def test_dnn_classifier(self):
embedding = feature_column_lib.embedding_column(
feature_column_lib.categorical_column_with_vocabulary_list(
'wire_cast', ['kima', 'omar', 'stringer']), 8)
dnn = estimator_lib.DNNClassifier(feature_columns=[embedding],
hidden_units=[3, 1])
def train_input_fn():
return dataset_ops.Dataset.from_tensors(({
'wire_cast': [['omar'], ['kima']]
}, [[0], [1]])).repeat(3)
def eval_input_fn():
return dataset_ops.Dataset.from_tensors(({
'wire_cast': [['stringer'], ['kima']]
}, [[0], [1]])).repeat(2)
evaluator = hooks_lib.InMemoryEvaluatorHook(dnn,
eval_input_fn,
name='in-memory')
dnn.train(train_input_fn, hooks=[evaluator])
self.assertTrue(os.path.isdir(dnn.eval_dir('in-memory')))
step_keyword_to_value = summary_step_keyword_to_value_mapping(
dnn.eval_dir('in-memory'))
final_metrics = dnn.evaluate(eval_input_fn)
step = final_metrics[ops.GraphKeys.GLOBAL_STEP]
for summary_tag in final_metrics:
if summary_tag == ops.GraphKeys.GLOBAL_STEP:
continue
self.assertEqual(final_metrics[summary_tag],
step_keyword_to_value[step][summary_tag])
def test_creates_regular_stop_at_step_hook_for_chief(self):
# by default an estimator is in chief mode
dnn = estimator_lib.DNNClassifier(
feature_columns=[feature_column_lib.numeric_column('x')],
hidden_units=[3, 1])
hook = hooks_lib.make_stop_at_checkpoint_step_hook(dnn, 300)
self.assertIsInstance(hook, training.StopAtStepHook)
self.assertEqual(300, hook._last_step)
def test_creates_checkpoint_hook_for_workers(self):
class FakeWorkerConfig(estimator_lib.RunConfig):
@property
def is_chief(self):
return False
dnn = estimator_lib.DNNClassifier(
feature_columns=[feature_column_lib.numeric_column('x')],
hidden_units=[3, 1],
config=FakeWorkerConfig())
hook = hooks_lib.make_stop_at_checkpoint_step_hook(dnn, 300)
self.assertIsInstance(hook, hooks_lib._StopAtCheckpointStepHook)
self.assertEqual(300, hook._last_step)
self.assertEqual(dnn.model_dir, hook._model_dir)
def test_raise_error_with_ps(self):
tf_config = {
'cluster': {
run_config_lib.TaskType.CHIEF: ['host0:0'],
run_config_lib.TaskType.PS: ['host1:1'],
},
'task': {
'type': run_config_lib.TaskType.CHIEF,
'index': 0
}
}
with test.mock.patch.dict('os.environ',
{'TF_CONFIG': json.dumps(tf_config)}):
dnn = estimator_lib.DNNClassifier(
feature_columns=[feature_column_lib.numeric_column('x')],
hidden_units=[3, 1])
def eval_input_fn():
pass
with self.assertRaisesRegexp(ValueError,
'supports only single machine'):
hooks_lib.InMemoryEvaluatorHook(dnn, eval_input_fn)
