def optimizer(self):
return optimizers.Bop(
fp_optimizer=tf.keras.optimizers.Adam(0.01),
threshold=self.threshold,
gamma=tf.keras.optimizers.schedules.ExponentialDecay(
self.gamma, self.decay_step, self.gamma_decay, staircase=True),
)
def optimizer(self):
decay_step = self.epochs * 1281167 // self.batch_size
lr = tf.keras.optimizers.schedules.PolynomialDecay(
self.lr_start, decay_step, end_learning_rate=self.lr_end, power=1.0
)
gamma = tf.keras.optimizers.schedules.PolynomialDecay(
self.gamma_start, decay_step, end_learning_rate=self.gamma_end, power=1.0
)
'''
return optimizers.Bop(
tf.keras.optimizers.Adam(lr), threshold=self.threshold, gamma=gamma
)
'''
return lq.optimizers.CaseOptimizer(
(optimizers.Bop.is_binary_variable,
optimizers.Bop(
threshold=self.threshold,
gamma=gamma,
name="Bop"
)
),
default_optimizer=tf.keras.optimizers.Adam(lr), # for FP weights
)
def optimizer(self):
return lq.optimizers.CaseOptimizer(
(optimizers.Bop.is_binary_variable,
optimizers.Bop(
threshold=self.threshold, gamma=self.gamma, name="Bop")),
default_optimizer=tf.keras.optimizers.Adam(
self.lr), # for FP weights
)
def optimizer(self):
decay_step = 100 * 1281167 // self.batch_size
lr = tf.keras.optimizers.schedules.PolynomialDecay(
2.5e-3, decay_step, end_learning_rate=2.5e-6, power=1.0)
gamma = tf.keras.optimizers.schedules.PolynomialDecay(
5e-4, decay_step, end_learning_rate=2.5e-6, power=1.0)
return optimizers.Bop(tf.keras.optimizers.Adam(lr),
threshold=1e-7,
gamma=gamma)
def optimizer(self):
decay_step = self.epochs * 1281167 // self.batch_size
lr = tf.keras.optimizers.schedules.PolynomialDecay(
self.lr_start, decay_step, end_learning_rate=self.lr_end, power=1.0
)
gamma = tf.keras.optimizers.schedules.PolynomialDecay(
self.gamma_start, decay_step, end_learning_rate=self.gamma_end, power=1.0
)
return optimizers.Bop(
tf.keras.optimizers.Adam(lr), threshold=self.threshold, gamma=gamma
)
def optimizer(self):
return lq.optimizers.CaseOptimizer(
(optimizers.Bop.is_binary_variable,
optimizers.Bop(
threshold=self.threshold,
gamma=tf.keras.optimizers.schedules.ExponentialDecay(
self.gamma,
self.decay_step,
self.gamma_decay,
staircase=True),
name="Bop")),
default_optimizer=tf.keras.optimizers.Adam(
self.lr), # for FP weights
)
def optimizer(self):
return optimizers.Bop(
fp_optimizer=tf.keras.optimizers.Adam(0.01),
threshold=self.threshold,
gamma=self.gamma,
)