Hands-On Deep Learning with TensorFlow

DNNs

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 ...