In this video, we'll be looking at yet another way to build a neural network.
This time our goal will be to combine the two types of models we just learned how to build, specifically
the multi input time series CNN and the feature based CNN by using concatenation.
You'll see how easy it is to combine all different kinds of data sources into one big neural network.
So we'll start by creating two lists for the inputs and features.
This part is the same as the multi input enen where we use a separate mini neural network for each dimension
of the Time series.
The new part is the second part where we create a new input and a new mini neural network to process
the features, so we call these Eita.
Next to.
The next step is to concatenate all of these features together, so previously this was just the features
list.
But now we have one additional item, which is next to now.
People always ask about this syntax.
Remember that in Python, a list plus a list just puts the two lists together.
The next step is to pass this big feature vector into one final dense layer.
The last line of code is where we create our model object, passing in a list of all the inputs and
the output x.
The next step is to call the compile function the same as before.
The next step is to create a new checkpoint.
Again, we use a separate file name.
The next step is to call the Fed function, notice again that our inputs are now lists of inputs, so
we simply concatenate the static features with what we had before.
The next step is to plot the Lansberry Park.
OK, so nothing out of the ordinary.
The next step is to plot the accuracy prepack.
So it looks like this model may perform a bit better than what we had before.
The next step is to load up our best model and check its performance.
OK, so we see that our combine's model does a bit better than the individual models.
However, it still does not perform as well as a linear model that's learned only from the static features.