RNN and LSTM models have many architectures that can be modified. We can also compose their input and output data. However, in the examples that I found on the web, the inputs and outputs of RNNs/LSTMs are usually sequences.
Let's say we have a 3-column dataset:
data= np.array([[1.022 0.94 1.278]
[2.096 1.404 2.035]
[1.622 2.348 1.909]
[1.678 1.638 1.742]
[2.279 1.878 2.045]])
where the first two columns contain the inputs (features) and the third one contains the labels.
Usually, when modeling with feedforward neural networks (FFNNs), the input and output look like this:
Input:
x_input = np.vstack((data[:, 0], data[:, 1])).reshape(5, 2)
[[1.022 2.096]
[1.622 1.678]
[2.279 0.94 ]
[1.404 2.348]
[1.638 1.878]]
Output:
y_output = np.vstack((data[:, 2])).reshape(5, 1)
[[1.278]
[2.035]
[1.909]
[1.742]
[2.045]]
When modeling with RNN, the input and output are:
Input:
[[1.022 0.94 1.278]
[2.096 1.404 2.035]
[1.622 2.348 1.909]
Output (as a sequence):
[1.678 1.638 1.742]
[2.279 1.878 2.045]]
I would like to ask: Is it possible to model the input and output as an ANN model when modeling with RNN? Would it be correct?