def test_train_saves_all_keys_in_config(self):
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},
},
"pytorch_seed": 42,
"numpy_seed": 42,
"random_seed": 42,
"dataset_reader": {"type": "sequence_tagging"},
"train_data_path": SEQUENCE_TAGGING_DATA_PATH,
"validation_data_path": SEQUENCE_TAGGING_DATA_PATH,
"iterator": {"type": "basic", "batch_size": 2},
"trainer": {"num_epochs": 2, "optimizer": "adam"},
}
)
serialization_dir = os.path.join(self.TEST_DIR, "test_train_model")
params_as_dict = (
params.as_ordered_dict()
) # Do it here as train_model will pop all the values.
train_model(params, serialization_dir=serialization_dir)
config_path = os.path.join(serialization_dir, CONFIG_NAME)
with open(config_path, "r") as config:
saved_config_as_dict = OrderedDict(json.load(config))
assert params_as_dict == saved_config_as_dict
