def define_imagenet_flags():
resnet_run_loop.define_resnet_flags(
resnet_size_choices=['18', '34', '50', '101', '152', '200'],
dynamic_loss_scale=True,
fp16_implementation=True)
flags.adopt_module_key_flags(resnet_run_loop)
flags_core.set_defaults(train_epochs=90)
def define_cifar_flags(hp, model_id, model_dir, data_dir, train_epochs, total_epochs, epoch_index): # Xinyi modified
resnet_run_loop.define_resnet_flags()
flags.adopt_module_key_flags(resnet_run_loop)
# Xinyi add followings
flags.DEFINE_string(
name="optimizer", short_name="opt", default=hp['opt_case']['optimizer'],
help=help_wrap("The name of optimizer type"))
if hp['opt_case']['optimizer']=='Momentum' \
or hp['opt_case']['optimizer']=='RMSProp':
flags.DEFINE_float(
name="momentum", short_name="mm",
default=hp['opt_case']['momentum'],
help=help_wrap("The momentum of Momentum SGD or RMSProp"))
if hp['opt_case']['optimizer']=='RMSProp':
flags.DEFINE_float(
name="grad_decay", short_name="rmspd",
default=hp['opt_case']['grad_decay'],
help=help_wrap("The decay of RMSProp"))
flags.DEFINE_float(
name="learning_rate", short_name="lr",
default=hp['opt_case']['lr'],
help=help_wrap("The initial learning rate of optimizer"))
flags.DEFINE_float(
name="decay_rate", short_name="lrdr", default=hp['decay_rate'],
help=help_wrap("The base term of learning rate decay function"))
flags.DEFINE_integer(
name="decay_steps", short_name="lrds", default=hp['decay_steps'],
help=help_wrap("The power term of learning rate decay function"
"This value is in percentage of train_epochs"
"Zero value means turnning off decay"))
flags.DEFINE_string(
name="initializer", short_name="initn", default=hp['initializer'],
help=help_wrap("The name of initialization method"
"None value means glorot_uniform_initializer"))
flags.DEFINE_string(
name="regularizer", short_name="regn", default=hp['regularizer'],
help=help_wrap("The name of regularization method"
"None value means turnning off weight decay"))
flags.DEFINE_float(
name="weight_decay", short_name="wd", default=hp['weight_decay'],
help=help_wrap("The amount of regularization"
"If regularizer=None, the variable becomes useless"))
flags.DEFINE_integer(
name="model_id", short_name="mid", default=model_id,
help=help_wrap("The index of model in the population"))
flags.DEFINE_integer(
name="total_epochs", short_name="ttep", default=train_epochs,
help=help_wrap("The total epochs the model will be trained"))
flags.DEFINE_integer(
name="epoch_index", short_name="epi", default=epoch_index,
help=help_wrap("The epoch index write to csv."))
flags_core.set_defaults(data_dir=data_dir,
model_dir=model_dir,
resnet_size='50',
train_epochs=train_epochs,
epochs_between_evals=1,
batch_size=hp['batch_size'])
def define_cifar_flags():
resnet_run_loop.define_resnet_flags()
flags.adopt_module_key_flags(resnet_run_loop)
flags_core.set_defaults(data_dir='/home/yotamg/data/rgb/train',
model_dir=RESOURCES_OUT_DIR,
resnet_size='32',
train_epochs=2500,
epochs_between_evals=10,
batch_size=128)
def define_cifar_flags():
resnet_run_loop.define_resnet_flags()
flags.adopt_module_key_flags(resnet_run_loop)
flags_core.set_defaults(data_dir='',
model_dir='',
resnet_size='56',
train_epochs=182,
epochs_between_evals=5,
batch_size=128,
image_bytes_as_serving_input=False)
def define_imagenet_flags(): resnet_run_loop.define_resnet_flags( resnet_size_choices=['18', '34', '50', '101', '152', '200']) flags.adopt_module_key_flags(resnet_run_loop) flags_core.set_defaults(train_epochs=90)