This is my first question, suggestions are appreciated!
I built a model similar to this one.
I am using
model.predict(inputs) to make predictions and I built my custom functions and process to make it learn. It seems to work and when it predicts all seems good.
Some days ago, I watched this video:
(It starts in the part that generated my question) And I copied the image that caught my attention:
It says that each input is inserted sequentially on each cell, and each cell will provide more information to the next in that order.
I began to wonder if my implementation is correct and I will give you all the details that might be useful.
- I am using
stateful=True for both GRU layers.
- The input at
model.predict(inputs) is defined like
inputs = [np.array(arr).reshape(1, 1, 6), np.array(arr2).reshape(1, 1, 4)].
- I have a CSV with the values of the first part of the input, but for the other values, I create them on the fly. That is why I used
model.predict. Because of this, if there is another way to be able to send both the static and "dynamic data", I would be happy to do it.
- The only way that I am interacting with the model after loading it is by asking to predict the values, and by updating its weights (and resetting states when I input a new sequence).
Now, with all this information, the question would be:
- Is model. predict updating all the cell states correctly and using the GRU layer to its full capacity?
- Or is it just sending the data to the first cell always?