def testTransformerRelative(self):
model, features = get_model(transformer.transformer_relative_tiny())
logits, _ = model(features)
with self.test_session() as session:
session.run(tf.global_variables_initializer())
res = session.run(logits)
self.assertEqual(res.shape, (BATCH_SIZE, TARGET_LENGTH, 1, 1, VOCAB_SIZE))
def testTransformerRelative(self):
model, features = get_model(transformer.transformer_relative_tiny())
logits, _ = model(features)
with self.test_session() as session:
session.run(tf.global_variables_initializer())
res = session.run(logits)
self.assertEqual(res.shape, (BATCH_SIZE, TARGET_LENGTH, 1, 1, VOCAB_SIZE))
def testBeamDecodeWithRelativeAttention(self):
decode_length = 2
model, features = get_model(transformer.transformer_relative_tiny())
model.set_mode(tf.estimator.ModeKeys.PREDICT)
beam_result = model._beam_decode(
features, decode_length, beam_size=4, top_beams=1,
alpha=1.0)["outputs"]
with self.test_session():
tf.global_variables_initializer().run()
beam_result.eval()
def testBeamDecodeWithRelativeAttention(self):
decode_length = 2
model, features = get_model(transformer.transformer_relative_tiny())
model.set_mode(tf.estimator.ModeKeys.PREDICT)
beam_result = model._beam_decode(
features, decode_length, beam_size=4, top_beams=1,
alpha=1.0)["outputs"]
with self.test_session():
tf.global_variables_initializer().run()
beam_result.eval()
def testBeamDecodeWithRelativeAttention(self):
decode_length = 2
model, features = self.getModel(transformer.transformer_relative_tiny())
model(features)
model.set_mode(tf.estimator.ModeKeys.PREDICT)
with tf.variable_scope(tf.get_variable_scope(), reuse=True):
beam_result = model._beam_decode(
features, decode_length, beam_size=4, top_beams=1,
alpha=1.0)["outputs"]
with self.test_session():
tf.global_variables_initializer().run()
beam_res = beam_result.eval()
self.assertEqual(beam_res.shape, (BATCH_SIZE, INPUT_LENGTH + decode_length))
