Htydjtk

I have an array X_train = (1110,25,2) and a y_train = (1110,5,2). It means I use arrays with length of 25 for inputs and length of 5 for labels. But when I use:

model = Sequential()

model.add(LSTM(units = 25, return_sequences = True, input_shape = (25, 2))) 

model.add(Dropout(0.2))

model.add(Dense(units = 2))

model.compile(optimizer = 'adam', loss = 'mean_squared_error')

model.fit(X_train, y_train, epochs = 100 , batch_size = 25)

It gives me this error in the last line of the code:

ValueError: Error when checking target: expected dense_1 to have 2
dimensions, but got array with shape (1110, 5, 2) [Finished in 5.1s
with exit code 1]

The code works if I change the length of y_train to the 1, but I like to test longer y labels to train. What is the problem and how can I fix it?

EDIT:
I create X_train and y_train arrays with this code:

for i in range((len(training_set)%30) + 30 , len(training_set) - days ):

    X_train.append(training_set_scaled[i-30:i-5])

    y_train.append(training_set_scaled[i-5:i])

X_train, y_train = np.array(X_train), np.array(y_train)

This is the result of model.summary()

model.summary()

_________________________________________________________________

Layer (type)                 Output Shape              Param #   

=================================================================

lstm_45 (LSTM)               (None, 25, 25)            2800      

_________________________________________________________________

dropout_45 (Dropout)         (None, 25, 25)            0         

_________________________________________________________________

lstm_46 (LSTM)               (None, 25, 25)            5100      

_________________________________________________________________

dropout_46 (Dropout)         (None, 25, 25)            0         

_________________________________________________________________

lstm_47 (LSTM)               (None, 25, 25)            5100      

_________________________________________________________________

dropout_47 (Dropout)         (None, 25, 25)            0         

_________________________________________________________________

lstm_48 (LSTM)               (None, 25)                5100      

_________________________________________________________________

dropout_48 (Dropout)         (None, 25)                0         

_________________________________________________________________

dense_12 (Dense)             (None, 2)                 52        

=================================================================

Total params: 18,152

Trainable params: 18,152

Non-trainable params: 0

_________________________________________________________________

EDIT2:
I have tried to solve my problem with RepeatVector() function in encoder-decoder approach with the following code:

model = Sequential()

model.add(LSTM(units = 25, return_sequences = True, input_shape = (25, 2))) 

model.add(Dropout(0.2))

model.add(LSTM(units = 25, return_sequences = True))

model.add(Dropout(0.2))

model.add(LSTM(units = 25)) #, return_sequences = True))

model.add(Dropout(0.2))

model.add(RepeatVector(5))

model.add(LSTM(units = 5 ,return_sequences = True)) 

model.add(Dropout(0.2))

model.add(LSTM(units = 5 ,return_sequences = True )) 

model.add(Dropout(0.2))

model.add(Dense(units = 2))

But I get this stupid result:

Can you post a model summary using:

model.summary()

Also, elaborate on how exactly the Y_train dataset works with the X_train? It's not clear how the 25 time steps from X_train data correspond to the Y_train 5 outputs.