keras LSTM 输入和 output 形状如何工作？

Question

        trainX, trainY, sequence_length=len(train), batch_size=batchTrain
    )
    val=timeseries_dataset_from_array(
        valX, valY, sequence_length=len(val), batch_size=batchVal
    )    
    test=timeseries_dataset_from_array(
        testX, testY, sequence_length=len(test), batch_size=batchTest
    )
    
    return train, val, test

train, val, test = preprocessor()

model=Sequential()
model.add(LSTM(4,return_sequences=True))
model.add(Dense(2,activation='softmax'))
model.compile(optimizer='Adam', loss="mae")
model.fit(train, epochs=200, verbose=2, validation_data=val, shuffle=False)

I'm trying to make an LSTM from time-series data and when I run the above, the loss doesn't change at all. I'm definitely struggling to understand how lstm input/output shapes work. I've read as much online as I could find, but I can't seem to get the model to learn. I'm under the impression that the first argument is the dimensionality of the output space. I want the lstm to return the whole sequence to the output function.

Answer 1

There are many problems in your model. You final layer is dense with two units and you are using softmax which should be replaced by sigmoid . Since you are using softmax , i guess that you are using this model for classification and not regression.

If you are using a model for classification tasks then you should use BinaryCrossentropy and not MeanAbsoluteError as loss.

To answer the question in full detail, you need to post the additional information. For example: What are you target variables etc.