做GridsearchCV时获取'function'对象没有属性'loss'

Question

我想在我的模型上尝试 GridsearchCV，我的导入是：

from keras import models
from keras import layers
from keras import regularizers
from sklearn.model_selection import GridSearchCV
from keras.wrappers.scikit_learn import KerasClassifier

我的代码是：

def build_model(X_train = X_train,neurons=4,optimizer='Adam'):
    model = models.Sequential()
    model.add(layers.Dense(X_train.shape[1], kernel_regularizer=regularizers.l2(0.001), 
                           activation='relu', input_shape=(X_train.shape[1],)))
    model.add(layers.BatchNormalization())
    model.add(layers.Dense(neurons,  kernel_regularizer=regularizers.l2(0.001), activation='relu'))
    model.add(layers.BatchNormalization())
    model.add(layers.Dense(1, activation='sigmoid'))

    model.compile(optimizer=optimizer,loss='binary_crossentropy',metrics=['accuracy'])

    return build_model

model = KerasClassifier(build_fn=build_model, verbose=1)

# define the grid search parameters
batch_size = [16, 32, 64]
epochs = [50, 100]
param_grid = dict(batch_size=batch_size, epochs=epochs)

# search the grid
grid = GridSearchCV(estimator=model, 
                    param_grid=param_grid,
                    cv=10,
                    verbose=2)  
grid_result = grid.fit(X_train, y_train)

但我遇到如下错误：

AttributeError                            Traceback (most recent call last)
<ipython-input-93-2eb813d3aab7> in <module>
     12                     verbose=2)  # include n_jobs=-1 if you are using CPU
     13 
---> 14 grid_result = grid.fit(X_train, y_train)
     15 
     16 print(model)


/anaconda3/envs/lance/lib/python3.7/site-packages/keras/wrappers/scikit_learn.py in fit(self, x, y, sample_weight, **kwargs)
    208         if sample_weight is not None:
    209             kwargs['sample_weight'] = sample_weight
--> 210         return super(KerasClassifier, self).fit(x, y, **kwargs)
    211 
    212     def predict(self, x, **kwargs):



/anaconda3/envs/lance/lib/python3.7/site-packages/keras/wrappers/scikit_learn.py in fit(self, x, y, **kwargs)
    141             self.model = self.build_fn(**self.filter_sk_params(self.build_fn))
    142 
--> 143         loss_name = self.model.loss
    144         if hasattr(loss_name, '__name__'):
    145             loss_name = loss_name.__name__

AttributeError: 'function' object has no attribute 'loss'

我不明白这个错误是什么，我确定数据处理是正确的，因为它在没有网格搜索的情况下运行良好，我做错了吗？

Answer 1

在build_model函数的末尾，你写return build_model。此 returns 是对函数本身的引用，而不是对您目前构建的模型对象的引用。我很确定你想要 return model。

做GridsearchCV时获取'function'对象没有属性'loss'

Get 'function' object has no attribute 'loss' when doing GridsearchCV

python

deep-learning

hyperparameters

keras

grid-search