我想用sklearn交叉验证功能训练多输入keras模型。但我失败了。 sklearn不支持多输入模型？还有其他方法可以克服它吗？
这些是输入形状
for sheet in tower_dataset :
print sheet.shape
输出
(126L, 3L)
(126L, 45L)
(126L, 148L)
(126L, 148L)
(126L, 148L)
(126L, 148L)
(126L, 148L)
(126L, 148L)
(126L, 148L)
(126L, 148L)
(126L, 100L)
(126L, 296L)
(126L, 296L)
(126L, 176L)
(126L, 31L)
(126L, 5L)
而我试图做的是用sklearn交叉验证训练keras模型。
epochs = 100
n_folds = 10
model = KerasClassifier(build_fn=create_model, nb_epoch=150, batch_size=10)
skf = StratifiedKFold(y=label, n_folds=n_folds, shuffle=True, random_state=rand_seed)
kfold = StratifiedKFold(y=label, n_folds=n_folds, shuffle=True, random_state=rand_seed)
错误输出
results = cross_val_score(model, tower_dataset, label, cv=kfold)
print(results.mean())
ValueErrorTraceback (most recent call last)
<ipython-input-219-924ce2fda183> in <module>()
6 kfold = StratifiedKFold(y=label, n_folds=n_folds, shuffle=True, random_state=rand_seed)
7
----> 8 results = cross_val_score(model, tower_dataset, label, cv=kfold)
9 print(results.mean())
10
C:\Users\user\Anaconda2\lib\site-packages\sklearn\cross_validation.pyc in cross_val_score(estimator, X, y, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch)
1420 Array of scores of the estimator for each run of the cross validation.
1421 """
-> 1422 X, y = indexable(X, y)
1423
1424 cv = check_cv(cv, X, y, classifier=is_classifier(estimator))
C:\Users\user\Anaconda2\lib\site-packages\sklearn\utils\validation.pyc in indexable(*iterables)
199 else:
200 result.append(np.array(X))
--> 201 check_consistent_length(*result)
202 return result
203
C:\Users\user\Anaconda2\lib\site-packages\sklearn\utils\validation.pyc in check_consistent_length(*arrays)
174 if len(uniques) > 1:
175 raise ValueError("Found arrays with inconsistent numbers of samples: "
--> 176 "%s" % str(uniques))
177
178
ValueError: Found arrays with inconsistent numbers of samples: [ 16 126]