CODEWITHSUNDEEP
keraslstm
ShanN
ShanN

Reputation: 943

How can I fix TypeError: unsupported operand type(s) for +: 'int' and 'NoneType' when process Bagging method

I need to use Bagging method for LSTM, training on Time-Series data. I have defined the model base and use the KerasRegressor to link to scikit-learn. But have AttributeError: 'KerasRegressor' object has no attribute 'loss'. How can I fix it?

Update: I have used the method of Manoj Mohan (at the first comment) and successful at the fit step. However, the problem comes as TypeError when I modify the class of Manoj Mohan to

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)

It has solved the dimension problem of predict() which the same as the .fit(). The problem is:

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'

Full script:

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'

Upvotes: 1

Views: 574

Answers (1)

Manoj Mohan
Manoj Mohan

Reputation: 6044

There is an error in the model_base_LSTM() method. Replace

return model

with 

return model_cii

Fix for "Error when checking input", an extra dimension could be added like this. This also takes care of scikit-learn(2 dimensions) vs Keras LSTM(3 dimensions) problem. Create a subclass of KerasRegressor to handle the dimension mismatch. 

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)

Upvotes: 1

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