处理打包方法时，如何修复TypeError：+：'int‘和'NoneType’的操作数类型不受支持

Question

我需要对LSTM使用打包方法，对时间序列数据进行训练。我已经定义了模型库，并使用KerasRegressor链接到scikit learn。但是有损失：'KerasRegressor‘对象没有’AttributeError‘属性。我怎么才能修复它呢？

更新:我使用了Manoj Mohan的方法(在第一条评论中)，并成功地完成了fit步骤。但是，当我将Manoj Mohan的类修改为TypeError时，问题就来了。

class MyKerasRegressor(KerasRegressor): 
    def fit(self, x, y, **kwargs):
        x = np.expand_dims(x, -2)
        super().fit(x, y, **kwargs)

    def predict(self, x, **kwargs):
        x = np.expand_dims(x, -2)
        super().predict(x, **kwargs)

它解决了predict()与.fit()相同的维数问题。问题是：

TypeError                                 Traceback (most recent call last)
<ipython-input-84-68d76cb73e8b> in <module>
----> 1 pred_bag = bagging_model.predict(x_test)
TypeError: unsupported operand type(s) for +: 'int' and 'NoneType'

完整脚本：

def model_base_LSTM():

    model_cii = Sequential()

    # Make layers
    model_cii.add(CuDNNLSTM(50, return_sequences=True,input_shape=((1, 20))))
    model_cii.add(Dropout(0.4))

    model_cii.add(CuDNNLSTM(50, return_sequences=True))
    model_cii.add(Dropout(0.4))

    model_cii.add(CuDNNLSTM(50, return_sequences=True))
    model_cii.add(Dropout(0.4))

    model_cii.add(CuDNNLSTM(50, return_sequences=True))
    model_cii.add(Dropout(0.4))

    model_cii.add(Flatten())
    # Output layer
    model_cii.add(Dense(1))

    # Compile
    model_cii.compile(optimizer = 'adam', loss = 'mean_squared_error', metrics=['accuracy'])

    return model_cii

model = MyKerasRegressor(build_fn = model_base_LSTM, epochs=100, batch_size =70)
bagging_model = BaggingRegressor(base_estimator=model, n_estimators=10)
train_model = bagging_model.fit(x_train, y_train)

bagging_model.predict(x_test)

Output:
TypeError                                 Traceback (most recent call last)
<ipython-input-84-68d76cb73e8b> in <module>
----> 1 pred_bag = bagging_model.predict(x_test)
TypeError: unsupported operand type(s) for +: 'int' and 'NoneType'

Answer 1

model_base_LSTM()方法中存在错误。替换

return model

使用

return model_cii

修复“检查输入时出错”，可以像这样添加额外的维度。这也解决了scikit-learn(2维)与Keras LSTM(3维)的问题。创建KerasRegressor的子类来处理维度不匹配。

class MyKerasRegressor(KerasRegressor):
    def fit(self, x, y, **kwargs):
        x = np.expand_dims(x, -2)
        return super().fit(x, y, **kwargs)

    def predict(self, x, **kwargs):
        x = np.expand_dims(x, -2)
        return super().predict(x, **kwargs)

model = MyKerasRegressor(build_fn = model_base_LSTM, epochs=100, batch_size =70)