我正在尝试为用Keras构建的神经网络进行参数调整。这是我的代码，其中有一行注释说明了错误发生的位置：
from sklearn.cross_validation import StratifiedKFold, cross_val_scorefrom sklearn import grid_searchfrom sklearn.metrics import classification_reportimport multiprocessingfrom keras.models import Sequentialfrom keras.layers import Densefrom sklearn.preprocessing import LabelEncoderfrom keras.utils import np_utilsfrom keras.wrappers.scikit_learn import KerasClassifierimport numpy as npdef tuning(X_train,Y_train,X_test,Y_test): in_size=X_train.shape[1] num_cores=multiprocessing.cpu_count() model = Sequential() model.add(Dense(in_size, input_dim=in_size, init='uniform', activation='relu')) model.add(Dense(8, init='uniform', activation='relu')) model.add(Dense(1, init='uniform', activation='sigmoid')) model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) batch_size = [10, 20, 40, 60, 80, 100] epochs = [10,20] param_grid = dict(batch_size=batch_size, nb_epoch=epochs) k_model = KerasClassifier(build_fn=model, verbose=0) clf = grid_search.GridSearchCV(estimator=k_model, param_grid=param_grid, cv=StratifiedKFold(Y_train, n_folds=10, shuffle=True, random_state=1234), scoring="accuracy", verbose=100, n_jobs=num_cores) clf.fit(X_train, Y_train) #ERROR HERE print("Best parameters set found on development set:") print() print(clf.best_params_) print() print("Grid scores on development set:") print() for params, mean_score, scores in clf.grid_scores_: print("%0.3f (+/-%0.03f) for %r" % (mean_score, scores.std() * 2, params)) print() print("Detailed classification report:") print() print("The model is trained on the full development set.") print("The scores are computed on the full evaluation set.") print() y_true, y_pred = Y_test, clf.predict(X_test) print(classification_report(y_true, y_pred)) print()这是错误报告：
clf.fit(X_train, Y_train) File "/usr/local/lib/python2.7/dist-packages/sklearn/grid_search.py", line 804, in fit return self._fit(X, y, ParameterGrid(self.param_grid)) File "/usr/local/lib/python2.7/dist-packages/sklearn/grid_search.py", line 553, in _fit for parameters in parameter_iterable File "/usr/local/lib/python2.7/dist-packages/sklearn/externals/joblib/parallel.py", line 800, in __call__ while self.dispatch_one_batch(iterator): File "/usr/local/lib/python2.7/dist-packages/sklearn/externals/joblib/parallel.py", line 658, in dispatch_one_batch self._dispatch(tasks) File "/usr/local/lib/python2.7/dist-packages/sklearn/externals/joblib/parallel.py", line 566, in _dispatch job = ImmediateComputeBatch(batch) File "/usr/local/lib/python2.7/dist-packages/sklearn/externals/joblib/parallel.py", line 180, in __init__ self.results = batch() File "/usr/local/lib/python2.7/dist-packages/sklearn/externals/joblib/parallel.py", line 72, in __call__ return [func(*args, **kwargs) for func, args, kwargs in self.items] File "/usr/local/lib/python2.7/dist-packages/sklearn/cross_validation.py", line 1531, in _fit_and_score estimator.fit(X_train, y_train, **fit_params) File "/usr/local/lib/python2.7/dist-packages/keras/wrappers/scikit_learn.py", line 135, in fit **self.filter_sk_params(self.build_fn.__call__))TypeError: __call__() takes at least 2 arguments (1 given)我是不是遗漏了什么？网格搜索在随机森林、SVM和逻辑回归上运行得很好。我只在神经网络上遇到问题。
回答：
这里的错误表明build_fn需要有两个参数，正如param_grid中的参数数量所示。
所以你需要明确定义一个新函数，并将其用作build_fn=make_model
def make_model(batch_size, nb_epoch): model = Sequential() model.add(Dense(in_size, input_dim=in_size, init='uniform', activation='relu')) model.add(Dense(8, init='uniform', activation='relu')) model.add(Dense(1, init='uniform', activation='sigmoid')) model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) return model还可以查看keras/examples/mnist_sklearn_wrapper.py，其中使用GridSearchCV进行超参数搜索。
