boston.target
params = {'activation':['logistic', 'relu'],
'learning_rate':['adaptive'],
'alpha':np.logspace(0.0001, 100, 10),
'max_iter':[1000],
'hidden_layer_sizes':[(10,10), (30,10), (50,20), (60,30)]}
reg = MLPRegressor()
random_search = RandomizedSearchCV(estimator = reg,
param_distributions=params,
n_iter=10,
scoring = 'neg_mean_squared_error',
cv=3,
n_jobs = -3,
pre_dispatch = '2*n_jobs',
return_train_score = True)
random_search.fit(X,y)
df = pd.DataFrame(random_search.cv_results_)
df['train_RMSE'] = np.sqrt(-df['mean_train_score'])
df['test_RMSE'] = np.sqrt(-df['mean_test_score'])
print(random_search.best_params_)附言:如果任何人对我选择的参数有任何意见，请随时发表评论。这些参数将用于最多具有7个输入的回归问题。
有什么想法吗？
回答 1
Stack Overflow用户
发布于 2019-02-18 08:36:21
是的，你做得对。此外，您还可以设置verbose级别以查看上一次交叉验证使用的超参数，例如[CV] activation=tanh, alpha=1e+100, hidden_layer_sizes=(30, 10), score=-4.180054117738231, total= 2.7s。
我选择了GridSearchCV而不是RandomizedSearchCV来查找最佳参数集，在我的机器上花了5分钟。
import numpy as np
from sklearn.neural_network import MLPRegressor
from sklearn.datasets import load_boston
from sklearn.model_selection import GridSearchCV, train_test_split
from sklearn.metrics import explained_variance_score
X, y = load_boston(return_X_y=True)
# Split data for final evaluation:
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, shuffle=True, random_state=42)
# Define base regressor:
base_reg = MLPRegressor(learning_rate='adaptive', max_iter=5000, random_state=42)
# Define search space:
params = {
'activation': ['logistic', 'relu', 'tanh'], # <-- added 'tanh' as third non-linear activation function
'alpha': np.logspace(0.0001, 100, 10),
'hidden_layer_sizes': [
(10, 10), (20, 10), (30, 10),
(40, 10), (90, 10), (90, 30, 10) # <-- added more neurons or layers
]
}
# Find best hyper params and then refit on all training data:
reg = GridSearchCV(estimator=base_reg, param_grid=params,
n_jobs=8, cv=3, refit=True, verbose=5) # <-- verbose=5
reg.fit(X_train, y_train)
print(reg.best_estimator_)
# MLPRegressor(activation='logistic', alpha=1.0002302850208247,
# batch_size='auto', beta_1=0.9, beta_2=0.999, early_stopping=False,
# epsilon=1e-08, hidden_layer_sizes=(30, 10),
# learning_rate='adaptive', learning_rate_init=0.001, max_iter=5000,
# momentum=0.9, n_iter_no_change=10, nesterovs_momentum=True,
# power_t=0.5, random_state=42, shuffle=True, solver='adam',
# tol=0.0001, validation_fraction=0.1, verbose=False,
# warm_start=False)
print(reg.best_params_)
# {'activation': 'logistic', 'alpha': 1.0002302850208247, 'hidden_layer_sizes': (30, 10)}
# Evaluate on unseen test data:
err = explained_variance_score(y_train, reg.predict(X_train))
print(err) # 0.8936815412058757
err = explained_variance_score(y_test, reg.predict(X_test))
print(err) # 0.801353064635174页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：
https://stackoverflow.com/questions/54735717
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