def make_experiment_fn(args):
train_input_fn = util.make_input_fn(
args.train_data_file,
args.batch_size,
args.num_skips,
args.skip_window,
args.vocab_size,
num_epochs=args.num_epochs
)
eval_input_fn = util.make_input_fn(
args.eval_data_file,
args.batch_size,
args.num_skips,
args.skip_window,
args.vocab_size,
num_epochs=args.num_epochs
)
def experiment_fn(output_dir):
return Experiment(
Estimator(
model_fn=model.make_model_fn(**args.__dict__),
model_dir=output_dir
),
train_input_fn=train_input_fn,
eval_input_fn=eval_input_fn,
continuous_eval_throttle_secs=args.min_eval_seconds,
min_eval_frequency=args.min_train_eval_rate,
# Until Experiment moves to train_and_evaluate call internally
local_eval_frequency=args.min_train_eval_rate
)
return experiment_fn
def make_experiment_fn(args):
train_input_fn = util.make_input_fn(
args.train_data_paths,
args.batch_size,
args.index_file,
num_epochs=args.num_epochs
)
eval_input_fn = util.make_input_fn(
args.eval_data_paths,
args.batch_size,
args.index_file,
num_epochs=args.num_epochs
)
def experiment_fn(output_dir):
return Experiment(
Estimator(
model_fn=model.make_model_fn(args),
model_dir=output_dir
),
train_input_fn=train_input_fn,
eval_input_fn=eval_input_fn,
continuous_eval_throttle_secs=args.min_eval_seconds,
min_eval_frequency=args.min_train_eval_rate,
# Until learn_runner is updated to use train_and_evaluate
local_eval_frequency=args.min_train_eval_rate
)
return experiment_fn
def make_experiment_fn(args):
train_input_fn = util.make_input_fn(
args.train_data_paths,
util.parse_examples,
args.batch_size,
num_epochs=args.num_epochs
)
eval_input_fn = util.make_input_fn(
args.eval_data_paths,
util.parse_examples,
args.batch_size,
num_epochs=args.num_epochs
)
def _experiment_fn(output_dir):
return learn.Experiment(
learn.Estimator(
model_fn=model.make_model_fn(args),
model_dir=output_dir
),
train_input_fn=train_input_fn,
eval_input_fn=eval_input_fn,
train_steps=args.max_steps,
eval_metrics=model.METRICS,
continuous_eval_throttle_secs=args.min_eval_seconds,
min_eval_frequency=args.min_train_eval_rate,
# Until learn_runner is updated to use train_and_evaluate
local_eval_frequency=args.min_train_eval_rate
)
return _experiment_fn
def make_experiment_fn(args):
train_input_fn = util.make_input_fn(
args.train_data_paths,
util.parse_examples,
args.batch_size,
num_epochs=args.num_epochs
)
eval_input_fn = util.make_input_fn(
args.eval_data_paths,
util.parse_examples,
args.batch_size,
num_epochs=args.num_epochs
)
def _experiment_fn(output_dir):
return learn.Experiment(
learn.Estimator(
model_fn=model.make_model_fn(args),
model_dir=output_dir
),
train_input_fn=train_input_fn,
eval_input_fn=eval_input_fn,
train_steps=args.max_steps,
eval_metrics=model.METRICS,
continuous_eval_throttle_secs=args.min_eval_seconds,
min_eval_frequency=args.min_train_eval_rate,
# Until learn_runner is updated to use train_and_evaluate
local_eval_frequency=args.min_train_eval_rate
)
return _experiment_fn
def make_experiment_fn(args):
train_input_fn = util.make_input_fn(args.train_data_file,
args.batch_size,
args.num_skips,
args.skip_window,
args.vocab_size,
num_epochs=args.num_epochs)
eval_input_fn = util.make_input_fn(args.eval_data_file,
args.batch_size,
args.num_skips,
args.skip_window,
args.vocab_size,
num_epochs=args.num_epochs)
def experiment_fn(output_dir):
return Experiment(
Estimator(model_fn=model.make_model_fn(**args.__dict__),
model_dir=output_dir),
train_input_fn=train_input_fn,
eval_input_fn=eval_input_fn,
continuous_eval_throttle_secs=args.min_eval_seconds,
min_eval_frequency=args.min_train_eval_rate,
# Until Experiment moves to train_and_evaluate call internally
local_eval_frequency=args.min_train_eval_rate)
return experiment_fn
def make_experiment_fn(args):
train_input_fn = util.make_input_fn(args.train_data_paths,
args.batch_size,
args.index_file,
num_epochs=args.num_epochs)
eval_input_fn = util.make_input_fn(args.eval_data_paths,
args.batch_size,
args.index_file,
num_epochs=args.num_epochs)
def experiment_fn(output_dir):
return Experiment(
Estimator(model_fn=model.make_model_fn(args),
model_dir=output_dir),
train_input_fn=train_input_fn,
eval_input_fn=eval_input_fn,
continuous_eval_throttle_secs=args.min_eval_seconds,
min_eval_frequency=args.min_train_eval_rate,
# Until learn_runner is updated to use train_and_evaluate
local_eval_frequency=args.min_train_eval_rate)
return experiment_fn