# Model
cbow = CBOW(vocab_size=wcd.vocab_size,
embed_dim=100)
# Training Parameters
n_epoch = 1000
learning_rate = 0.001
optimizer = optim.SGD(cbow.parameters(),
lr=learning_rate)
loss_fn = nn.NLLLoss()
loss_list = []
# Use GPU, if available.
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
cbow.to(device)
for epoch_i in range(n_epoch):
for batch_i, (X, Y) in enumerate(data_loader):
X, Y = X.to(device), Y.to(device)
cbow.zero_grad()
pred_log_prob = cbow(X)
loss = loss_fn(pred_log_prob, Y)
loss.backward()
loss_list.append(float(loss.to('cpu').data.numpy()))
optimizer.step()
print("loss : {}".format(loss))
def train(_=None,
corpus=None,
corpus_path=None,
context_size=2,
min_word=1,
embed_dim=100,
n_epoch=10,
batch_size=32,
learning_rate=0.001,
shuffle=True,
verbose_iterval=1):
if _:
raise Exception("Don't put parameters without keys. Set parameters with the key together.")
# Load data
wcd = WordContextDataset(corpus=corpus,
corpus_path=corpus_path,
context_size=context_size,
min_word=min_word)
data_loader = DataLoader(wcd,
batch_size=batch_size,
shuffle=shuffle)
# Model
cbow = CBOW(vocab_size=wcd.vocab_size,
embed_dim=embed_dim)
# Training Parameters
optimizer = optim.SGD(cbow.parameters(),
lr=learning_rate)
loss_fn = nn.NLLLoss()
loss_list = []
# Use GPU, if available.
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
cbow.to(device)
for epoch_i in range(n_epoch):
for batch_i, (X, Y) in enumerate(data_loader):
X, Y = X.to(device), Y.to(device)
cbow.zero_grad()
pred_log_prob = cbow(X)
loss = loss_fn(pred_log_prob, Y)
loss.backward()
loss_list.append(float(loss.to('cpu').data.numpy()))
optimizer.step()
if epoch_i % verbose_iterval == 0:
print("loss : {:.3f}".format(loss_list[-1]))
return {'wcd': wcd,
'cbow': cbow,
'loss_list': loss_list,
'data_loader': data_loader}