model3 | simple test program and board support package

Took a step back and it was easy to solve it, kept overthinking it. Instead of reading the file as a list I just read the file as a dict and the solution kinda presented itself. Can obviously be done nicer but this works in my case.

Put the models in a list and use sapply/lapply to extract the values.

Needs to be Op.or instead of Op.Or Thank you @anatoly for noticing!

The intermediate layers do not have to output 3 categories (as in model #2). There's no "correct way" in this case it's just two different way.

In the case of model #2, the added layer is compressing the information of each channel into 3 cells. It could be interpreted as if each channel was asked to do their own prediction and the last one rely on the intermediate predictions to output the final class.

Whereas in the case of model #1, the final layer has much more connections with the channels layers but the overall model is less deeper.

But none of this can tell you in advance which one is the best in your case.

You should be able to solve this with a double forEach loop

You can use map2 function from purrr package and also mapply from base R since we are iterating over two variables at the same time:

If you declare your @step variable as data type XML instead of varchar, you can use XQuery to process the data, in this case a FLOWR expression in curly braces within literal XML elements, the curly braces switch from literal mode to XPath mode:

As far as I can tell we can't just pass all variable-size inputs together in fit for the multi-input model. The way you pass your training pairs to the model, it surely unable to unpack for concern input layers. The related post that you mentioned is also an important fact to consider.

However, in tensorflow, we can use tf.ragged.RaggedTensor for variable-length input sequence, discussed here. But not sure if there is any workaround possible by converting to the ragged tensor. It probably would possible if a single input layer takes a different length of the input sequence.

If you read the fit method's training pairs input you would see keras expects the x paramer as follows:

Check my solution on this fiddle. If you can, then pass only model name like in the fiddle, otherwise you will need additional parsing. Of course you need here additional check when to append minus sign if already exists.