def setUp(self):
xnmt.events.clear()
self.model_context = ModelContext()
self.model_context.dynet_param_collection = PersistentParamCollection(
"some_file", 1)
self.model = DefaultTranslator(
src_embedder=SimpleWordEmbedder(self.model_context,
vocab_size=100),
encoder=BiLSTMSeqTransducer(self.model_context),
attender=MlpAttender(self.model_context),
trg_embedder=SimpleWordEmbedder(self.model_context,
vocab_size=100),
decoder=MlpSoftmaxDecoder(self.model_context,
vocab_size=100,
bridge=CopyBridge(self.model_context,
dec_layers=1)),
)
self.model.initialize_training_strategy(TrainingStrategy())
self.model.set_train(False)
self.model.initialize_generator()
self.training_corpus = BilingualTrainingCorpus(
train_src="examples/data/head.ja",
train_trg="examples/data/head.en",
dev_src="examples/data/head.ja",
dev_trg="examples/data/head.en")
self.corpus_parser = BilingualCorpusParser(
src_reader=PlainTextReader(),
trg_reader=PlainTextReader(),
training_corpus=self.training_corpus)
def test_loss_model2(self):
model = DefaultTranslator(
src_embedder=SimpleWordEmbedder(self.model_context,
vocab_size=100),
encoder=PyramidalLSTMSeqTransducer(self.model_context, layers=3),
attender=MlpAttender(self.model_context),
trg_embedder=SimpleWordEmbedder(self.model_context,
vocab_size=100),
decoder=MlpSoftmaxDecoder(self.model_context, vocab_size=100),
)
model.initialize_training_strategy(TrainingStrategy())
model.set_train(False)
self.assert_single_loss_equals_batch_loss(model)
def test_overfitting(self):
self.model_context = ModelContext()
self.model_context.dynet_param_collection = PersistentParamCollection(
"some_file", 1)
self.model_context.default_layer_dim = 16
train_args = {}
training_corpus = BilingualTrainingCorpus(
train_src="examples/data/head.ja",
train_trg="examples/data/head.en",
dev_src="examples/data/head.ja",
dev_trg="examples/data/head.en")
train_args['corpus_parser'] = BilingualCorpusParser(
training_corpus=training_corpus,
src_reader=PlainTextReader(),
trg_reader=PlainTextReader())
train_args['training_strategy'] = TrainingStrategy()
train_args['model'] = DefaultTranslator(
src_embedder=SimpleWordEmbedder(self.model_context,
vocab_size=100),
encoder=BiLSTMSeqTransducer(self.model_context),
attender=MlpAttender(self.model_context),
trg_embedder=SimpleWordEmbedder(self.model_context,
vocab_size=100),
decoder=MlpSoftmaxDecoder(self.model_context, vocab_size=100),
)
train_args['model_file'] = None
train_args['save_num_checkpoints'] = 0
train_args['trainer'] = AdamTrainer(self.model_context, alpha=0.1)
train_args['batcher'] = SrcBatcher(batch_size=10,
break_ties_randomly=False)
training_regimen = xnmt.train.TrainingRegimen(
yaml_context=self.model_context, **train_args)
training_regimen.model_context = self.model_context
for _ in range(50):
training_regimen.one_epoch(update_weights=True)
self.assertAlmostEqual(
0.0,
training_regimen.logger.epoch_loss.loss_values['loss'] /
training_regimen.logger.epoch_words,
places=2)
def create_corpus_and_model(self):
self.corpus_parser = self.args["corpus_parser"]
if not hasattr(
self.corpus_parser.training_corpus,
"train_src_data"): # TODO: not so pretty, needs refactoring
self.corpus_parser._read_training_corpus(
self.corpus_parser.training_corpus)
self.total_train_sent = len(
self.corpus_parser.training_corpus.train_src_data)
self.model_context.default_layer_dim = self.args["default_layer_dim"]
self.model_context.dropout = self.args["dropout"]
self.model_context.weight_noise = self.args["weight_noise"]
if not self.args["model"]:
raise RuntimeError("No model specified!")
self.model = self.args["model"]
if self.args["training_strategy"]:
self.training_strategy = self.args["training_strategy"]
else:
self.training_strategy = TrainingStrategy(TrainingMLELoss())
if self.args.get("pretrained_model_file", None):
self.model_context.dynet_param_collection.load_from_data_file(
self.args["pretrained_model_file"] + '.data')