[英]Keras hyperparameter tuning with hyperas using manual metric
我正在使用hyperas文檔示例來調整網絡參數,但基於f1得分而不是准確性。
我將以下實現用於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()))
在以下代碼行中更新用於編譯功能的度量參數:
model.compile(loss='categorical_crossentropy', metrics=['accuracy'],
optimizer={{choice(['rmsprop', 'adam', 'sgd'])}})
至
model.compile(loss='categorical_crossentropy', metrics=[f1],
optimizer={{choice(['rmsprop', 'adam', 'sgd'])}})
上面的指標在不使用hyperas的情況下可以完美運行,而當我嘗試在調整過程中使用它時,出現以下錯誤:
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
如果您正在遵循鏈接到的代碼示例,則不會使hyperas意識到自定義f1函數。 包作者也提供了一個示例來執行此操作 。
簡而言之,您需要在optim.minimize()
調用中添加一個附加的functions
參數。 就像是
best_run, best_model = optim.minimize(model=model,
data=data,
functions=[f1],
algo=tpe.suggest,
max_evals=5,
trials=Trials())
我實際上只是在今天實現了它,所以我相信您也可以使它起作用:)
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