Multiclass classification problems

In this exercise, we expand beyond binary classification to cover multiclass problems. A multiclass problem has targets that can take on three or more values. In the credit card dataset, the education variable can take on 6 different values, each corresponding to a different level of education. We will use that as our target in this exercise and will also expand the feature set from 3 to 10 columns.

As in the previous problem, you will define an input layer, dense layers, and an output layer. You will also print the untrained model's predictions, which are probabilities assigned to the classes. The tensor of features has been loaded and is available as borrower_features. Additionally, the constant(), float32, and keras.layers.Dense() operations are available.

Define the input layer as a 32-bit constant tensor using borrower_features.
Set the first dense layer to have 10 output nodes and a sigmoid activation function.
Set the second dense layer to have 8 output nodes and a rectified linear unit activation function.
Set the output layer to have 6 output nodes and the appropriate activation function.

Exercise

Multiclass classification problems

Instructions