def test_serialization():
optimizer = AdaBelief(lr=1e-3,
total_steps=10000,
warmup_proportion=0.1,
min_lr=1e-5,
rectify=False)
config = tf.keras.optimizers.serialize(optimizer)
new_optimizer = tf.keras.optimizers.deserialize(config)
assert new_optimizer.get_config() == optimizer.get_config()
def test_sparse_sample():
# Expected values are obtained from the previous implementation
run_sparse_sample(
iterations=200,
expected=[[0.0538936, 2.0], [3.0, 3.0538926]],
optimizer=AdaBelief(lr=1e-3, rectify=False),
)
def test_dense_sample():
# Expected values are obtained from the previous implementation
run_dense_sample(
iterations=100,
expected=[[0.66723955, 1.6672393], [2.6672382, 3.6672382]],
optimizer=AdaBelief(lr=1e-3, rectify=False),
)
def test_sparse_sample_with_weight_decay():
# Expected values are obtained from the previous implementation
run_sparse_sample(
iterations=200,
expected=[[0.05264655, 2.0], [3.0, 3.0466535]],
optimizer=AdaBelief(lr=1e-3, weight_decay=0.01, rectify=False),
)
def test_dense_sample_with_weight_decay():
# Expected values are obtained from the previous implementation
run_dense_sample(
iterations=100,
expected=[[0.66637343, 1.6653734], [2.6643748, 3.6633751]],
optimizer=AdaBelief(lr=1e-3, weight_decay=0.01, rectify=False),
)
def test_sparse_sample_with_amsgrad():
# Expected values are obtained from the official implementation
# `amsgrad` has no effect because the gradient is fixed
run_sparse_sample(
iterations=200,
expected=[[0.09575394, 2.0], [3.0, 3.0957537]],
optimizer=AdaBelief(lr=1e-3, amsgrad=True, rectify=False),
)
def test_dense_sample_with_amsgrad():
# Expected values are obtained from the official implementation
# `amsgrad` has no effect because the gradient is fixed
run_dense_sample(
iterations=100,
expected=[[0.67249274, 1.6724932], [2.6724923, 3.6724923]],
optimizer=AdaBelief(lr=1e-3, amsgrad=True, rectify=False),
)
def test_dense_sample_with_warmup():
run_dense_sample(
iterations=100,
expected=[[0.85635465, 1.8563547], [2.8563545, 3.8563545]],
optimizer=AdaBelief(lr=1e-3,
total_steps=100,
warmup_proportion=0.1,
min_lr=1e-5,
rectify=False),
)
def test_sparse_sample_with_warmup():
# Expected values are obtained from the previous implementation
run_sparse_sample(
iterations=200,
expected=[[0.8502214, 2.0], [3.0, 3.85022]],
optimizer=AdaBelief(lr=1e-3,
total_steps=100,
warmup_proportion=0.1,
min_lr=1e-5,
rectify=False),
)
def test_sparse_sample_with_lookahead():
# Expected values are obtained from the previous implementation
# of Ranger.
run_sparse_sample(
iterations=150,
expected=[[0.8114481, 2.0], [3.0, 3.8114486]],
optimizer=Lookahead(
AdaBelief(lr=1e-3, beta_1=0.95, rectify=False),
sync_period=6,
slow_step_size=0.45,
),
)
def test_dense_sample_with_lookahead():
# Expected values are obtained from the original implementation
# of Ranger
run_dense_sample(
iterations=100,
expected=[[0.88910455, 1.889104], [2.8891046, 3.8891046]],
optimizer=Lookahead(
AdaBelief(lr=1e-3, beta_1=0.95, rectify=False),
sync_period=6,
slow_step_size=0.45,
),
)
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 = AdaBelief(learning_rate=lr_scheduler,
weight_decay=wd_scheduler,
rectify=False)
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_schedulers():
lr_scheduler = tf.keras.optimizers.schedules.ExponentialDecay(
1e-3, 50, 0.5)
wd_scheduler = tf.keras.optimizers.schedules.InverseTimeDecay(
2e-3, 25, 0.25)
run_dense_sample(
iterations=100,
expected=[[0.84216374, 1.8420818], [2.8420012, 3.841918]],
optimizer=AdaBelief(learning_rate=lr_scheduler,
weight_decay=wd_scheduler,
rectify=False),
)
def test_checkpoint_serialization(tmpdir):
optimizer = AdaBelief()
optimizer2 = AdaBelief()
var_0 = tf.Variable([1.0, 2.0], dtype=tf.dtypes.float32)
var_1 = tf.Variable([3.0, 4.0], dtype=tf.dtypes.float32)
grad_0 = tf.constant([0.1, 0.2], dtype=tf.dtypes.float32)
grad_1 = tf.constant([0.03, 0.04], dtype=tf.dtypes.float32)
grads_and_vars = list(zip([grad_0, grad_1], [var_0, var_1]))
optimizer.apply_gradients(grads_and_vars)
checkpoint = tf.train.Checkpoint(optimizer=optimizer)
checkpoint2 = tf.train.Checkpoint(optimizer=optimizer2)
model_path = str(tmpdir / "adabelief_chkpt")
checkpoint.write(model_path)
checkpoint2.read(model_path)
optimizer2.apply_gradients(grads_and_vars)
def test_get_config():
opt = AdaBelief(lr=1e-4)
config = opt.get_config()
assert config["learning_rate"] == 1e-4
assert config["total_steps"] == 0
def test_sparse_sample_with_rectify():
run_sparse_sample(
iterations=200,
expected=[[0.7836679, 2.0], [3.0, 3.7839665]],
optimizer=AdaBelief(lr=1e-3, rectify=True),
)
