Building an MLP in Keras
Keras uses an instance of a model object to contain a neural network. For those of you familiar with scikit-learn, this is probably quite familiar. What is somewhat different is that Keras models contain a set of layers. This set of layers needs to be defined by us. This allows for amazing flexibility in network architecture, with very little code.
Note
Keras currently has two APIs for building models. In my examples, I'll be using the functional API. It's slightly more verbose but it allows additional flexibility. I'd recommend using the functional API whenever possible.
Our MLP will need an input layer, a hidden layer, and an output layer.
Input layer shape
Since we've already identified our inputs, we know that the input matrix will have a number of rows equal to the number of data elements/observations in our dataset and a number of columns equal to the number of variables/features. The shape of the input matrix then is (number of observations x 10 features). ...
