Building a binary classifier in Keras

Now that we've defined our problem, our inputs, our desired output, and our cost function, we can quickly code the rest in Keras. The only thing we're missing is a network architecture. We will talk more about that soon. One of my favorite things about Keras is how easy it is tune the network architecture. As you're about to see, it might take a lot of experimentation before you locate the best architecture. If that's true, a framework that easily changes makes your job easier!

The input layer

As before, our input layer needs to know the dimensions of our dataset. I like to build the entire Keras model inside a function, and allow that function to pass back the compiled model. Right now, this function only takes a single argument, the number of features. The following code is used to define the input layer:

def build_network(input_features=None):
    # first we specify an input layer, with a shape == features
    inputs = Input(shape=(input_features,),...