def test_TFSimpleRecordInput_rounded(self):
dataset_input = tfrecord_simple.TFSimpleRecordInput(
train_path=None,
validate_path=None,
text_feature="comment",
labels={"label": tf.float32},
round_labels=True)
with self.test_session():
features, labels = dataset_input._read_tf_example(self.ex_tensor)
self.assertEqual(features["comment"].eval(), b"Hi there Bob")
np.testing.assert_almost_equal(labels["label"].eval(), 1.0)
def test_TFSimpleRecordInput_default_values(self):
dataset_input = tfrecord_simple.TFSimpleRecordInput(
train_path=None,
validate_path=None,
text_feature="comment",
labels={"label": tf.float32, "fake_label": tf.float32},
round_labels=False)
with self.test_session():
features, labels = dataset_input._read_tf_example(self.ex_tensor)
self.assertEqual(list(features["comment"].eval()), [b"Hi there Bob"])
np.testing.assert_almost_equal(labels["label"].eval(), [0.8])
np.testing.assert_almost_equal(labels["fake_label"].eval(), [-1.0])
def main(argv):
del argv # unused
dataset = tfrecord_simple.TFSimpleRecordInput(
train_path=FLAGS.train_path,
validate_path=FLAGS.validate_path,
text_feature=FLAGS.text_feature_name,
labels=LABELS,
batch_size=FLAGS.batch_size)
model = tf_hub_classifier.TFHubClassifierModel(FLAGS.text_feature_name,
set(LABELS.keys()))
trainer = model_trainer.ModelTrainer(dataset, model)
trainer.train_with_eval(FLAGS.train_steps, FLAGS.eval_period,
FLAGS.eval_steps)
serving_input_fn = create_serving_input_fn(
text_feature_name=FLAGS.text_feature_name, key_name=FLAGS.key_name)
trainer.export(serving_input_fn)