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 setUp(self): xnmt.events.clear() self.model_context = ModelContext() self.model_context.dynet_param_collection = PersistentParamCollection( "some_file", 1) 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_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 test_train_dev_loss_equal(self): self.model_context = ModelContext() self.model_context.dynet_param_collection = NonPersistentParamCollection( ) 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['loss_calculator'] = LossCalculator() 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['trainer'] = None train_args['batcher'] = SrcBatcher(batch_size=5, break_ties_randomly=False) train_args['run_for_epochs'] = 1 training_regimen = xnmt.training_regimen.SimpleTrainingRegimen( yaml_context=self.model_context, **train_args) training_regimen.model_context = self.model_context training_regimen.run_training(update_weights=False) self.assertAlmostEqual( training_regimen.logger.epoch_loss.loss_values['loss'] / training_regimen.logger.epoch_words, training_regimen.logger.dev_score.loss)