The model we used in the previous chapter is a simple fully connected network with three layers. Here's the section of
tftab.py that defines the layers:
model = tf.keras.models.Sequential([ tf.keras.layers.Dense(128, activation=tf.nn.relu), tf.keras.layers.Dense(128, activation=tf.nn.relu), tf.keras.layers.Dense(1) ])
Additionally, we did a lot of feature engineering in our Jupyter notebook. That will complicate our use of the inference endpoint. For example, rather than passing in
State = CO, we need to understand which column of the transformed data set contains the equivalent one-hot encoded variable.
In the rest of this chapter, we'll make the following improvements to our TensorFlow model: