Question

我正在使用hyperas document example来调整网络参数，但是基于f1得分而不是准确性。

我正在将以下实现用于f1得分：

from keras import backend as K

def f1(y_true, y_pred):
    def recall(y_true, y_pred):
        """Recall metric.
        Only computes a batch-wise average of recall.
        Computes the recall, a metric for multi-label classification of
        how many relevant items are selected.
        """
        true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
        possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))
        recall = true_positives / (possible_positives + K.epsilon())
        return recall

    def precision(y_true, y_pred):
        """Precision metric.
        Only computes a batch-wise average of precision.
        Computes the precision, a metric for multi-label classification of
        how many selected items are relevant.
        """
        true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
        predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))
        precision = true_positives / (predicted_positives + K.epsilon())
        return precision
    precision = precision(y_true, y_pred)
    recall = recall(y_true, y_pred)
    return 2*((precision*recall)/(precision+recall+K.epsilon()))

在以下代码行中更新用于编译功能的度量参数：

model.compile(loss='categorical_crossentropy', metrics=['accuracy'],
                  optimizer={{choice(['rmsprop', 'adam', 'sgd'])}})

到

model.compile(loss='categorical_crossentropy', metrics=[f1],
                  optimizer={{choice(['rmsprop', 'adam', 'sgd'])}})

上述指标在不使用hyperas的情况下也可以完美运行，而当我尝试在调整过程中使用它时，出现以下错误：

Traceback (most recent call last):
  File "D:/path/test.py", line 96, in <module>
    trials=Trials())
  File "C:\Python35\lib\site-packages\hyperas\optim.py", line 67, in minimize
    verbose=verbose)
  File "C:\Python35\lib\site-packages\hyperas\optim.py", line 133, in base_minimizer
    return_argmin=True),
  File "C:\Python35\lib\site-packages\hyperopt\fmin.py", line 367, in fmin
    return_argmin=return_argmin,
  File "C:\Python35\lib\site-packages\hyperopt\base.py", line 635, in fmin
    return_argmin=return_argmin)
  File "C:\Python35\lib\site-packages\hyperopt\fmin.py", line 385, in fmin
    rval.exhaust()
  File "C:\Python35\lib\site-packages\hyperopt\fmin.py", line 244, in exhaust
    self.run(self.max_evals - n_done, block_until_done=self.asynchronous)
  File "C:\Python35\lib\site-packages\hyperopt\fmin.py", line 218, in run
    self.serial_evaluate()
  File "C:\Python35\lib\site-packages\hyperopt\fmin.py", line 137, in serial_evaluate
    result = self.domain.evaluate(spec, ctrl)
  File "C:\Python35\lib\site-packages\hyperopt\base.py", line 840, in evaluate
    rval = self.fn(pyll_rval)
  File "D:\path\temp_model.py", line 86, in keras_fmin_fnct
NameError: name 'f1' is not defined

Answer 1

如果您遵循链接到的代码示例，则不会使Hyperas意识到自定义f1函数。程序包作者也提供了example to do that。

简而言之，您需要在$db = new mysqli("localhost","paroshic_paroshic","kxmcwQzLTrTR","paroshic_matri2018jl"); $sql = "select * from tbldatingusermaster order by userid desc"; $result = $db->query( $sql ); if( $result ){ while( $data = $result->fetch_object() ){ /* explode the string into little integers */ $ids=explode( ',', $data->educationid ); /* iterate through the pieces and generate an input[checkbox] element */ foreach( $ids as $id )printf('<input type="checkbox" name="UNKNOWN[]" value="%s" />',$id); } }调用中添加一个附加的functions参数。像

optim.minimize()

我实际上实际上是在今天实现的，所以我相信您也可以使用它：）

使用手动度量与Hyperas进行Keras超参数调整

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