def testParameterSearchRunsNormallyUsingFirst(self):
interface = BasicInterface()
filename = "iris_tests/full_iris_with_parameter_search.xml"
block_filepath = self.setup_holder.filepath_handler.get_test_block_path(
filename)
data_filepath = self.setup_holder.filepath_handler.get_test_block_path(
"iris_tests")
interface.load_file(block_filepath)
interface.set_variable("data_folder", data_filepath)
interface.initialize()
interface.train()
performance = interface.evaluate()
self.assertGreaterEqual(1.0, performance)
self.assertLess(0.9, performance)
bg = interface.get_execution_component("batch_generator")
self.assertEqual(10, bg.get_value_model().get_batch_size())
mlp = interface.get_execution_component("mlp")
self.assertEqual(10, mlp.get_value_model().get_transform_shape()[1])
self.assertEqual(0.1, mlp.get_value_model().get_dropout_rate())
def testSeqtoSeqWithSgdLrDecease(self):
filename = "full_ml_tests/toy_nmt/toy_nmt_sgd_learning_rate_decay.xml"
block_filepath = self.setup_holder.filepath_handler.get_test_block_path(
filename)
data_filepath = self.setup_holder.filepath_handler.get_test_data_path(
"nmt/toy/")
embedding_filepath = self.setup_holder.filepath_handler.get_test_data_path(
"embeddings/")
interface = BasicInterface()
interface.load_file(block_filepath)
interface.set_variable("data_folder", data_filepath)
interface.set_variable("embedding_folder", embedding_filepath)
interface.initialize()
f = open(data_filepath + "tgt.txt")
lines = [l.strip() for l in f]
gold_sentences = [l + " EOS" for l in lines]
f.close()
upd_component = interface.get_execution_component("upd")
learning_rate_variable = upd_component.value_model.get_learning_rate()
lr = interface.ml_helper.tensorflow_session_model.run(
learning_rate_variable, {})
self.assertAlmostEqual(0.1, lr)
interface.train(iterations=10)
lr = interface.ml_helper.tensorflow_session_model.run(
learning_rate_variable, {})
self.assertAlmostEqual(0.1, lr)
interface.train(iterations=1991)
lr = interface.ml_helper.tensorflow_session_model.run(
learning_rate_variable, {})
self.assertAlmostEqual(0.0999, lr)
interface.train(iterations=1)
lr = interface.ml_helper.tensorflow_session_model.run(
learning_rate_variable, {})
self.assertAlmostEqual(0.0998001, lr)
interface.train()
predictions = interface.predict()
self.assertEqual(len(gold_sentences), len(predictions))
for i, s in enumerate(gold_sentences):
pred_sent = " ".join(predictions[i])
self.assertEqual(s, pred_sent)
