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def test_pickle_vocab(text1, text2):
vocab = Vocab(lex_attr_getters={int(NORM): lambda string: string[:-1]})
vocab.set_vector("dog", numpy.ones((5,), dtype="f"))
lex1 = vocab[text1]
lex2 = vocab[text2]
assert lex1.norm_ == text1[:-1]
assert lex2.norm_ == text2[:-1]
data = srsly.pickle_dumps(vocab)
unpickled = srsly.pickle_loads(data)
assert unpickled[text1].orth == lex1.orth
assert unpickled[text2].orth == lex2.orth
assert unpickled[text1].norm == lex1.norm
assert unpickled[text2].norm == lex2.norm
assert unpickled[text1].norm != unpickled[text2].norm
assert unpickled.vectors is not None
assert list(vocab["dog"].vector) == [1.0, 1.0, 1.0, 1.0, 1.0]
def test_pickle_string_store(text1, text2):
stringstore = StringStore()
store1 = stringstore[text1]
store2 = stringstore[text2]
data = srsly.pickle_dumps(stringstore, protocol=-1)
unpickled = srsly.pickle_loads(data)
assert unpickled[text1] == store1
assert unpickled[text2] == store2
assert len(stringstore) == len(unpickled)
def train_epoch(
model, sgd, hparams, train_X, train_y, dev_X, dev_y, device_id=-1, temperature=0.0
):
model, sgd, hparams = srsly.pickle_loads(srsly.pickle_dumps((model, sgd, hparams)))
if device_id >= 0:
model.to_gpu(device_id)
sgd.ops = model.ops
sgd.to_gpu()
if isinstance(train_y, numpy.ndarray):
train_y = model.ops.asarray(train_y)
dev_y = model.ops.asarray(dev_y)
hparams = resample_hyper_params(hparams, temperature)
sgd.learn_rate = hparams["learn_rate"]
sgd.beta1 = hparams["beta1"]
sgd.beta2 = hparams["beta2"]
sgd.L2 = hparams["L2"]
train_acc = 0.0
train_n = 0
for X, y in minibatch(
train_X, train_y, size=hparams["batch_size"], nr_update=hparams["nr_update"]
def __setstate__(self, state_data: bytes) -> None:
self.__dict__ = srsly.pickle_loads(state_data)
def __setstate__(self, state_data):
self.__dict__ = srsly.pickle_loads(state_data)