Looking at the decision boundary a classifier generates can give us some geometric intuition about the decision rule a classifier uses and how this decision rule changes as the classifier is trained on more data.
|import tensorflow as tf|
|import tensorflow.experimental.numpy as tnp|
|from absl import logging|
|from typing import Callable, Dict, Union, Optional, Iterable, Sequence|
|from tensorflow import keras|
|from tensorflow.keras.optimizers.schedules import LearningRateSchedule|
|# Schedules ported from Optax|