def run_experiment(run_config):
"""Train, evaluate, and export the model using tf.estimator.train_and_evaluate API"""
train_input_fn = input.generate_input_fn(
file_names_pattern=HYPER_PARAMS.train_files,
mode=tf.estimator.ModeKeys.TRAIN,
num_epochs=HYPER_PARAMS.num_epochs,
batch_size=HYPER_PARAMS.train_batch_size)
eval_input_fn = input.generate_input_fn(
file_names_pattern=HYPER_PARAMS.eval_files,
mode=tf.estimator.ModeKeys.EVAL,
batch_size=HYPER_PARAMS.eval_batch_size)
exporter = tf.estimator.FinalExporter(
'estimator',
input.SERVING_FUNCTIONS[HYPER_PARAMS.export_format],
as_text=
False # change to true if you want to export the model as readable text
)
# compute the number of training steps based on num_epoch, train_size, and train_batch_size
if HYPER_PARAMS.train_size is not None and HYPER_PARAMS.num_epochs is not None:
train_steps = (HYPER_PARAMS.train_size / HYPER_PARAMS.train_batch_size) * \
HYPER_PARAMS.num_epochs
else:
train_steps = HYPER_PARAMS.train_steps
train_spec = tf.estimator.TrainSpec(train_input_fn,
max_steps=int(train_steps))
eval_spec = tf.estimator.EvalSpec(
eval_input_fn,
steps=HYPER_PARAMS.eval_steps,
exporters=[exporter],
name='estimator-eval',
throttle_secs=HYPER_PARAMS.eval_every_secs,
)
print("* experiment configurations")
print("===========================")
print("Train size: {}".format(HYPER_PARAMS.train_size))
print("Epoch count: {}".format(HYPER_PARAMS.num_epochs))
print("Train batch size: {}".format(HYPER_PARAMS.train_batch_size))
print("Training steps: {} ({})".format(
int(train_steps),
"supplied" if HYPER_PARAMS.train_size is None else "computed"))
print("Evaluate every: {} seconds".format(HYPER_PARAMS.eval_every_secs))
print("===========================")
if metadata.TASK_TYPE == "classification":
estimator = model.create_classifier(config=run_config)
elif metadata.TASK_TYPE == "regression":
estimator = model.create_regressor(config=run_config)
else:
estimator = model.create_estimator(config=run_config)
# train and evaluate
tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec)
def experiment():
train_input_fn = generate_input_fn(is_train=True,
tfrecords_path=config.tfrecords_path,
batch_size=config.batch_size,
time_step=config.time_step)
eval_input_fn = generate_input_fn(is_train=False,
tfrecords_path=config.tfrecords_path,
batch_size=config.batch_size_eval,
time_step=config.time_step_eval)
estimator = Estimator(train_input_fn=train_input_fn,
eval_input_fn=eval_input_fn,
model_fn=model_fn)
estimator.train()
def run_experiment(run_config):
"""Train, evaluate, and export the model using tf.estimator.train_and_evaluate API"""
train_input_fn = input.generate_input_fn(
file_names_pattern=HYPER_PARAMS.train_files,
mode=tf.estimator.ModeKeys.TRAIN,
num_epochs=HYPER_PARAMS.num_epochs,
batch_size=HYPER_PARAMS.train_batch_size)
eval_input_fn = input.generate_input_fn(
file_names_pattern=HYPER_PARAMS.eval_files,
mode=tf.estimator.ModeKeys.EVAL,
batch_size=HYPER_PARAMS.eval_batch_size)
exporter = tf.estimator.FinalExporter(
'estimator',
input.SERVING_FUNCTIONS[HYPER_PARAMS.export_format],
as_text=
False # change to true if you want to export the model as readable text
)
# compute the number of training steps based on num_epoch, train_size, and train_batch_size
if HYPER_PARAMS.train_size is not None and HYPER_PARAMS.num_epochs is not None:
train_steps = (HYPER_PARAMS.train_size / HYPER_PARAMS.train_batch_size) * \
HYPER_PARAMS.num_epochs
else:
train_steps = HYPER_PARAMS.train_steps
train_spec = tf.estimator.TrainSpec(train_input_fn,
max_steps=int(train_steps))
eval_spec = tf.estimator.EvalSpec(
eval_input_fn,
steps=HYPER_PARAMS.eval_steps,
exporters=[exporter],
throttle_secs=HYPER_PARAMS.eval_every_secs,
)
print("* experiment configurations")
print("===========================")
print("Train size: {}".format(HYPER_PARAMS.train_size))
print("Epoch count: {}".format(HYPER_PARAMS.num_epochs))
print("Train batch size: {}".format(HYPER_PARAMS.train_batch_size))
print("Training steps: {} ({})".format(
int(train_steps),
"supplied" if HYPER_PARAMS.train_size is None else "computed"))
print("Evaluate every {} seconds".format(HYPER_PARAMS.eval_every_secs))
print("===========================")
if metadata.TASK_TYPE == "classification":
estimator = model.create_classifier(config=run_config)
elif metadata.TASK_TYPE == "regression":
estimator = model.create_regressor(config=run_config)
else:
estimator = model.create_estimator(config=run_config)
# train and evaluate
tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec)
# This is the export for the Tensorflow Model Analysis tool.
if HYPER_PARAMS.export_format in ['CSV', 'EXAMPLE']:
eval_receiver_fn = input.TFMA_SERVING_FUNCTIONS[
HYPER_PARAMS.export_format]
tfma_export.export_eval_savedmodel(
estimator=estimator,
export_dir_base=os.path.join(estimator.model_dir, "tfma_export"),
eval_input_receiver_fn=eval_receiver_fn)
else:
tf.logging.info("TFMA doesn't yet support a JSON input receiver. "
"The TFMA export will not be created.")