def test_simple_model_roundtrip_bytes_serializable_attrs(): fwd = lambda model, X, is_train: (X, lambda dY: dY) attr = SerializableAttr() assert attr.value == "foo" assert attr.to_bytes() == b"foo" model = Model("test", fwd, attrs={"test": attr}) model.initialize() @serialize_attr.register(SerializableAttr) def serialize_attr_custom(_, value, name, model): return value.to_bytes() @deserialize_attr.register(SerializableAttr) def deserialize_attr_custom(_, value, name, model): return SerializableAttr().from_bytes(value) model_bytes = model.to_bytes() model = model.from_bytes(model_bytes) assert "test" in model.attrs assert model.attrs["test"].value == "foo from bytes"
def train_model( model: Model, *, train: Sequence[Tuple[str, str]], test: Sequence[Tuple[str, str]], n_iter: int, batch_size: int | thinc.types.Generator = 32, learn_rate: float | List[float] | thinc.types.Generator = 0.001, ) -> Model: """ Args: model train test n_iter batch_size learn_rate """ # binarize language labels # NOTE: thinc seems to require type "float32" arrays for training labels # errors otherwise... :/ lb = sklearn.preprocessing.LabelBinarizer() lb.fit([lang for _, lang in train]) # THIS NEXT LINE IS CRITICAL: we need to save the training class labels # but don't want to keep this label binarizer around; so, add it to the model model.layers[-1].attrs["classes"] = lb.classes_ Y_train = lb.transform([lang for _, lang in train]).astype("float32") Y_test = lb.transform([lang for _, lang in test]) # make sure data is on the right device? # Y_train = self.model.ops.asarray(Y_train) # Y_test = self.model.ops.asarray(Y_test) X_train = [text for text, _ in train] X_test = [text for text, _ in test] losser = thinc.api.CategoricalCrossentropy(normalize=True) optimizer = thinc.api.Adam(learn_rate) model.initialize(X=X_train[:10], Y=Y_train[:10]) print(f"{'epoch':>5} {'loss':>8} {'score':>8}") # iterate over epochs for n in range(n_iter): loss = 0.0 # iterate over batches batches = model.ops.multibatch(batch_size, X_train, Y_train, shuffle=True) for X, Y in tqdm(batches, leave=False): Yh, backprop = model.begin_update(X) dYh, loss_batch = losser(Yh, Y) loss += loss_batch backprop(dYh) model.finish_update(optimizer) optimizer.step_schedules() if optimizer.averages: with model.use_params(optimizer.averages): score = evaluate_model(model, X_test=X_test, Y_test=Y_test, batch_size=1000) else: score = evaluate_model(model, X_test=X_test, Y_test=Y_test, batch_size=1000) print(f"{n:>5} {loss:>8.3f} {score:>8.3f}") if optimizer.averages: with model.use_params(optimizer.averages): pred_langs = models.get_model_preds( model, X_test, model.layers[-1].attrs["classes"]) else: pred_langs = models.get_model_preds(model, X_test, model.layers[-1].attrs["classes"]) true_langs = list(lb.inverse_transform(Y_test)) print(sklearn.metrics.classification_report(true_langs, pred_langs)) return model