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 optimizer(self):
lr = tf.get_variable('learning_rate',
initializer=0.003,
trainable=False)
tf.summary.scalar('learning_rate-summary', lr)
factor = cfg.TRAIN.NUM_GPUS / 8.
if factor != 1:
lr = lr * factor
opt = tf.train.MomentumOptimizer(lr, 0.9)
if cfg.TRAIN.NUM_GPUS < 8:
opt = optimizer.AccumGradOptimizer(opt, 8 // cfg.TRAIN.NUM_GPUS)
return opt
def optimizer(self):
lr = tf.get_variable('learning_rate',
initializer=0.003,
trainable=False)
tf.summary.scalar('learning_rate-summary', lr)
# The learning rate is set for 8 GPUs, and we use trainers with average=False.
lr = lr / 8.
opt = tf.train.MomentumOptimizer(lr, 0.9)
opt = optimizer.AccumGradOptimizer(
opt, 8 // cfg.TRAIN.NUM_GPUS) # assume cfg.TRAIN.NUM_GPUS < 8:
return opt
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
def optimizer(self):
lr = tf.get_variable("learning_rate",
initializer=0.003,
trainable=False)
tf.summary.scalar("learning_rate-summary", lr)
# The learning rate in the config is set for 8 GPUs, and we use trainers with average=False.
lr = lr / 8.0
opt = tf.train.MomentumOptimizer(lr, 0.9)
if cfg.TRAIN.NUM_GPUS < 8:
opt = optimizer.AccumGradOptimizer(opt, 8 // cfg.TRAIN.NUM_GPUS)
if cfg.TRAIN.GRADIENT_CLIP != 0:
opt = GradientClipOptimizer(opt, cfg.TRAIN.GRADIENT_CLIP)
return opt
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_optimizer(self):
lr = tf.get_variable('learning_rate',
initializer=0.01,
trainable=False)
tf.summary.scalar('learning_rate', lr)
print("get_nr_gpu", get_nr_gpu())
if config.BIG:
if config.ACC:
factor = 4
lr = lr / float(factor)
opt = tf.train.AdamOptimizer(lr)
opt = optimizer.AccumGradOptimizer(opt, factor)
else:
opt = tf.train.AdamOptimizer(lr, 0.9)
else:
#opt = tf.train.MomentumOptimizer(lr, 0.9)
opt = tf.train.AdamOptimizer(lr)
return opt
def _get_optimizer(self):
lr = tf.get_variable('learning_rate',
initializer=0.01,
trainable=False)
tf.summary.scalar('learning_rate', lr)
"""
factor = get_batch_factor() # accumulate size
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)
"""
if config.ACCU:
factor = 2
# lr = lr / float(factor)
opt = tf.train.AdamOptimizer(lr)
opt = optimizer.AccumGradOptimizer(opt, factor)
else:
opt = tf.train.AdamOptimizer(lr)
#opt = tf.train.MomentumOptimizer(lr, 0.9)
return opt
