我试图根据 ‘input_data’ 中的特征预测收盘价（1 或 0）。但当我运行代码时，出现了下面的错误，我不确定如何修复这个问题。任何帮助都将不胜感激，谢谢

Traceback (most recent call last):  File "F:/Machine Learning/SK_Learn/SVM_Stock.py", line 71, in <module>    estimator.fit(x,y)  File "C:\Python35\lib\site-packages\keras\wrappers\scikit_learn.py", line 210, in fit    return super(KerasClassifier, self).fit(x, y, **kwargs)  File "C:\Python35\lib\site-packages\keras\wrappers\scikit_learn.py", line 139, in fit    **self.filter_sk_params(self.build_fn.__call__))TypeError: __call__() missing 1 required positional argument: 'inputs'

这是代码:

class SVM_Stock:    def __init__(self):        pass    def create_model(self):        model = Sequential()        model.add(Dense(14, input_dim=16, kernel_initializer='normal', activation='relu'))        model.add(Dense(7, kernel_initializer='normal', activation='relu'))        model.add(Dense(1, kernel_initializer='normal', activation='sigmoid'))        model.compile(loss='binary_crossentropy',optimizer='rmsprop', metrics=['accuracy'])        return modelif __name__ == "__main__":    desired_width = 450    pd.set_option('display.width', desired_width)    pd.set_option('display.max_columns', 17)    ds = pd.read_csv('F:\\Machine Learning\\Linear Regression\\BIOCON-EQ.csv')    ds = ds[['Date','Open','High','Low','Close','Volume','Slow VWMA','Fast VWMA']][14:].sort_values('Date')    ds.loc[ds['Slow VWMA'] > ds['Fast VWMA'], 'Trend UP'] = 1    ds.loc[ds['Slow VWMA'] < ds['Fast VWMA'], 'Trend UP'] = 0    ds.loc[ds['Slow VWMA'] == ds['Fast VWMA'], 'Trend UP'] = -1    ds.loc[ds['Slow VWMA'] < ds['Fast VWMA'], 'Trend Down'] = 1    ds.loc[ds['Slow VWMA'] > ds['Fast VWMA'], 'Trend Down'] = 0    ds.loc[ds['Slow VWMA'] == ds['Fast VWMA'], 'Trend Down'] = -1    ds.loc[ds['Close'] > ds['Open'], 'Close Price'] = 1    ds.loc[ds['Close'] < ds['Open'], 'Close Price'] = 0    ds.loc[ds['Close'] == ds['Open'], 'Close Price'] = -1    input_data = ds[['Date','Open','High','Low','Close','Trend UP', 'Trend     Down']]    input_data.index = input_data.Date    input_data.drop('Date', axis=1, inplace=True)    target = ds[['Close Price']]    scaler = MinMaxScaler(feature_range=(0, 1))    x = scaler.fit_transform(input_data)    y = target.values.ravel()    # clf = svm.SVC(gamma=0.1, C=100)    # clf.fit(x[:400], y[:400])    # print(clf.score(x[:400], y[:400]))    #    # for i in range(420, len(x)):    #     print("Prediction :", clf.predict(x[i].reshape(1, -1)))    #     print(i, y[i])    SS = SVM_Stock()    estimator = KerasClassifier(build_fn=SS.create_model(), nb_epoch=10, verbose=0)    estimator.fit(x,y)    '''Cross Validate'''    cv_scores = cross_val_score(estimator, x, y, cv=10)    print(cv_scores.mean())

回答：

在创建你的估计器时，你应该传递 create_model 函数而不调用它（即不加括号）:

estimator = KerasClassifier(build_fn=SS.create_model, nb_epoch=10, verbose=0)