While there are better ways to implement purely linear models, simplifying DNNs with a varying number of layers is where TensorFlow and learn really shine.
We'll use the same input features, but now we'll build a DNN with two hidden layers, first with 10 neurons and then 5. Creating this model will only take one line of Python code; it could not be easier.
The specification is similar to our linear model. We still need SKCompat, but now it's learn.DNNClassifier. For arguments, there's one additional requirement: the number of neurons on each hidden layer, passed as a list. This one simple argument, which really captures the essence of a DNN model, puts the power of deep learning at your fingertips.
There are some optional arguments to this as well, but we'll only mention optimizer. This allows you to choose between different common optimizer routines, such as Stochastic Gradient Descent (SGD) or Adam. Very convenient!
# Dense neural net classifier = estimator.SKCompat(learn.DNNClassifier...
