def test_input_fn(self):
dataset = census_dataset.input_fn(self.input_csv, 1, False, 1)
features, labels = dataset.make_one_shot_iterator().get_next()
with self.test_session() as sess:
features, labels = sess.run((features, labels))
# Compare the two features dictionaries.
for key in TEST_INPUT_VALUES:
self.assertTrue(key in features)
self.assertEqual(len(features[key]), 1)
feature_value = features[key][0]
# Convert from bytes to string for Python 3.
if isinstance(feature_value, bytes):
feature_value = feature_value.decode()
self.assertEqual(TEST_INPUT_VALUES[key], feature_value)
self.assertFalse(labels)
def eval_input_fn():
return census_dataset.input_fn(test_file, 1, False,
flags_obj.batch_size)
def train_input_fn():
return census_dataset.input_fn(train_file,
flags_obj.epochs_between_evals, True,
flags_obj.batch_size)
def input_fn():
return census_dataset.input_fn(
TEST_CSV, num_epochs=num_epochs, shuffle=shuffle,
batch_size=batch_size)
def eval_input_fn():
return census_dataset.input_fn(data_file=test_file,
num_epochs=1,
shuffle=False,
batch_size=flags_obj.batch_size)
def train_input_fn():
return census_dataset.input_fn(
data_file=train_file,
num_epochs=flags_obj.epochs_between_evals,
shuffle=True,
batch_size=flags_obj.batch_size)
def eval_input_fn():
return census_dataset.input_fn(test_file,
flags_obj.epochs_between_evals, False,
flags_obj.batch_size)