def run_movie(flags_obj):
"""Construct all necessary functions and call run_loop.
Args:
flags_obj: Object containing user specified flags.
"""
if flags_obj.download_if_missing:
movielens.download(dataset=flags_obj.dataset,
data_dir=flags_obj.data_dir)
train_input_fn, eval_input_fn, model_column_fn = \
movielens_dataset.construct_input_fns(
dataset=flags_obj.dataset, data_dir=flags_obj.data_dir,
batch_size=flags_obj.batch_size, repeat=flags_obj.epochs_between_evals)
tensors_to_log = {'loss': '{loss_prefix}head/weighted_loss/value'}
wide_deep_run_loop.run_loop(name="MovieLens",
train_input_fn=train_input_fn,
eval_input_fn=eval_input_fn,
model_column_fn=model_column_fn,
build_estimator_fn=build_estimator,
flags_obj=flags_obj,
tensors_to_log=tensors_to_log,
early_stop=False)
def test_input_fn(self):
train_input_fn, _, _ = movielens_dataset.construct_input_fns(
dataset=movielens.ML_1M, data_dir=self.temp_dir, batch_size=8, repeat=1)
dataset = train_input_fn()
features, labels = dataset.make_one_shot_iterator().get_next()
with self.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.assertAllClose(TEST_INPUT_VALUES[key], features[key][0])
self.assertAllClose(labels[0], [1.0])
