def optimizer(self):
lr = tf.get_variable('learning_rate', initializer=5e-4, trainable=False)
opt = tf.train.AdamOptimizer(lr, epsilon=1e-3)
return optimizer.apply_grad_processors(
opt, [
gradproc.ScaleGradient(('STN.*', 0.1)),
gradproc.SummaryGradient()])
def optimizer(self):
lr = tf.get_variable('learning_rate',
initializer=self.learning_rate,
trainable=False)
opt = tf.train.AdamOptimizer(lr, epsilon=1e-3)
return optimizer.apply_grad_processors(
opt, [gradproc.GlobalNormClip(10),
gradproc.SummaryGradient()])
def optimizer(self):
lr = tf.get_variable('learning_rate',
initializer=3e-5,
trainable=False)
opt = tf.train.AdamOptimizer(lr, epsilon=1e-3)
return optimizer.apply_grad_processors(opt, [
gradproc.ScaleGradient([('convfcweight.*', 0.1), ('conv5_.*', 5)])
])
def optimizer(self):
opt = get_optimizer(self.optimizer_config)
if self.quantizer_config['name'] == 'linear' and eval(self.quantizer_config['W_opts']['stop_grad']):
self.add_stop_grad()
opt = optimizer.apply_grad_processors(opt, [gradproc.MapGradient(self.stop_grad)])
if self.quantizer_config['name'] == 'linear' and eval(self.quantizer_config['W_opts']['centralized']):
self.add_centralizing_update()
opt = optimizer.PostProcessOptimizer(opt, self.centralizing)
if self.quantizer_config['name'] == 'cent':
self.add_centralizing_update()
opt = optimizer.PostProcessOptimizer(opt, self.centralizing)
if self.quantizer_config['name'] == 'cluster' and eval(self.load_config['clustering']):
opt = optimizer.apply_grad_processors(opt, [gradproc.MapGradient(self.clustering)])
if self.quantizer_config['name'] == 'linear' and eval(self.quantizer_config['W_opts']['pruning']):
self.add_masking_update()
opt = optimizer.PostProcessOptimizer(opt, self.masking)
return opt
def optimizer(self):
lr = tf.get_variable("learning_rate",
initializer=0.0002,
trainable=False)
tf.summary.scalar("learning_rate", lr)
opt = tf.train.AdamOptimizer(lr, beta1=0.5, beta2=0.999)
return optimizer.apply_grad_processors(
opt, [gradproc.SummaryGradient(),
gradproc.CheckGradient()])
def optimizer(self):
lr = tf.get_variable('learning_rate',
initializer=5e-4,
trainable=False)
opt = tf.train.AdamOptimizer(lr, epsilon=1e-3)
return optimizer.apply_grad_processors(opt, [
gradproc.ScaleGradient(('STN.*', 0.1)),
gradproc.SummaryGradient()
])
def optimizer(self):
lr = tf.get_variable('learning_rate', initializer=self.learning_rate, trainable=False)
# opt = tf.train.AdamOptimizer(lr, epsilon=1e-3)
opt = tf.train.AdamOptimizer(lr)
return optimizer.apply_grad_processors(
opt, [
# gradproc.GlobalNormClip(2.0),
gradproc.MapGradient(lambda grad: tf.clip_by_average_norm(grad, 0.5)),
gradproc.SummaryGradient()])
def _get_optimizer(self):
conf = Config()
lr = tf.get_variable('learning_rate',
initializer=conf.learning_rate,
trainable=False)
opt = tf.train.AdamOptimizer(lr)
tf.summary.scalar('learning_rate', lr)
return optimizer.apply_grad_processors(
opt, [gradproc.GlobalNormClip(conf.max_grad_norm)])
def _get_optimizer(self):
lr = tf.get_variable('learning_rate',
initializer=2e-4,
dtype=tf.float32,
trainable=False)
opt = tf.train.AdamOptimizer(lr, beta1=0.5, epsilon=1e-6)
# generator learns 5 times faster
return optimizer.apply_grad_processors(
opt, [gradproc.ScaleGradient(('gen/.*', 5))])
def _get_optimizer(self):
lr = tf.get_variable('learning_rate', initializer=1e-4, trainable=True)
tf.summary.scalar('lr', lr)
opt = tf.train.AdamOptimizer(lr)
if self.stage == 1:
return opt
else:
# Only apply Global Norm Clipping to gradients of ConvLSTM
return apply_grad_processors(opt, [FilteredGlobalNormClip(3, 'ConvLSTM')])
def optimizer(self):
lr = tf.get_variable('learning_rate',
initializer=1e-3,
trainable=False)
opt = tf.train.AdamOptimizer(lr)
# gradprocs = [MapGradient(lambda grad: tf.clip_by_average_norm(grad, 0.5)),
# SummaryGradient()]
gradprocs = [MapGradient(lambda grad: grad), SummaryGradient()]
opt = optimizer.apply_grad_processors(opt, gradprocs)
return opt
def optimizer(self):
lr = tf.get_variable('a3c_learning_rate',
initializer=1e-4,
trainable=False)
opt = tf.train.AdamOptimizer(lr)
gradprocs = [
MapGradient(lambda grad: tf.clip_by_average_norm(grad, 0.3))
]
opt = optimizer.apply_grad_processors(opt, gradprocs)
return opt
def optimizer(self):
lr = tf.train.exponential_decay(self.base_lr,
global_step=get_global_step_var(),
decay_steps=self.decay_steps,
decay_rate=self.decay_rate,
name='learning-rate')
opt = tf.train.RMSPropOptimizer(learning_rate=lr)
tf.summary.scalar('lr', lr)
return optimizer.apply_grad_processors(opt,
[gradproc.SummaryGradient()])
def optimizer(self):
lr = tf.get_variable('learning_rate',
initializer=1e-3,
trainable=False)
tf.summary.scalar("learning_rate", lr)
opt = tf.train.RMSPropOptimizer(lr,
decay=0.95,
momentum=0.95,
epsilon=1e-2)
return optimizer.apply_grad_processors(opt,
[gradproc.SummaryGradient()])
def optimizer(self):
lr = tf.get_variable('learning_rate', initializer=1e-3, trainable=False)
# This will also put the summary in tensorboard, stat.json and print in terminal,
# but this time without moving average
tf.summary.scalar('lr', lr)
# opt = tf.train.MomentumOptimizer(lr, 0.9)
opt = tf.train.AdamOptimizer(lr)
return optimizer.apply_grad_processors(
opt, [gradproc.MapGradient(lambda grad: tf.clip_by_average_norm(grad, 0.5)),
gradproc.SummaryGradient()])
def _get_optimizer(self):
gradprocs = [
FilterGradientVariables('.*net2.*', verbose=False),
gradproc.MapGradient(
lambda grad: tf.clip_by_value(grad, hp.train2.clip_value_min, hp.train2.clip_value_max)),
gradproc.GlobalNormClip(hp.train2.clip_norm),
# gradproc.PrintGradient(),
# gradproc.CheckGradient(),
]
lr = tf.get_variable('learning_rate', initializer=hp.train2.lr, trainable=False)
opt = tf.train.AdamOptimizer(learning_rate=lr)
return optimizer.apply_grad_processors(opt, gradprocs)
def optimizer(self):
lr = tf.get_variable('learning_rate',
initializer=args.base_lr,
trainable=False)
opt = tf.train.MomentumOptimizer(lr, 0.9, use_nesterov=True)
add_moving_summary(lr)
if args.load:
gradprocs = [
gradproc.ScaleGradient([('conv.*', 0.1), ('fc.*', 0.1)])
]
return optimizer.apply_grad_processors(opt, gradprocs)
else:
return opt
def _get_optimizer(self):
lr = symbf.get_scalar_var('learning_rate', 0.003, summary=True)
factor = get_batch_factor()
if factor != 1:
lr = lr / float(factor)
opt = tf.train.MomentumOptimizer(lr, 0.9)
opt = optimizer.AccumGradOptimizer(opt, factor)
else:
opt = tf.train.MomentumOptimizer(lr, 0.9)
return opt
return optimizer.apply_grad_processors(
opt, [gradproc.ScaleGradient(('.*/b', 2))])
def _get_opt(name, init_lr):
lr = symbf.get_scalar_var('learning_rate/'+name, init_lr, summary=True)
opt = tf.train.AdamOptimizer(lr)
logger.info("create opt {}".format(name))
gradprocs = [
# MapGradient(lambda grad: tf.Print(grad, [grad], 'grad {}='.format(grad.op.name), summarize=4)),
MapGradient(lambda grad: tf.clip_by_average_norm(grad, 0.1), regex='^actor/.*'),
MapGradient(lambda grad: tf.clip_by_average_norm(grad, 0.05), regex='^critic/.*'),
# GlobalNormClip(40.),
SummaryGradient(),
]
opt = optimizer.apply_grad_processors(opt, gradprocs)
return opt
def _get_optimizer(self):
gradprocs = [
tensorpack_extension.FilterGradientVariables('.*net2.*', verbose=False),
gradproc.MapGradient(
lambda grad: tf.clip_by_value(grad, hp.train2.clip_value_min, hp.train2.clip_value_max)),
gradproc.GlobalNormClip(hp.train2.clip_norm),
# gradproc.PrintGradient(),
# gradproc.CheckGradient(),
]
global_step = tf.Variable(0, name='global_step',trainable=False)
#self.lr = self.learning_rate_decay(global_step, hp.train2.lr)
#lr = learning_rate_decay(initial_lr = hp.train2.lr, global_step)
lr = tf.get_variable('learning_rate', initializer=hp.train2.lr, trainable=False)
opt = tf.train.AdamOptimizer(learning_rate=lr)
return optimizer.apply_grad_processors(opt, gradprocs)
def _get_optimizer(self):
lr = get_scalar_var('learning_rate', cfg.base_lr, summary=True)
opt = tf.train.MomentumOptimizer(learning_rate=lr,
momentum=cfg.momentum)
gradprocs = [
gradproc.ScaleGradient([('conv.*/W', 1),
('conv.*/b', cfg.bias_lr_mult),
('bottleneck.*/W', 1),
('bottleneck.*/b', cfg.bias_lr_mult),
('stage_1.*/W', 1),
('stage_1.*/b', cfg.bias_lr_mult),
('stage_[2-6].*/W', cfg.lr_mult),
('stage_[2-6].*/b',
cfg.lr_mult * cfg.bias_lr_mult)])
]
return optimizer.apply_grad_processors(opt, gradprocs)
def _get_optimizer(self):
lr = tf.get_variable('learning_rate',
initializer=0.003,
trainable=False)
tf.summary.scalar('learning_rate', lr)
factor = get_batch_factor()
if factor != 1:
lr = lr / float(factor)
opt = tf.train.MomentumOptimizer(lr, 0.9)
opt = optimizer.AccumGradOptimizer(opt, factor)
else:
opt = tf.train.MomentumOptimizer(lr, 0.9)
return opt
return optimizer.apply_grad_processors(
opt, [gradproc.ScaleGradient(('.*/b', 2))])
def _get_opt(name, init_lr):
lr = symbf.get_scalar_var('learning_rate/' + name,
init_lr,
summary=True)
opt = tf.train.AdamOptimizer(lr)
logger.info("create opt {}".format(name))
gradprocs = [
# MapGradient(lambda grad: tf.Print(grad, [grad], 'grad {}='.format(grad.op.name), summarize=4)),
MapGradient(lambda grad: tf.clip_by_average_norm(grad, 0.1),
regex='^actor/.*'),
MapGradient(lambda grad: tf.clip_by_average_norm(grad, 0.05),
regex='^critic/.*'),
# GlobalNormClip(40.),
SummaryGradient(),
]
opt = optimizer.apply_grad_processors(opt, gradprocs)
return opt
def _get_optimizer(self, name):
from tensorpack.tfutils import optimizer
from tensorpack.tfutils.gradproc import SummaryGradient, GlobalNormClip, MapGradient
init_lr = INIT_LEARNING_RATE_A if name == 'actor' else INIT_LEARNING_RATE_C
import tensorpack.tfutils.symbolic_functions as symbf
lr = symbf.get_scalar_var('learning_rate/' + name,
init_lr,
summary=True)
opt = tf.train.AdamOptimizer(lr)
logger.info("create opt {}".format(name))
gradprocs = [
# MapGradient(lambda grad: tf.Print(grad, [grad], 'grad {}='.format(grad.op.name), summarize=4)),
MapGradient(lambda grad: tf.clip_by_average_norm(grad, 0.1),
regex='^actor/.*'),
MapGradient(lambda grad: tf.clip_by_average_norm(grad, 0.05),
regex='^critic/.*'),
# GlobalNormClip(40.),
SummaryGradient(),
]
opt = optimizer.apply_grad_processors(opt, gradprocs)
return opt
def optimizer(self):
lr = tf.get_variable('learning_rate', initializer=2e-4, dtype=tf.float32, trainable=False)
opt = tf.train.AdamOptimizer(lr, beta1=0.5, epsilon=1e-6)
# generator learns 5 times faster
return optimizer.apply_grad_processors(
opt, [gradproc.ScaleGradient(('gen/.*', 5))])
def _get_optimizer(self):
lr = tf.get_variable('learning_rate', initializer=1e-3, trainable=False)
opt = tf.train.AdamOptimizer(lr, epsilon=1e-3)
return optimizer.apply_grad_processors(
opt, [gradproc.GlobalNormClip(10), gradproc.SummaryGradient()])
def _get_optimizer(self):
lr = symbf.get_scalar_var('learning_rate', 2e-4, summary=True)
opt = tf.train.AdamOptimizer(lr, beta1=0.5, epsilon=1e-6)
# generator learns 5 times faster
return optimizer.apply_grad_processors(
opt, [gradproc.ScaleGradient(('gen/.*', 5))])
def optimizer(self):
lr = tf.get_variable("learning_rate",
initializer=self.lr,
trainable=False)
opt = tf.train.AdamOptimizer(lr)
return optimizer.apply_grad_processors(opt, [GlobalNormClip(5)])
def _get_optimizer(self):
lr = tf.get_variable('learning_rate', initializer=0.1, trainable=False)
opt = tf.train.MomentumOptimizer(lr, 0.9, use_nesterov=True)
gradprocs = [gradproc.ScaleGradient(
[('conv0.*', 0.1), ('group[0-2].*', 0.1)])]
return optimizer.apply_grad_processors(opt, gradprocs)
def _get_optimizer(self):
lr = symbf.get_scalar_var('learning_rate', 0.003, summary=True)
opt = tf.train.MomentumOptimizer(lr, 0.9)
return optimizer.apply_grad_processors(
opt, [gradproc.ScaleGradient(('.*/b', 2))])
def optimizer(self):
lr = tf.get_variable('learning_rate', initializer=1.0, trainable=False)
opt = tf.train.GradientDescentOptimizer(lr)
return optimizer.apply_grad_processors(
opt, [gradproc.GlobalNormClip(5)])
def optimizer(self):
lr = tf.get_variable('learning_rate', initializer=self.learning_rate, trainable=False)
opt = tf.train.RMSPropOptimizer(lr, epsilon=1e-5)
return optimizer.apply_grad_processors(opt, [gradproc.SummaryGradient()])
def _get_optimizer(self):
lr = tf.get_variable('learning_rate', initializer=3e-5, trainable=False)
opt = tf.train.AdamOptimizer(lr, epsilon=1e-3)
return optimizer.apply_grad_processors(
opt, [gradproc.ScaleGradient(
[('convfcweight.*', 0.1), ('conv5_.*', 5)])])
def _get_optimizer(self):
lr = symbf.get_scalar_var('learning_rate', 1e-3, summary=True)
opt = tf.train.AdamOptimizer(lr, epsilon=1e-3)
return optimizer.apply_grad_processors(
opt, [gradproc.GlobalNormClip(10),
gradproc.SummaryGradient()])
def optimizer(self):
lr = tf.get_variable('learning_rate', initializer=self.learning_rate, trainable=False)
opt = tf.train.RMSPropOptimizer(lr, epsilon=1e-5)
return optimizer.apply_grad_processors(opt, [gradproc.SummaryGradient()])
def optimizer(self):
lr = tf.get_variable('learning_rate', initializer=0.1, trainable=False)
opt = tf.train.MomentumOptimizer(lr, 0.9, use_nesterov=True)
gradprocs = [gradproc.ScaleGradient(
[('conv0.*', 0.1), ('group[0-2].*', 0.1)])]
return optimizer.apply_grad_processors(opt, gradprocs)
def optimizer(self):
lr = tf.get_variable('learning_rate', initializer=1.0, trainable=False)
opt = tf.train.GradientDescentOptimizer(lr)
return optimizer.apply_grad_processors(opt,
[gradproc.GlobalNormClip(5)])
def _get_optimizer(self):
lr = symbolic_functions.get_scalar_var('learning_rate',
2e-3,
summary=True)
opt = tf.train.AdamOptimizer(lr)
return optimizer.apply_grad_processors(opt, [GlobalNormClip(5)])
def _get_optimizer(self):
lr = tf.get_variable('learning_rate', initializer=2e-3, trainable=False)
opt = tf.train.AdamOptimizer(lr)
return optimizer.apply_grad_processors(opt, [GlobalNormClip(5)])
