我尝试使用Keras Scikit Learn 包装器来简化参数的随机搜索。我在这里写了一个示例代码，其中：
- 我生成了一个人工数据集：
我使用了scikit learn中的moons
from sklearn.datasets import make_moonsdataset = make_moons(1000)- 模型构建器定义：
我定义了所需的build_fn函数：
def build_fn(nr_of_layers = 2, first_layer_size = 10, layers_slope_coeff = 0.8, dropout = 0.5, activation = "relu", weight_l2 = 0.01, act_l2 = 0.01, input_dim = 2): result_model = Sequential() result_model.add(Dense(first_layer_size, input_dim = input_dim, activation=activation, W_regularizer= l2(weight_l2), activity_regularizer=activity_l2(act_l2) )) current_layer_size = int(first_layer_size * layers_slope_coeff) + 1 for index_of_layer in range(nr_of_layers - 1): result_model.add(BatchNormalization()) result_model.add(Dropout(dropout)) result_model.add(Dense(current_layer_size, W_regularizer= l2(weight_l2), activation=activation, activity_regularizer=activity_l2(act_l2) )) current_layer_size = int(current_layer_size * layers_slope_coeff) + 1 result_model.add(Dense(1, activation = "sigmoid", W_regularizer = l2(weight_l2))) result_model.compile(optimizer="rmsprop", metrics = ["accuracy"], loss = "binary_crossentropy") return result_modelNeuralNet = KerasClassifier(build_fn)- 参数网格定义：
然后我定义了一个参数网格：
param_grid = { "nr_of_layers" : [2, 3, 4, 5], "first_layer_size" : [5, 10, 15], "layers_slope_coeff" : [0.4, 0.6, 0.8], "dropout" : [0.3, 0.5, 0.8], "weight_l2" : [0.01, 0.001, 0.0001], "verbose" : [0], "batch_size" : [1], "nb_epoch" : [30]}- 随机搜索阶段：
我定义了RandomizedSearchCV对象并使用人工数据集的值进行拟合：
random_search = RandomizedSearchCV(NeuralNet, param_distributions=param_grid, verbose=2, n_iter=1, scoring="roc_auc")random_search.fit(dataset[0], dataset[1])运行这段代码后，我在控制台中得到的结果是：
Traceback (most recent call last): File "C:\Anaconda2\lib\site-packages\IPython\core\interactiveshell.py", line 2885, in run_code exec(code_obj, self.user_global_ns, self.user_ns) File "<ipython-input-3-c5bdbc2770b7>", line 2, in <module> random_search.fit(dataset[0], dataset[1]) File "C:\Anaconda2\lib\site-packages\sklearn\grid_search.py", line 996, in fit return self._fit(X, y, sampled_params) File "C:\Anaconda2\lib\site-packages\sklearn\grid_search.py", line 553, in _fit for parameters in parameter_iterable File "C:\Anaconda2\lib\site-packages\sklearn\externals\joblib\parallel.py", line 800, in __call__ while self.dispatch_one_batch(iterator): File "C:\Anaconda2\lib\site-packages\sklearn\externals\joblib\parallel.py", line 658, in dispatch_one_batch self._dispatch(tasks) File "C:\Anaconda2\lib\site-packages\sklearn\externals\joblib\parallel.py", line 566, in _dispatch job = ImmediateComputeBatch(batch) File "C:\Anaconda2\lib\site-packages\sklearn\externals\joblib\parallel.py", line 180, in __init__ self.results = batch() File "C:\Anaconda2\lib\site-packages\sklearn\externals\joblib\parallel.py", line 72, in __call__ return [func(*args, **kwargs) for func, args, kwargs in self.items] File "C:\Anaconda2\lib\site-packages\sklearn\cross_validation.py", line 1550, in _fit_and_score test_score = _score(estimator, X_test, y_test, scorer) File "C:\Anaconda2\lib\site-packages\sklearn\cross_validation.py", line 1606, in _score score = scorer(estimator, X_test, y_test) File "C:\Anaconda2\lib\site-packages\sklearn\metrics\scorer.py", line 175, in __call__ y_pred = y_pred[:, 1]IndexError: index 1 is out of bounds for axis 1 with size 1当我使用accuracy指标代替scoring = "roc_auc"时，这段代码可以正常工作。谁能解释一下这是怎么回事？有没有人遇到过类似的问题？
回答：
KerasClassifier 中有一个导致此问题的错误。我已经在仓库中为此开了个问题。 https://github.com/fchollet/keras/issues/2864
修复方法也在其中。作为临时解决方案，你可以定义自己的 KerasClassifier。
class FixedKerasClassifier(KerasClassifier): def predict_proba(self, X, **kwargs): kwargs = self.filter_sk_params(Sequential.predict_proba, kwargs) probs = self.model.predict_proba(X, **kwargs) if(probs.shape[1] == 1): probs = np.hstack([1-probs,probs]) return probs