我使用了Keras回归量来对数据进行回归 . 我使用Scikit-learn包装器和Pipeline来首先标准化数据，然后将其放在Keras回归器上 . 有点像：
from sklearn.grid_search import GridSearchCV
from keras.models import Sequential
from keras.layers import Dense
from keras.wrappers.scikit_learn import KerasRegressor
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
from sklearn.cross_validation import cross_val_score
from sklearn.cross_validation import KFold
from sklearn.externals import joblib
import cPickle
import pandas as pd
import os
from create_model import *
estimators = []
estimators.append(('standardize', StandardScaler()))
estimators.append(('mlp', KerasRegressor(build_fn=create_model, nb_epoch=50, batch_size=5, verbose=0, neurons = 1)))
pipeline = Pipeline(estimators)
然后我通过GridSearchCv网格搜索最佳匹配，并获得最适合变量：
batch_size = [60, 80, 100, 200]
epochs = [2, 4, 6, 8, 10, 50]
neurons = np.arange(3,10,1)
optimizer = ['sgd', 'adam', 'rmsprom']
activation = ['relu', 'tanh']
lr = [0.001, 0.01, 0.1]
param_grid = dict(mlp__neurons = neurons, mlp__batch_size = batch_size, mlp__nb_epoch = epochs, mlp__optimizer = optimizer, mlp__activation = activation, mlp__learn_rate = lr)
grid = GridSearchCV(estimator=pipeline, param_grid=param_grid, cv = kfold,scoring='mean_squared_error')
grid_result = grid.fit(X, Y)
clf = []
clf = grid_result.best_estimator_
clf变量有2个流程，如管道中定义的那样 . 我的问题是如何通过get_params函数提取keras回归量的权重和偏差以获得最佳拟合（clf）？
clf.get_params()
我找不到这方面的好文件 .
1 回答
weights = KerasRegressor.model.layers [0] .get_weights（）[0] biases = KerasRegressor.model.layers [0] .get_weights（）[1]