def setup_lrn_rate(self, global_step):
"""Setup the learning rate (and number of training iterations)."""
batch_size = FLAGS.batch_size * (1 if not FLAGS.enbl_multi_gpu else
mgw.size())
if FLAGS.mobilenet_version == 1:
nb_epochs = 100
nb_epochs = 412
idxs_epoch = [12000, 20000]
step_rate = [200, 200, 4000]
epoch_step = setup_lrn_rate_piecewise_constant(
global_step, batch_size, idxs_epoch, step_rate)
decay_rates = [0.985, 0.980, 0.505]
decay_rate = setup_lrn_rate_piecewise_constant(
global_step, batch_size, idxs_epoch, decay_rates)
lrn_rate = setup_lrn_rate_exponential_decay(
global_step, batch_size, epoch_step, decay_rate)
nb_iters = int(30000)
elif FLAGS.mobilenet_version == 2:
nb_epochs = 412
epoch_step = 500
decay_rate = 0.9 # which is better, 0.98 OR (0.98 ** epoch_step)?
lrn_rate = setup_lrn_rate_exponential_decay(
global_step, batch_size, epoch_step, decay_rate)
nb_iters = int(15000)
else:
raise ValueError('invalid MobileNet version: {}'.format(
FLAGS.mobilenet_version))
return lrn_rate, nb_iters
def setup_lrn_rate(self, global_step):
"""Setup the learning rate (and number of training iterations)."""
batch_size = FLAGS.batch_size * (1 if not FLAGS.enbl_multi_gpu else
mgw.size())
if FLAGS.mobilenet_version == 1:
nb_epochs = 100
idxs_epoch = [30, 60, 80, 90]
decay_rates = [1.0, 0.1, 0.01, 0.001, 0.0001]
lrn_rate = setup_lrn_rate_piecewise_constant(
global_step, batch_size, idxs_epoch, decay_rates)
nb_iters = int(FLAGS.nb_smpls_train * nb_epochs *
FLAGS.nb_epochs_rat / batch_size)
elif FLAGS.mobilenet_version == 2:
nb_epochs = 412
epoch_step = 2.5
decay_rate = 0.98**epoch_step # which is better, 0.98 OR (0.98 ** epoch_step)?
lrn_rate = setup_lrn_rate_exponential_decay(
global_step, batch_size, epoch_step, decay_rate)
nb_iters = int(FLAGS.nb_smpls_train * nb_epochs *
FLAGS.nb_epochs_rat / batch_size)
else:
raise ValueError('invalid MobileNet version: {}'.format(
FLAGS.mobilenet_version))
return lrn_rate, nb_iters
def setup_lrn_rate(self, global_step):
"""Setup the learning rate (and number of training iterations)."""
nb_epochs = 250
idxs_epoch = [100, 150, 200]
decay_rates = [1.0, 0.1, 0.01, 0.001]
batch_size = FLAGS.batch_size * (1)
lrn_rate = setup_lrn_rate_piecewise_constant(global_step, batch_size, idxs_epoch, decay_rates)
nb_iters = int(FLAGS.nb_smpls_train * nb_epochs * FLAGS.nb_epochs_rat / batch_size)
return lrn_rate, nb_iters
def setup_lrn_rate(self, global_step):
"""Setup the learning rate (and number of training iterations)."""
nb_epochs = 100
idxs_epoch = [30, 60, 80, 90]
decay_rates = [1.0, 0.1, 0.01, 0.001, 0.0001]
batch_size = FLAGS.batch_size * (1 if not FLAGS.enbl_multi_gpu else mgw.size())
lrn_rate = setup_lrn_rate_piecewise_constant(global_step, batch_size, idxs_epoch, decay_rates)
nb_iters = int(FLAGS.nb_smpls_train * nb_epochs * FLAGS.nb_epochs_rat / batch_size)
return lrn_rate, nb_iters
def setup_lrn_rate(self, global_step):
"""Setup the learning rate (and number of training iterations)."""
nb_epochs = 100
idxs_epoch = [0.4, 0.8]
decay_rates = [0.001, 0.0005, 0.0001]
batch_size = FLAGS.batch_size * (1 if not FLAGS.enbl_multi_gpu else
mgw.size())
lrn_rate = setup_lrn_rate_piecewise_constant(global_step, batch_size,
idxs_epoch, decay_rates)
nb_iters = int(12000)
#nb_iters = int(200)
return lrn_rate, nb_iters
