But I'd like to add a couple
of new layers to this, layers that use the Lambda type. This type of layer is one
that allows us to perform arbitrary operations
to effectively expand the functionality
of TensorFlow's kares, and we can do this within
the model definition itself. So the first Lambda layer will be used to help us with
our dimensionality. If you recall when we wrote the window dataset
helper function, it returned
two-dimensional batches of Windows on the data, with the first being
the batch size and the second the number
of timestamps. But an RNN expects
three-dimensions; batch size, the number
of timestamps, and the series dimensionality. With the Lambda layer, we can fix this without rewriting our Window
dataset helper function. Using the Lambda, we just expand the array
by one dimension. By setting input shape to none, we're saying that
the model can take sequences of any length. Similarly, if we scale
up the outputs by 100, we can help training. The default activation
function in the RNN layers is tan H which is the hyperbolic
tangent activation. This outputs values between
negative one and one. Since the time
series values are in that order usually in
the 10s like 40s, 50s, 60s, and 70s, then scaling up the outputs to the same ballpark can help us with learning. We can do that in
a Lambda layer too, we just simply multiply
that by a 100. So let's now take a look at
what it takes to build out the full RNN so we can start doing
some predictions with it. You'll see that in
the next video.