def test_recurrent_greedy(self):
batch_size = 2
max_output_length = 3
src_mask, emb, decoder, encoder_output, encoder_hidden = self._build(
batch_size=batch_size)
output, attention_scores = recurrent_greedy(
src_mask=src_mask,
embed=emb,
bos_index=self.bos_index,
eos_index=self.eos_index,
max_output_length=max_output_length,
decoder=decoder,
encoder_output=encoder_output,
encoder_hidden=encoder_hidden)
self.assertEqual(output.shape, (batch_size, max_output_length))
np.testing.assert_equal(output, [[4, 0, 4], [4, 4, 4]])
expected_attention_scores = np.array(
[[[0.22914883, 0.24638498, 0.21247596, 0.3119903],
[0.22970565, 0.24540883, 0.21261126, 0.31227428],
[0.22903332, 0.2459198, 0.2110187, 0.3140282]],
[[0.252522, 0.29074305, 0.257121, 0.19961396],
[0.2519883, 0.2895494, 0.25718424, 0.201278],
[0.2523954, 0.28959078, 0.25769445, 0.2003194]]])
np.testing.assert_array_almost_equal(attention_scores,
expected_attention_scores)
self.assertEqual(attention_scores.shape,
(batch_size, max_output_length, 4))
def test_recurrent_beam1(self):
# beam=1 and greedy should return the same result
batch_size = 2
max_output_length = 3
src_mask, emb, decoder, encoder_output, encoder_hidden = self._build(
batch_size=batch_size)
greedy_output, _ = recurrent_greedy(
src_mask=src_mask,
embed=emb,
bos_index=self.bos_index,
eos_index=self.eos_index,
max_output_length=max_output_length,
decoder=decoder,
encoder_output=encoder_output,
encoder_hidden=encoder_hidden)
beam_size = 1
alpha = 1.0
output, _ = beam_search(size=beam_size,
eos_index=self.eos_index,
pad_index=self.pad_index,
src_mask=src_mask,
embed=emb,
bos_index=self.bos_index,
n_best=1,
max_output_length=max_output_length,
decoder=decoder,
alpha=alpha,
encoder_output=encoder_output,
encoder_hidden=encoder_hidden)
np.testing.assert_array_equal(greedy_output, output)
def test_recurrent_beam1(self):
# beam=1 and greedy should return the same result
batch_size = 2
max_output_length = 3
src_mask, model, encoder_output, encoder_hidden = self._build(
batch_size=batch_size)
greedy_output, _ = recurrent_greedy(
src_mask=src_mask,
max_output_length=max_output_length,
model=model,
encoder_output=encoder_output,
encoder_hidden=encoder_hidden)
beam_size = 1
alpha = 1.0
output, _ = beam_search(size=beam_size,
src_mask=src_mask,
n_best=1,
max_output_length=max_output_length,
model=model,
alpha=alpha,
encoder_output=encoder_output,
encoder_hidden=encoder_hidden)
np.testing.assert_array_equal(greedy_output, output)