def _experiment_fn(output_dir):
return Experiment(model.build_estimator(output_dir),
train_input_fn=model.get_input_fn(
filename=os.path.join(data_dir,
'train.tfrecords'),
batch_size=train_batch_size),
eval_input_fn=model.get_input_fn(
filename=os.path.join(data_dir,
'test.tfrecords'),
batch_size=eval_batch_size),
export_strategies=[
saved_model_export_utils.make_export_strategy(
model.serving_input_fn,
default_output_alternative_key=None,
exports_to_keep=1)
],
train_steps=train_steps,
eval_metrics=model.get_eval_metrics(),
eval_steps=eval_steps,
**experiment_args)
def _experiment_fn(output_dir):
input_fn = model.generate_csv_input_fn
train_input = input_fn(train_data_paths,
num_epochs=num_epochs,
batch_size=train_batch_size)
eval_input = input_fn(eval_data_paths,
batch_size=eval_batch_size,
mode=tf.contrib.learn.ModeKeys.EVAL)
return Experiment(model.build_estimator(output_dir,
hidden_units=hidden_units),
train_input_fn=train_input,
eval_input_fn=eval_input,
export_strategies=[
saved_model_export_utils.make_export_strategy(
model.serving_input_fn,
default_output_alternative_key=None,
exports_to_keep=1)
],
eval_metrics=model.get_eval_metrics(),
**experiment_args)
def _experiment_fn(output_dir):
input_fn = model.generate_csv_input_fn
train_input = input_fn(
train_data_paths, num_epochs=num_epochs, batch_size=train_batch_size)
eval_input = input_fn(
eval_data_paths, batch_size=eval_batch_size, mode=tf.contrib.learn.ModeKeys.EVAL)
return Experiment(
model.build_estimator(
output_dir,
hidden_units=hidden_units
),
train_input_fn=train_input,
eval_input_fn=eval_input,
export_strategies=[saved_model_export_utils.make_export_strategy(
model.serving_input_fn,
default_output_alternative_key=None,
exports_to_keep=1
)],
eval_metrics=model.get_eval_metrics(),
#min_eval_frequency = 1000, # change this to speed up training on large datasets
**experiment_args
)
def _experiment_fn(output_dir):
input_fn = (model.generate_csv_input_fn if format == 'csv'
else model.generate_tfrecord_input_fn)
train_input = input_fn(
train_data_paths, num_epochs=num_epochs, batch_size=train_batch_size)
eval_input = input_fn(
eval_data_paths, batch_size=eval_batch_size, mode=tf.contrib.learn.ModeKeys.EVAL)
return Experiment(
model.build_estimator(
output_dir,
nbuckets=nbuckets,
hidden_units=parse_to_int(hidden_units)
),
train_input_fn=train_input,
eval_input_fn=eval_input,
export_strategies=[saved_model_export_utils.make_export_strategy(
model.serving_input_fn,
default_output_alternative_key=None,
exports_to_keep=1
)],
eval_metrics=model.get_eval_metrics(),
#min_eval_frequency = 1000, # change this to speed up training on large datasets
**experiment_args
)