def test_serialization():
optimizer = RectifiedAdam(
lr=1e-3, total_steps=10000, warmup_proportion=0.1, min_lr=1e-5,
)
config = tf.keras.optimizers.serialize(optimizer)
new_optimizer = tf.keras.optimizers.deserialize(config)
assert new_optimizer.get_config() == optimizer.get_config()
def test_scheduler_serialization():
lr_scheduler = tf.keras.optimizers.schedules.ExponentialDecay(
1e-3, 50, 0.5)
wd_scheduler = tf.keras.optimizers.schedules.InverseTimeDecay(
2e-3, 25, 0.25)
optimizer = RectifiedAdam(learning_rate=lr_scheduler,
weight_decay=wd_scheduler)
config = tf.keras.optimizers.serialize(optimizer)
new_optimizer = tf.keras.optimizers.deserialize(config)
assert new_optimizer.get_config() == optimizer.get_config()
assert new_optimizer.get_config()["learning_rate"] == {
"class_name": "ExponentialDecay",
"config": lr_scheduler.get_config(),
}
assert new_optimizer.get_config()["weight_decay"] == {
"class_name": "InverseTimeDecay",
"config": wd_scheduler.get_config(),
}
def test_get_config(self):
opt = RectifiedAdam(lr=1e-4)
config = opt.get_config()
self.assertEqual(config["learning_rate"], 1e-4)
self.assertEqual(config["total_steps"], 0)
def test_get_config():
opt = RectifiedAdam(lr=1e-4)
config = opt.get_config()
assert config["learning_rate"] == 1e-4
assert config["total_steps"] == 0
