def test_dropout(self):
dparams = self.decoder_params
dparams["dropout_keep_prob"] = -0.5
with self.assertRaises(ValueError):
Decoder(**dparams)
dparams["dropout_keep_prob"] = 1.5
with self.assertRaises(ValueError):
Decoder(**dparams)
def test_embedding_size(self):
dparams = copy.deepcopy(DECODER_PARAMS)
dparams["embedding_size"] = None
with self.assertRaises(ValueError):
Decoder(**dparams)
dparams["embedding_size"] = -10
with self.assertRaises(ValueError):
Decoder(**dparams)
def test_dropout(self):
dparams = copy.deepcopy(DECODER_PARAMS)
dparams["dropout_keep_prob"] = -0.5
with self.assertRaises(ValueError):
Decoder(**dparams)
dparams["dropout_keep_prob"] = 1.5
with self.assertRaises(ValueError):
Decoder(**dparams)
def test_embedding_size(self):
dparams = self.decoder_params
dparams["embedding_size"] = None
with self.assertRaises(ValueError):
dec = Decoder(**dparams)
print(dec.embedding_size)
dparams["embedding_size"] = -10
with self.assertRaises(ValueError):
Decoder(**dparams)
def test_cell_type(self):
dparams = copy.deepcopy(DECODER_PARAMS)
dparams.update({"rnn_cell": "bogus_cell"})
with self.assertRaises(ValueError):
Decoder(**dparams)
for cell_type in ("GRU", "LSTM", "NematusGRU"):
print(dparams)
dparams["rnn_cell"] = cell_type
dparams["name"] = "test-decoder-{}".format(cell_type)
Decoder(**dparams)
def build_decoder(config: Dict, attention: Attention,
encoder: RecurrentEncoder) -> Tuple[Decoder, str]:
vocabulary = from_nematus_json(config["tgt_vocabulary"],
max_size=config["n_words_tgt"],
pad_to_max_size=True)
vocabulary_ini = VOCABULARY_TEMPLATE.format("tgt",
config["tgt_vocabulary"],
config["n_words_tgt"])
decoder = Decoder(
name=DECODER_NAME,
vocabulary=vocabulary,
data_id="target",
max_output_len=config["max_length"],
embedding_size=config["embedding_size"],
rnn_size=config["rnn_size"],
encoders=[encoder],
attentions=[attention],
attention_on_input=False,
conditional_gru=True,
encoder_projection=nematus_projection(dropout_keep_prob=1.0),
output_projection=nematus_output(config["embedding_size"]),
rnn_cell="NematusGRU")
decoder_ini = DECODER_TEMPLATE.format(DECODER_NAME,
config["embedding_size"],
config["rnn_size"],
config["max_length"],
config["embedding_size"])
return decoder, "\n".join([vocabulary_ini, decoder_ini])
def test_tie_embeddings(self):
dparams = copy.deepcopy(DECODER_PARAMS)
dparams["tie_embeddings"] = True
dparams["rnn_size"] = 20
dparams["embedding_size"] = 10
with self.assertRaises(ValueError):
Decoder(**dparams)
def test_init(self):
decoder = Decoder(encoders=[],
vocabulary=Vocabulary(),
data_id="foo",
name="test-decoder",
max_output_len=5,
dropout_keep_prob=1.0,
embedding_size=10,
rnn_size=10)
self.assertIsNotNone(decoder)
def test_max_output_len(self):
dparams = copy.deepcopy(DECODER_PARAMS)
dparams["max_output_len"] = -10
with self.assertRaises(ValueError):
Decoder(**dparams)
def test_init(self):
decoder = Decoder(**DECODER_PARAMS)
self.assertIsNotNone(decoder)
def test_max_output_len(self):
dparams = self.decoder_params
dparams["max_output_len"] = -10
with self.assertRaises(ValueError):
Decoder(**dparams)
def test_init(self):
decoder = Decoder(**self.decoder_params)
self.assertIsNotNone(decoder)
def test_init(self):
decoder = Decoder([], Vocabulary(), "foo", "test-decoder")
self.assertIsNotNone(decoder)