def test_exclude_weight_decay_sgdw():
optimizer = weight_decay_optimizers.SGDW(
learning_rate=0.01, weight_decay=1e-4, exclude_from_weight_decay=["var1"]
)
assert optimizer._do_use_weight_decay(tf.Variable([], name="var0"))
assert not optimizer._do_use_weight_decay(tf.Variable([], name="var1"))
assert not optimizer._do_use_weight_decay(tf.Variable([], name="var1_weight"))
def test_weight_decay_with_piecewise_constant_decay_schedule():
model = tf.keras.models.Sequential([tf.keras.layers.Dense(2)])
loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
wd_schedule = tf.optimizers.schedules.PiecewiseConstantDecay([2],
[1e-4, 1e-5])
optimizer = weight_decay_optimizers.SGDW(learning_rate=1e-2,
weight_decay=wd_schedule)
model.compile(optimizer=optimizer, loss=loss, metrics=["accuracy"])
x, y = np.random.uniform(size=(2, 4, 1))
model.fit(x, y, batch_size=1, epochs=1)
def test_exclude_weight_decay_sgdw():
optimizer = weight_decay_optimizers.SGDW(
learning_rate=0.01,
weight_decay=1e-4,
exclude_from_weight_decay=["var1"])
var0 = tf.Variable([], name="var0")
var1 = tf.Variable([], name="var1")
var1_weight = tf.Variable([], name="var1_weight")
optimizer._set_decay_var_list([var0, var1, var1_weight])
assert optimizer._do_use_weight_decay(var0)
assert not optimizer._do_use_weight_decay(var1)
assert not optimizer._do_use_weight_decay(var1_weight)