def objective(trial):
nl = trial.suggest_int("num_layers", 1, 2)
bi = trial.suggest_categorical("bidirectional", [False, True])
model = RNN(d_w, d_h, L, emb, num_layers=nl, bidirectional=bi)
lr = trial.suggest_loguniform("learning_rate", 1e-5, 1e-1)
bs = trial.suggest_categorical("batch_size", [32, -1])
model = run_train(train,
valid,
model,
epochs=11,
lr=lr,
batch_size=bs,
device=device)
loss_eval, acc_eval = run_eval(model, valid, device=device)
return acc_eval
from knock82 import run_eval, run_train
sys.path.append(os.path.join(os.path.dirname(__file__), "../../"))
from kiyuna.utils.pickle import load # noqa: E402 isort:skip
logging.basicConfig(level=logging.DEBUG)
d_w = 300
d_h = 50
V = get_V()
L = 4
if __name__ == "__main__":
train = torch.load("./data/train.pt")
valid = torch.load("./data/valid.pt")
test = torch.load("./data/test.pt")
device = torch.device("cuda:6")
emb = torch.Tensor(d_w, V).normal_()
wv = load("chap07-embeddings")
for i, word in enumerate(_list_valid_words()):
if word in wv:
wv_word = wv[word]
wv_word.flags["WRITEABLE"] = True
emb[:, i] = torch.from_numpy(wv_word)
rnn = RNN(d_w, d_h, L, emb, num_layers=2, bidirectional=True)
rnn = run_train(train, valid, rnn, epochs=11, lr=1e-1, batch_size=32, device=device)
loss, acc = run_eval(rnn, test, device=device)
print(f"Accuracy (test): {acc:f}, Loss (test): {loss:f}")