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I’m implementing an LSTM with Keras and I know that I have to reshape the training dataset in a 3D object. Basically I have a dataset of shape (300000, 839)and I reshape it as (100000, 3, 839) because I want a timestep of 3. The question is: how could I treat the training set labels? Have I to reshape also them? if I reshape the labels to 100000, I don't truncate 200000 labels, since the starting number of labels is 300000?
Thanks in advance.
Your approch so far is not exactly right. Assuming you want to use the many to one approcah, the way you do it is as follows:
You have a dataset like this:
And you prepare your data in a way like this:
For a timestep of 3, you discard the first 2 occurrences of your labels and use $y_{3}$ for the inputs $\{x_1, x_2,x_3\}$.
Next you don't start in $x_4$. Instead you use the next three-item slice $\{x_2, x_3, x_4\}$ with the training label $y_4$.
You continue this way until the last imput $\{x_{n-2}, x_{n-1}, x_{n}\}$ to predict $y_n$. If you have $n=30000$ samples, you can generate up to $n-2$ (29998) training samples for your model following this logic.
timesteps=3) I think that is not a problem for the learning. What do you think about it?
$\endgroup$
– pairon
Feb 28 '20 at 15:00
