def test_trainer_fn(self):
temp_dir = os.path.join(
os.environ.get('TEST_UNDECLARED_OUTPUTS_DIR', self.get_temp_dir()),
self._testMethodName)
schema_file = os.path.join(self._testdata_path,
'schema_gen/schema.pbtxt')
hparams = tf.contrib.training.HParams(
train_files=os.path.join(temp_dir, 'train_files'),
transform_output=os.path.join(self._testdata_path,
'transform/transform_output/'),
output_dir=os.path.join(temp_dir, 'output_dir'),
serving_model_dir=os.path.join(temp_dir, 'serving_model_dir'),
eval_files=os.path.join(temp_dir, 'eval_files'),
schema_file=schema_file,
train_steps=10001,
eval_steps=5000,
verbosity='INFO',
warm_start_from=os.path.join(temp_dir, 'serving_model_dir'))
schema = io_utils.parse_pbtxt_file(schema_file, schema_pb2.Schema())
training_spec = taxi_utils.trainer_fn(hparams, schema)
self.assertIsInstance(training_spec['estimator'],
tf.estimator.DNNLinearCombinedClassifier)
self.assertIsInstance(training_spec['train_spec'],
tf.estimator.TrainSpec)
self.assertIsInstance(training_spec['eval_spec'],
tf.estimator.EvalSpec)
self.assertIsInstance(training_spec['eval_input_receiver_fn'],
types.FunctionType)
def test_trainer_fn(self):
temp_dir = os.path.join(
os.environ.get('TEST_UNDECLARED_OUTPUTS_DIR', self.get_temp_dir()),
self._testMethodName)
schema_file = os.path.join(self._testdata_path, 'schema_gen/schema.pbtxt')
hparams = tf.contrib.training.HParams(
train_files=os.path.join(temp_dir, 'train_files'),
transform_output=os.path.join(self._testdata_path,
'transform/transform_output/'),
output_dir=os.path.join(temp_dir, 'output_dir'),
serving_model_dir=os.path.join(temp_dir, 'serving_model_dir'),
eval_files=os.path.join(temp_dir, 'eval_files'),
schema_file=schema_file,
train_steps=10001,
eval_steps=5000,
verbosity='INFO',
warm_start_from=os.path.join(temp_dir, 'serving_model_dir'))
schema = io_utils.parse_pbtxt_file(schema_file, schema_pb2.Schema())
training_spec = taxi_utils.trainer_fn(hparams, schema)
self.assertIsInstance(training_spec['estimator'],
tf.estimator.DNNLinearCombinedClassifier)
self.assertIsInstance(training_spec['train_spec'], tf.estimator.TrainSpec)
self.assertIsInstance(training_spec['eval_spec'], tf.estimator.EvalSpec)
self.assertIsInstance(training_spec['eval_input_receiver_fn'],
types.FunctionType)
def test_trainer_fn(self):
temp_dir = os.path.join(
os.environ.get('TEST_UNDECLARED_OUTPUTS_DIR', self.get_temp_dir()),
self._testMethodName)
schema_file = os.path.join(self._testdata_path,
'schema_gen/schema.pbtxt')
output_dir = os.path.join(temp_dir, 'output_dir')
hparams = tf.contrib.training.HParams(
train_files=os.path.join(
self._testdata_path,
'transform/transformed_examples/train/*.gz'),
transform_output=os.path.join(self._testdata_path,
'transform/transform_output/'),
output_dir=output_dir,
serving_model_dir=os.path.join(temp_dir, 'serving_model_dir'),
eval_files=os.path.join(
self._testdata_path,
'transform/transformed_examples/eval/*.gz'),
schema_file=schema_file,
train_steps=1,
eval_steps=1,
verbosity='INFO',
warm_start_from=os.path.join(self._testdata_path,
'trainer/current/serving_model_dir'))
schema = io_utils.parse_pbtxt_file(schema_file, schema_pb2.Schema())
training_spec = taxi_utils.trainer_fn(hparams, schema)
estimator = training_spec['estimator']
train_spec = training_spec['train_spec']
eval_spec = training_spec['eval_spec']
eval_input_receiver_fn = training_spec['eval_input_receiver_fn']
self.assertIsInstance(estimator,
tf.estimator.DNNLinearCombinedClassifier)
self.assertIsInstance(train_spec, tf.estimator.TrainSpec)
self.assertIsInstance(eval_spec, tf.estimator.EvalSpec)
self.assertIsInstance(eval_input_receiver_fn, types.FunctionType)
# Train for one step, then eval for one step.
eval_result, exports = tf.estimator.train_and_evaluate(
estimator, train_spec, eval_spec)
self.assertGreater(eval_result['loss'], 0.0)
self.assertEqual(len(exports), 1)
self.assertGreaterEqual(len(tf.gfile.ListDirectory(exports[0])), 1)
# Export the eval saved model.
eval_savedmodel_path = tfma.export.export_eval_savedmodel(
estimator=estimator,
export_dir_base=path_utils.eval_model_dir(output_dir),
eval_input_receiver_fn=eval_input_receiver_fn)
self.assertGreaterEqual(
len(tf.gfile.ListDirectory(eval_savedmodel_path)), 1)
# Test exported serving graph.
with tf.Session() as sess:
metagraph_def = tf.compat.v1.saved_model.loader.load(
sess, [tf.saved_model.tag_constants.SERVING], exports[0])
self.assertIsInstance(metagraph_def, tf.MetaGraphDef)
def testTrainerFn(self):
temp_dir = os.path.join(
os.environ.get('TEST_UNDECLARED_OUTPUTS_DIR', self.get_temp_dir()),
self._testMethodName)
schema_file = os.path.join(self._testdata_path,
'schema_gen/schema.pbtxt')
output_dir = os.path.join(temp_dir, 'output_dir')
data_accessor = DataAccessor(tf_dataset_factory=tfxio_utils.
get_tf_dataset_factory_from_artifact(
[standard_artifacts.Examples()], []),
record_batch_factory=None)
trainer_fn_args = trainer_executor.TrainerFnArgs(
train_files=os.path.join(
self._testdata_path,
'transform/transformed_examples/train/*.gz'),
transform_output=os.path.join(self._testdata_path,
'transform/transform_graph'),
output_dir=output_dir,
serving_model_dir=os.path.join(temp_dir, 'serving_model_dir'),
eval_files=os.path.join(
self._testdata_path,
'transform/transformed_examples/eval/*.gz'),
schema_file=schema_file,
train_steps=1,
eval_steps=1,
verbosity='INFO',
base_model=None,
data_accessor=data_accessor)
schema = io_utils.parse_pbtxt_file(schema_file, schema_pb2.Schema())
training_spec = taxi_utils.trainer_fn(trainer_fn_args, schema)
estimator = training_spec['estimator']
train_spec = training_spec['train_spec']
eval_spec = training_spec['eval_spec']
eval_input_receiver_fn = training_spec['eval_input_receiver_fn']
self.assertIsInstance(estimator,
tf.estimator.DNNLinearCombinedClassifier)
self.assertIsInstance(train_spec, tf.estimator.TrainSpec)
self.assertIsInstance(eval_spec, tf.estimator.EvalSpec)
self.assertIsInstance(eval_input_receiver_fn, types.FunctionType)
# Test keep_max_checkpoint in RunConfig
self.assertGreater(estimator._config.keep_checkpoint_max, 1)
# Train for one step, then eval for one step.
eval_result, exports = tf.estimator.train_and_evaluate(
estimator, train_spec, eval_spec)
self.assertGreater(eval_result['loss'], 0.0)
self.assertEqual(len(exports), 1)
self.assertGreaterEqual(len(tf.io.gfile.listdir(exports[0])), 1)
# Export the eval saved model.
eval_savedmodel_path = tfma.export.export_eval_savedmodel(
estimator=estimator,
export_dir_base=path_utils.eval_model_dir(output_dir),
eval_input_receiver_fn=eval_input_receiver_fn)
self.assertGreaterEqual(len(tf.io.gfile.listdir(eval_savedmodel_path)),
1)
# Test exported serving graph.
with tf.compat.v1.Session() as sess:
metagraph_def = tf.compat.v1.saved_model.loader.load(
sess, [tf.saved_model.SERVING], exports[0])
self.assertIsInstance(metagraph_def, tf.compat.v1.MetaGraphDef)
