def add_trainer(
experiment: cfg.ExperimentConfig,
train_batch_size: int,
eval_batch_size: int,
learning_rate: float = 0.0001,
train_epochs: int = 50,
num_train_examples: int = YT8M_TRAIN_EXAMPLES,
num_val_examples: int = YT8M_VAL_EXAMPLES,
):
"""Add and config a trainer to the experiment config."""
if num_train_examples <= 0:
raise ValueError('Wrong train dataset size {!r}'.format(
experiment.task.train_data))
if num_val_examples <= 0:
raise ValueError('Wrong validation dataset size {!r}'.format(
experiment.task.validation_data))
experiment.task.train_data.global_batch_size = train_batch_size
experiment.task.validation_data.global_batch_size = eval_batch_size
steps_per_epoch = num_train_examples // train_batch_size
steps_per_loop = 500
experiment.trainer = cfg.TrainerConfig(
steps_per_loop=steps_per_loop,
summary_interval=steps_per_loop,
checkpoint_interval=steps_per_loop,
train_steps=train_epochs * steps_per_epoch,
validation_steps=num_val_examples // eval_batch_size,
validation_interval=steps_per_loop,
optimizer_config=optimization.OptimizationConfig({
'optimizer': {
'type': 'adam',
'adam': {}
},
'learning_rate': {
'type': 'exponential',
'exponential': {
'initial_learning_rate': learning_rate,
'decay_rate': 0.95,
'decay_steps': int(steps_per_epoch * 1.5),
'offset': 500,
}
},
'warmup': {
'linear': {
'name': 'linear',
'warmup_learning_rate': 0,
'warmup_steps': 500,
},
'type': 'linear',
}
}))
return experiment
def add_trainer(experiment: cfg.ExperimentConfig,
train_batch_size: int,
eval_batch_size: int,
learning_rate: float = 1.6,
train_epochs: int = 44,
warmup_epochs: int = 5):
"""Add and config a trainer to the experiment config."""
if experiment.task.train_data.num_examples <= 0:
raise ValueError('Wrong train dataset size {!r}'.format(
experiment.task.train_data))
if experiment.task.validation_data.num_examples <= 0:
raise ValueError('Wrong validation dataset size {!r}'.format(
experiment.task.validation_data))
experiment.task.train_data.global_batch_size = train_batch_size
experiment.task.validation_data.global_batch_size = eval_batch_size
steps_per_epoch = experiment.task.train_data.num_examples // train_batch_size
experiment.trainer = cfg.TrainerConfig(
steps_per_loop=steps_per_epoch,
summary_interval=steps_per_epoch,
checkpoint_interval=steps_per_epoch,
train_steps=train_epochs * steps_per_epoch,
validation_steps=experiment.task.validation_data.num_examples //
eval_batch_size,
validation_interval=steps_per_epoch,
optimizer_config=optimization.OptimizationConfig({
'optimizer': {
'type': 'sgd',
'sgd': {
'momentum': 0.9,
'nesterov': True,
}
},
'learning_rate': {
'type': 'cosine',
'cosine': {
'initial_learning_rate': learning_rate,
'decay_steps': train_epochs * steps_per_epoch,
}
},
'warmup': {
'type': 'linear',
'linear': {
'warmup_steps': warmup_epochs * steps_per_epoch,
'warmup_learning_rate': 0
}
}
}))
return experiment
def add_trainer(
experiment: cfg.ExperimentConfig,
train_batch_size: int,
eval_batch_size: int,
learning_rate: float = 0.005,
train_epochs: int = 44,
):
"""Add and config a trainer to the experiment config."""
if YT8M_TRAIN_EXAMPLES <= 0:
raise ValueError('Wrong train dataset size {!r}'.format(
experiment.task.train_data))
if YT8M_VAL_EXAMPLES <= 0:
raise ValueError('Wrong validation dataset size {!r}'.format(
experiment.task.validation_data))
experiment.task.train_data.global_batch_size = train_batch_size
experiment.task.validation_data.global_batch_size = eval_batch_size
steps_per_epoch = YT8M_TRAIN_EXAMPLES // train_batch_size
experiment.trainer = cfg.TrainerConfig(
steps_per_loop=steps_per_epoch,
summary_interval=steps_per_epoch,
checkpoint_interval=steps_per_epoch,
train_steps=train_epochs * steps_per_epoch,
validation_steps=YT8M_VAL_EXAMPLES // eval_batch_size,
validation_interval=steps_per_epoch,
optimizer_config=optimization.OptimizationConfig({
'optimizer': {
'type': 'adam',
'adam': {}
},
'learning_rate': {
'type': 'exponential',
'exponential': {
'initial_learning_rate': learning_rate,
'decay_rate': 0.95,
'decay_steps': 1500000,
}
},
}))
return experiment