def Params(cls):
p = super(BaseModel, cls).Params()
p.Define(
'model', None, 'Which python function generates the param. It includes '
'the file name and lineno where the function is defined.')
p.Define(
'cluster', cluster_factory.Cluster.Params(),
'The training cluster. Individual layer may config differently'
' based on training cluster it is running under.')
p.Define('input', None, 'Input generator Params.')
p.Define('build_data', build_data.BuildData(), 'Build data of this binary.')
p.Define('train', hyperparams.Params(),
'Params to control how this model should be trained.')
tp = p.train
tp.Define(
'start_up_delay_steps', 200, 'i-th replica starts training after '
'i*(i+1)/2*start_up_delay_steps steps')
tp.Define('max_steps', 4 * 10**6, 'Training max of 4M steps.')
tp.Define('tpu_steps_per_loop', 100, 'The number of training steps per '
'training loop for TPUs.')
tp.Define(
'ema_decay', 0.0,
'If > 0, enable ExponentialMovingAverage during training '
'with the give decay. '
'Must be < 1. Disabled if <= 0.')
tp.Define('init_from_checkpoint_rules', {},
'See BaseTask documentation for details.')
tp.Define('early_stop', None,
'Early stopping based on dev-set performance.')
tp.Define('save_interval_seconds', 60 * 10,
'Generates a checkpoint roughly once every this many seconds.')
tp.Define('summary_interval_steps', 100,
'Generates a checkpoint roughly once every this many steps.')
return p
def Params(cls):
p = super().Params()
p.Define(
'model', None, 'Which python function generates the param. It includes '
'the file name and lineno where the function is defined.')
p.Define(
'cluster', cluster_factory.Cluster.Params(),
'The training cluster. Individual layer may config differently'
' based on training cluster it is running under.')
p.Define('input', None, 'Input generator Params.')
p.Define('build_data', build_data.BuildData(), 'Build data of this binary.')
p.Define('train', hyperparams.Params(),
'Params to control how this model should be trained.')
p.Define('reporting_job', 'evaler',
'Name of job that reports trial results.')
tp = p.train
tp.Define(
'start_up_delay_steps', 200, 'i-th replica starts training after '
'i*(i+1)/2*start_up_delay_steps steps')
tp.Define('max_steps', 4 * 10**6, 'Training max of 4M steps.')
tp.Define('tpu_steps_per_loop', 1000, 'The number of training steps per '
'training loop for TPUs.')
tp.Define(
'tpu_device_order_mode', None,
'A device_assignment_lib.DeviceOrderMode enum that determines whether '
'to assign devices in a way that the order of replicas or '
'model-parallel cores will form a ring or mesh, or let the library to '
'choose. Default None to AUTO.')
tp.Define(
'ema_decay', 0.0,
'If > 0, enable ExponentialMovingAverage during training '
'with the give decay. '
'Must be < 1. Disabled if <= 0. '
'Must be set consistent across all tasks.')
tp.Define(
'ema_decay_moving_vars', None,
'If True, include variables from collection "moving_vars" in ema. '
'Must be set consistent across all tasks.')
tp.Define('init_from_checkpoint_rules', {},
'See BaseTask documentation for details.')
tp.Define('early_stop', None,
'Early stopping based on dev-set performance.')
tp.Define(
'enqueue_max_steps', -1, 'Max enqueue steps. -1 meaning no limit.'
' This flag should be set for unit-test only.')
tp.Define('save_interval_seconds', 60 * 10,
'Generates a checkpoint roughly once every this many seconds.')
tp.Define('save_max_to_keep', 100,
'Maximum number of recent checkpoints to keep.')
tp.Define('save_keep_checkpoint_every_n_hours', 0.5,
'How often to keep a checkpoint.')
tp.Define('summary_interval_steps', 100,
'Generates a checkpoint roughly once every this many steps.')
return p
