def get_tok2vec_width(model: Model):
nO = None
if model.has_ref("tok2vec"):
tok2vec = model.get_ref("tok2vec")
if tok2vec.has_dim("nO"):
nO = tok2vec.get_dim("nO")
elif tok2vec.has_ref("listener"):
nO = tok2vec.get_ref("listener").get_dim("nO")
return nO
def debug_model(
config,
resolved_train_config,
nlp,
model: Model,
*,
print_settings: Optional[Dict[str, Any]] = None,
):
if not isinstance(model, Model):
msg.fail(
f"Requires a Thinc Model to be analysed, but found {type(model)} instead.",
exits=1,
)
if print_settings is None:
print_settings = {}
# STEP 0: Printing before training
msg.info(f"Analysing model with ID {model.id}")
if print_settings.get("print_before_training"):
msg.divider(f"STEP 0 - before training")
_print_model(model, print_settings)
# STEP 1: Initializing the model and printing again
X = _get_docs()
# The output vector might differ from the official type of the output layer
with data_validation(False):
try:
dot_names = [resolved_train_config["train_corpus"]]
with show_validation_error():
(train_corpus, ) = resolve_dot_names(config, dot_names)
nlp.initialize(lambda: train_corpus(nlp))
msg.info("Initialized the model with the training corpus.")
except ValueError:
try:
_set_output_dim(nO=7, model=model)
with show_validation_error():
nlp.initialize(
lambda: [Example.from_dict(x, {}) for x in X])
msg.info("Initialized the model with dummy data.")
except Exception:
msg.fail(
"Could not initialize the model: you'll have to provide a valid train_corpus argument in the config file.",
exits=1,
)
if print_settings.get("print_after_init"):
msg.divider(f"STEP 1 - after initialization")
_print_model(model, print_settings)
# STEP 2: Updating the model and printing again
optimizer = Adam(0.001)
set_dropout_rate(model, 0.2)
# ugly hack to deal with Tok2Vec listeners
tok2vec = None
if model.has_ref("tok2vec") and model.get_ref(
"tok2vec").name == "tok2vec-listener":
tok2vec = nlp.get_pipe("tok2vec")
goldY = None
for e in range(3):
if tok2vec:
tok2vec.update([Example.from_dict(x, {}) for x in X])
Y, get_dX = model.begin_update(X)
if goldY is None:
goldY = _simulate_gold(Y)
dY = get_gradient(goldY, Y, model.ops)
get_dX(dY)
model.finish_update(optimizer)
if print_settings.get("print_after_training"):
msg.divider(f"STEP 2 - after training")
_print_model(model, print_settings)
# STEP 3: the final prediction
prediction = model.predict(X)
if print_settings.get("print_prediction"):
msg.divider(f"STEP 3 - prediction")
msg.info(str(prediction))
msg.good(f"Succesfully ended analysis - model looks good.")
def test_model_init():
class MyShim(Shim):
name = "testshim"
model_a = create_model("a")
model = Model(
"test",
lambda X: (X, lambda dY: dY),
dims={
"nI": 10,
"nO": None
},
params={
"W": numpy.zeros((10, )),
"b": None
},
refs={
"a": model_a,
"b": None
},
attrs={"foo": "bar"},
shims=[MyShim(None)],
layers=[model_a, model_a],
)
assert model.has_param("W")
assert model.get_param("W").shape == (10, )
assert model.has_param("b") is None
with pytest.raises(KeyError):
model.get_param("b")
with pytest.raises(KeyError):
model.get_param("X")
model.set_param("X", numpy.zeros((10, )))
assert model.has_param("X")
assert model.get_param("X").shape == (10, )
with model.use_params({(model.id, "X"): numpy.ones((10, ))}):
assert numpy.array_equal(model.get_param("X"), numpy.ones((10, )))
assert numpy.array_equal(model.get_param("X"), numpy.zeros((10, )))
assert not model.has_grad("W")
assert not model.has_grad("xyz")
with pytest.raises(KeyError):
model.get_grad("b")
model.set_param("W", model.ops.alloc1f(10))
model.set_grad("W", model.ops.alloc1f(10))
with pytest.raises(ValueError):
model.inc_grad("W", numpy.zeros((5, 0)))
assert model.has_dim("nI")
assert model.get_dim("nI") == 10
with pytest.raises(KeyError):
model.get_dim("xyz")
with pytest.raises(ValueError):
model.get_dim("nO")
with pytest.raises(KeyError):
model.set_dim("xyz", 20)
with pytest.raises(ValueError):
model.set_dim("nI", 20)
assert model.has_ref("a")
assert model.get_ref("a").name == "a"
assert not model.has_ref("xyz")
with pytest.raises(KeyError):
model.get_ref("xyz")
assert model.has_ref("b") is None
with pytest.raises(ValueError):
model.get_ref("b")
model.set_ref("c", model_a)
assert model.has_ref("c")
assert model.get_ref("c").name == "a"
with pytest.raises(ValueError):
model.set_ref("c", create_model("c"))
assert "foo" in model.attrs
assert "bar" not in model.attrs
assert model.attrs["foo"] == "bar"
with pytest.raises(KeyError):
model.attrs["bar"]
model.attrs["bar"] = "baz"
model_copy = model.copy()
assert model_copy.name == "test"
