Converting a full model to a reduced float16 model

In this section, we are going to load the model we just trained and quantize it into a reduced float16 model. For the convenience of step-by-step explanations and your learning experience, it is recommended that you use JupyterLab or Jupyter Notebook to follow along with the explanation here:

1. Let's start by loading the trained model:

   import tensorflow as tf
   import pathlib
   import os
   import numpy as np
   from matplotlib.pyplot import imshow
   import matplotlib.pyplot as plt
   root_dir = '../train_base_model'
   model_dir = ' trained_resnet_vector-unquantized/save_model'
   saved_model_dir = os.path.join(root_dir, model_dir)
   trained_model = tf.saved_model.load(saved_model_dir)

   The tf.saved_model.load API helps us to load the saved model we built and trained.

2. Then we will create a converter object to refer to the savedModel directory with the following line of code:

   converter = tf.lite.TFLiteConverter.from_saved_model...