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, )