Python 使用GridsearchCV时访问验证数据Keras

我想使用GridsearchCV构建一个参数调优管道。我的模型是一个（二进制）分类器，我使用Keras Sequential（）构建它

由于我处理的是一个倾斜数据集（大约6/7标签为0，剩下1/7部分的数据集标记为1），因此我添加了一个回调，用于在每个历元结束时计算f1、召回率和精度指标，我希望将其用作验证模型的指标

为此，我在Keras中使用了一个回调函数，它需要在模型的fit（）实例中指定我的验证数据集。这反过来又使得访问验证集和使用GridsearchCV变得非常困难

我通过构建一种DIY简历程序成功地克服了这一问题，但我想知道是否可以结合GridsearchCV更有效地实现这一点。 这是我的密码：

设置不同的分类阈值

输入：x，神经网络学习产生的预测向量 thr，我们用于将预测分类为0或1类的阈值。 输出：包含0和1的向量，预测消息的标签

然后，我使用以下方法创建模型：

def create_model(optimizer="adam", dropout=0.1, init='uniform'):
model = Sequential()
model.add(Dense(1,input_shape=(N_FEATURES,), kernel_initializer=init,))  
model.add(Activation('sigmoid'))
model.compile(loss = 'binary_crossentropy', optimizer = OPTIMIZER,
            #metrics=['accuracy','binary_accuracy']
            )
return model
model = KerasClassifier(build_fn=create_model, verbose=1)
N_FEATURES = X_train.shape[1]
thresholds = [0.2,0.3,0.5] #0.15
EPOCHS =200
BATCH_SIZE = 256
VERBOSE = 1
OPTIMIZER = Adadelta()
N_HIDDEN = 2000
cv_repetitions = 5

现在，我希望优化的代码部分： 是否有一种方法可以使用GridsearchCV将这些循环括起来，并为交叉验证部分包含更多参数

提前感谢您的阅读和帮助

class Metrics(Callback):

def on_train_begin(self, logs={}):
    self.val_f1s = []
    self.val_recalls = []
    self.val_precisions = []

def on_epoch_end(self, epoch, logs={}):
    val_predict = pred_round(self.model.predict(self.validation_data[0]), threshold)
    val_targ = self.validation_data[1]
    _val_precision, _val_recall, _val_f1, dummy = precision_recall_fscore_support(val_targ, val_predict,beta = 1.0,average = 'binary')
    self.val_f1s.append(_val_f1)
    self.val_recalls.append(_val_recall)
    self.val_precisions.append(_val_precision)
metrics = Metrics()

def create_model(optimizer="adam", dropout=0.1, init='uniform'):
model = Sequential()
model.add(Dense(1,input_shape=(N_FEATURES,), kernel_initializer=init,))  
model.add(Activation('sigmoid'))
model.compile(loss = 'binary_crossentropy', optimizer = OPTIMIZER,
            #metrics=['accuracy','binary_accuracy']
            )
return model
model = KerasClassifier(build_fn=create_model, verbose=1)
N_FEATURES = X_train.shape[1]
thresholds = [0.2,0.3,0.5] #0.15
EPOCHS =200
BATCH_SIZE = 256
VERBOSE = 1
OPTIMIZER = Adadelta()
N_HIDDEN = 2000
cv_repetitions = 5

for threshold in thresholds:
    i=0
    f1_cv_scores = np.zeros(EPOCHS)
    recall_cv_scores = np.zeros(EPOCHS)
    precision_cv_scores = np.zeros(EPOCHS)
    loss_train_scores = np.zeros(EPOCHS)
    loss_cv_scores = np.zeros(EPOCHS)
    for i in range(cv_repetitions):
        i+=1
        X_train_NN, X_val_NN, y_train_NN, y_val_NN = train_test_split(X_train,y_train,test_size= 0.2,stratify = y_train)


        history = model.fit( x= X_train_NN, y= y_train_NN,
                            batch_size = BATCH_SIZE,
                            validation_data = (X_val_NN,y_val_NN),
                            epochs = EPOCHS,
                            verbose = VERBOSE,
                            callbacks = [metrics]
                            )
        loss_train_scores += history.history['loss']
        loss_cv_scores   += history.history['val_loss']
        f1_cv_scores += metrics.val_f1s
        recall_cv_scores += metrics.val_recalls
        precision_cv_scores += metrics.val_precisions

    loss_train_scores = loss_test_scores/cv_repetitions
    loss_cv_scores   = loss_cv_scores/cv_repetitions
    f1_cv_scores     = f1_cv_scores /cv_repetitions
    recall_cv_scores = recall_cv_scores/cv_repetitions
    precision_cv_scores = precision_cv_scores/cv_repetitions