我需要对LSTM使用Bagging方法，对时间序列数据进行培训。我已经定义了模型库，并使用KerasRegressor链接到scikit-learn。但是具有AttributeError：'KerasRegressor'对象没有属性'loss'。我该如何解决？
更新：我使用了Manoj Mohan的方法（在第一个评论中），并且在拟合步骤中成功了。但是，当我将Manoj Mohan的类修改为
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)
它解决了与（.fit（））相同的predict（）的尺寸问题。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'
该model_base_LSTM()方法有错误。更换
return model
与
return model_cii
修复了“检查输入时出错”的问题，可以添加一个额外的尺寸。这也解决了scikit-learn（2维）与Keras LSTM（3维）问题。创建KerasRegressor的子类来处理尺寸不匹配。
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)
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