def setUp(self):
super().setUp()
params = Params({
"model": {
"type": "simple_tagger",
"text_field_embedder": {
"token_embedders": {
"tokens": {
"type": "embedding",
"embedding_dim": 5
}
}
},
"encoder": {
"type": "lstm",
"input_size": 5,
"hidden_size": 7,
"num_layers": 2
},
},
"dataset_reader": {
"type": "sequence_tagging"
},
"train_data_path":
str(self.FIXTURES_ROOT / "data" / "sequence_tagging.tsv"),
"validation_data_path":
str(self.FIXTURES_ROOT / "data" / "sequence_tagging.tsv"),
"iterator": {
"type": "basic",
"batch_size": 2
},
"trainer": {
"cuda_device": -1,
"num_epochs": 2,
"optimizer": "adam"
},
})
all_datasets = datasets_from_params(params)
vocab = Vocabulary.from_params(
params.pop("vocabulary", {}),
(instance for dataset in all_datasets.values()
for instance in dataset),
)
model = Model.from_params(vocab=vocab, params=params.pop("model"))
iterator = DataIterator.from_params(params.pop("iterator"))
iterator.index_with(vocab)
train_data = all_datasets["train"]
trainer_params = params.pop("trainer")
serialization_dir = os.path.join(self.TEST_DIR,
"test_search_learning_rate")
self.trainer = Trainer.from_params(
model,
serialization_dir,
iterator,
train_data,
params=trainer_params,
validation_data=None,
validation_iterator=None,
)
def setUp(self):
super().setUp()
params = Params({
"model": {
"type": "simple_tagger",
"text_field_embedder": {
"token_embedders": {
"tokens": {
"type": "embedding",
"embedding_dim": 5
}
}
},
"encoder": {
"type": "lstm",
"input_size": 5,
"hidden_size": 7,
"num_layers": 2
}
},
"dataset_reader": {"type": "sequence_tagging"},
"train_data_path": str(self.FIXTURES_ROOT / 'data' / 'sequence_tagging.tsv'),
"validation_data_path": str(self.FIXTURES_ROOT / 'data' / 'sequence_tagging.tsv'),
"iterator": {"type": "basic", "batch_size": 2},
"trainer": {
"cuda_device": -1,
"num_epochs": 2,
"optimizer": "adam"
}
})
all_datasets = datasets_from_params(params)
vocab = Vocabulary.from_params(
params.pop("vocabulary", {}),
(instance for dataset in all_datasets.values()
for instance in dataset)
)
model = Model.from_params(vocab=vocab, params=params.pop('model'))
iterator = DataIterator.from_params(params.pop("iterator"))
iterator.index_with(vocab)
train_data = all_datasets['train']
trainer_params = params.pop("trainer")
serialization_dir = os.path.join(self.TEST_DIR, 'test_search_learning_rate')
self.trainer = Trainer.from_params(model,
serialization_dir,
iterator,
train_data,
params=trainer_params,
validation_data=None,
validation_iterator=None)