Error when checking target: expected dense_1 to ha

Question

I'm training a model to predict the stock price and input data is close price. I use 45 days data to predict the 46th day's close price and a economic Indicator to be second feature, here is the model:

model = Sequential()
model.add( LSTM( 512, input_shape=(45, 2), return_sequences=True))
model.add( LSTM( 512, return_sequences=True))
model.add( (Dense(1)))
model.compile(loss='mse', optimizer='adam')
history = model.fit( X_train, y_train, batch_size = batchSize, epochs=epochs, shuffle = False)

When I run this I get the following error:

ValueError: Error when checking target: expected dense_1 to have 3 dimensions, but got array with shape (118, 1)

However, I print the shape of data and they are:

X_train:(118, 45, 2)
y_train:(118, 1)

I have no idea why the model is expecting a 3 dimensional output when y_train is (118, 1). Where am I wrong and what should I do?

Answer 1

I had a similar problem, found the answer here:

I added model.add(Flatten()) before the last Dense layer

Answer 2

The Shape of the training data should be in the format of:(num_samples,num_features,num_signals/num_vectors). Following this convention, try passing the training data in the form of an array with the reshaped size in convention described above, along with that ensure to add the validation_data argument in the model.fit command. An example of this is:

model.fit(x=X_input ,y=y_input,batch_size=2,validation_data=(X_output, y_output),epochs=10)

where both X_input, y_input are training data arrays with shape (126,711,1) and (126,1) respectively and X_output, y_output are validation/test data arrays with shapes (53,711,1) and (53,1) respectively.

In case you find a shuffling error try setting the value of shuffle argument as True after following the above methodology.

Answer 3

Your second LSTM layer also returns sequences and Dense layers by default apply the kernel to every timestep also producing a sequence:

# (bs, 45, 2)
model.add( LSTM( 512, input_shape=(45, 2), return_sequences=True))
# (bs, 45, 512)
model.add( LSTM( 512, return_sequences=True))
# (bs, 45, 512)
model.add( (Dense(1)))
# (bs, 45, 1)

So your output is shape (bs, 45, 1). To solve the problem you need to set return_sequences=False in your second LSTM layer which will compress sequence:

# (bs, 45, 2)
model.add( LSTM( 512, input_shape=(45, 2), return_sequences=True))
# (bs, 45, 512)
model.add( LSTM( 512, return_sequences=False)) # SET HERE
# (bs, 512)
model.add( (Dense(1)))
# (bs, 1)

And you'll get the desired output. Note bs is the batch size.