Python ValueError：模型未配置为计算精度。您应该将`metrics=["；accurity"；]`传递给`model.compile（）`方法

我对深度学习非常陌生： 我正在做一个关于我的UDE的深入学习课程。一旦我执行代码，它就会说：

ValueError：模型未配置为计算精度。您应该将

metrics=[“accurity”]

传递给

model.compile（）

方法

我试着把“准确度”改成“acc”，它不会给我更多的错误，但是代码非常快，它只显示了10个纪元而不是100个纪元。此外，我预计准确率在83%左右，但我的准确率大约为79%

有人能帮我吗

'''

''' 这是我的完整回溯：

'''

---

“

在您的导入中，您在

keras

和

tf.keras

包之间混合导入，实际上不受支持，您应该选择一个并从所选包进行所有导入。

您能在问题中包含完整的回溯吗？@MatiasValdenegro对不起，您所说的完整回溯是什么意思？你能简单解释一下吗？完整的调用stacktrace，它以“Traceback（最近一次调用last）”开头，你在标题中包含的只是异常错误，这不是所有可用的信息。@MatiasValdenegro：如果我理解正确，这是完整的回拨：@MatiasValdenegro我编辑了问题并添加了完整的回拨

import keras
from keras.wrappers.scikit_learn import KerasClassifier
from sklearn.model_selection import cross_val_score
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense

def build_classifier():
    classifier = Sequential()
    classifier.add(Dense(6, kernel_initializer='uniform', activation='relu', input_dim= 11))
    classifier.add(Dense(6, kernel_initializer='uniform', activation='relu'))
    classifier.add(Dense(1, kernel_initializer='uniform', activation='sigmoid'))
    classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
    return classifier

classifier = KerasClassifier(build_fn= build_classifier, batch_size=10 , nb_epoch=100)
accuracies = cross_val_score(estimator = classifier, X = x_train, y = y_train, cv=10, n_jobs=-1)

accuracies = cross_val_score(estimator = classifier, X = x_train, y = y_train, cv=10, n_jobs=-1, verbose=0)
Traceback (most recent call last):

  File "<ipython-input-138-36c63e93365e>", line 1, in <module>
    accuracies = cross_val_score(estimator = classifier, X = x_train, y = y_train, cv=10, n_jobs=-1, verbose=0)

  File "C:\ProgramData\Anaconda3\envs\deeplearning\lib\site-packages\sklearn\model_selection\_validation.py", line 389, in cross_val_score
    error_score=error_score)

  File "C:\ProgramData\Anaconda3\envs\deeplearning\lib\site-packages\sklearn\model_selection\_validation.py", line 235, in cross_validate
    for train, test in cv.split(X, y, groups))

  File "C:\ProgramData\Anaconda3\envs\deeplearning\lib\site-packages\joblib\parallel.py", line 1017, in __call__
    self.retrieve()

  File "C:\ProgramData\Anaconda3\envs\deeplearning\lib\site-packages\joblib\parallel.py", line 909, in retrieve
    self._output.extend(job.get(timeout=self.timeout))

  File "C:\ProgramData\Anaconda3\envs\deeplearning\lib\site-packages\joblib\_parallel_backends.py", line 562, in wrap_future_result
    return future.result(timeout=timeout)

  File "C:\ProgramData\Anaconda3\envs\deeplearning\lib\concurrent\futures\_base.py", line 435, in result
    return self.__get_result()

  File "C:\ProgramData\Anaconda3\envs\deeplearning\lib\concurrent\futures\_base.py", line 384, in __get_result
    raise self._exception

ValueError: The model is not configured to compute accuracy. You should pass `metrics=["accuracy"]` to the `model.compile()` method.
''''

try:

    accuracies = cross_val_score(estimator = classifier, X = x_train, y = y_train, cv=10, n_jobs=-1, verbose=0)
except ValueError as e:
    print ("ValueError:", e)
ValueError: The model is not configured to compute accuracy. You should pass `metrics=["accuracy"]` to the `model.compile()` method.