Python 3에서 Keras를 사용할 때 ValueError

이 코드를 Python (버전 3.7.4)에서 실행했는데 ValueError가 발생했습니다. 코드는 다음과 같습니다.

import numpy
import pandas
from keras.models import Sequential
from keras.layers import Dense
from keras.wrappers.scikit_learn import KerasClassifier
from sklearn.model_selection import cross_val_score
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import StratifiedKFold
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline

seed = 7
numpy.random.seed(seed)

dataframe = pandas.read_csv("sonar.all-data.csv", header=None)
dataset = dataframe.values

X = dataset[:,0:60].astype(float)
Y = dataset[:,60]

encoder = LabelEncoder()
encoder.fit(Y)
encoded_Y = encoder.transform(Y)

def create_baseline():
    model = Sequential()
    model.add(Dense(60, input_dim=60, kernel_initializer='normal', activation='relu'))
    model.add(Dense(1, kernel_initializer='normal', activation='sigmoid'))
    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
    return model

estimator = KerasClassifier(build_fn=create_baseline, epochs=100, batch_size=5, verbose=0)
kfold = StratifiedKFold(n_splits=10, shuffle=True, random_state=seed)
results = cross_val_score(estimator, X, encoded_Y, cv=kfold)
print("Baseline: %.2f%% (%.2f%%)" % (results.mean()*100, results.std()*100))

이것은 오류입니다.

ValueError                                Traceback (most recent call last)
<ipython-input-14-cbcbbcf75cec> in <module>
      2 estimator = KerasClassifier(build_fn=create_baseline, epochs=100, batch_size=5, verbose=0)
      3 kfold = StratifiedKFold(n_splits=10, shuffle=True, random_state=seed)
----> 4 results = cross_val_score(estimator, X, encoded_Y, cv=kfold)
      5 print("Baseline: %.2f%% (%.2f%%)" % (results.mean()*100, results.std()*100))

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py in cross_val_score(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch, error_score)
    387                                 fit_params=fit_params,
    388                                 pre_dispatch=pre_dispatch,
--> 389                                 error_score=error_score)
    390     return cv_results['test_score']
    391 

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py in cross_validate(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch, return_train_score, return_estimator, error_score)
    229             return_times=True, return_estimator=return_estimator,
    230             error_score=error_score)
--> 231         for train, test in cv.split(X, y, groups))
    232 
    233     zipped_scores = list(zip(*scores))

C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py in __call__(self, iterable)
    919             # remaining jobs.
    920             self._iterating = False
--> 921             if self.dispatch_one_batch(iterator):
    922                 self._iterating = self._original_iterator is not None
    923 

C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py in dispatch_one_batch(self, iterator)
    757                 return False
    758             else:
--> 759                 self._dispatch(tasks)
    760                 return True
    761 

C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py in _dispatch(self, batch)
    714         with self._lock:
    715             job_idx = len(self._jobs)
--> 716             job = self._backend.apply_async(batch, callback=cb)
    717             # A job can complete so quickly than its callback is
    718             # called before we get here, causing self._jobs to

C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py in apply_async(self, func, callback)
    180     def apply_async(self, func, callback=None):
    181         """Schedule a func to be run"""
--> 182         result = ImmediateResult(func)
    183         if callback:
    184             callback(result)

C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py in __init__(self, batch)
    547         # Don't delay the application, to avoid keeping the input
    548         # arguments in memory
--> 549         self.results = batch()
    550 
    551     def get(self):

C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py in __call__(self)
    223         with parallel_backend(self._backend, n_jobs=self._n_jobs):
    224             return [func(*args, **kwargs)
--> 225                     for func, args, kwargs in self.items]
    226 
    227     def __len__(self):

C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py in <listcomp>(.0)
    223         with parallel_backend(self._backend, n_jobs=self._n_jobs):
    224             return [func(*args, **kwargs)
--> 225                     for func, args, kwargs in self.items]
    226 
    227     def __len__(self):

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py in _fit_and_score(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, return_estimator, error_score)
    512             estimator.fit(X_train, **fit_params)
    513         else:
--> 514             estimator.fit(X_train, y_train, **fit_params)
    515 
    516     except Exception as e:

C:\ProgramData\Anaconda3\lib\site-packages\keras\wrappers\scikit_learn.py in fit(self, x, y, sample_weight, **kwargs)
    207         if sample_weight is not None:
    208             kwargs['sample_weight'] = sample_weight
--> 209         return super(KerasClassifier, self).fit(x, y, **kwargs)
    210 
    211     def predict(self, x, **kwargs):

C:\ProgramData\Anaconda3\lib\site-packages\keras\wrappers\scikit_learn.py in fit(self, x, y, **kwargs)
    149         fit_args.update(kwargs)
    150 
--> 151         history = self.model.fit(x, y, **fit_args)
    152 
    153         return history

C:\ProgramData\Anaconda3\lib\site-packages\keras\engine\training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)
   1152             sample_weight=sample_weight,
   1153             class_weight=class_weight,
-> 1154             batch_size=batch_size)
   1155 
   1156         # Prepare validation data.

C:\ProgramData\Anaconda3\lib\site-packages\keras\engine\training.py in _standardize_user_data(self, x, y, sample_weight, class_weight, check_array_lengths, batch_size)
    619                 feed_output_shapes,
    620                 check_batch_axis=False,  # Don't enforce the batch size.
--> 621                 exception_prefix='target')
    622 
    623             # Generate sample-wise weight values given the `sample_weight` and

C:\ProgramData\Anaconda3\lib\site-packages\keras\engine\training_utils.py in standardize_input_data(data, names, shapes, check_batch_axis, exception_prefix)
    143                             ': expected ' + names[i] + ' to have shape ' +
    144                             str(shape) + ' but got array with shape ' +
--> 145                             str(data_shape))
    146     return data
    147 

ValueError: Error when checking target: expected dense_8 to have shape (1,) but got array with shape (2,)

Jupyter 노트북에서 다음 버전을 사용하고 있습니다.

tensorflow : 2.0.0 (백엔드)

numpy : 1.16.4

판다 : 0.24.2

어려움 : 2.3.1

scikit-learn : 0.21.2

이 오류를 수정하기위한 제안 / 해결 방법에 감사드립니다. 귀하의 답변을 기대해 주셔서 감사합니다.