The functional API of Keras makes it easy to design architectures with multiple inputs and outputs. This example illustrates a network with three inputs, as follows:
- A two stacked LSTM layers with 25 and 10 units respectively
- An embedding layer that learns a 10-dimensional real-valued representation of the equities
- A one-hot encoded representation of the month
We begin by defining the three inputs with their respective shapes, as described here:
returns = Input(shape=(window_size, n_features), name='Returns')tickers = Input(shape=(1,), name='Tickers')months = Input(shape=(12,), name='Months')
To define stacked LSTM layers, we set the return_sequences keyword to True. This ensures that the first layer produces ...
