wehub-resource-sync
78 lines
2.5 KiB
Python
78 lines
2.5 KiB
Python
# %%
|
|
# code by Tae Hwan Jung @graykode
|
|
import torch
|
|
import torch.nn as nn
|
|
import torch.optim as optim
|
|
|
|
def make_batch():
|
|
input_batch = []
|
|
target_batch = []
|
|
|
|
for sen in sentences:
|
|
word = sen.split() # space tokenizer
|
|
input = [word_dict[n] for n in word[:-1]] # create (1~n-1) as input
|
|
target = word_dict[word[-1]] # create (n) as target, We usually call this 'casual language model'
|
|
|
|
input_batch.append(input)
|
|
target_batch.append(target)
|
|
|
|
return input_batch, target_batch
|
|
|
|
# Model
|
|
class NNLM(nn.Module):
|
|
def __init__(self):
|
|
super(NNLM, self).__init__()
|
|
self.C = nn.Embedding(n_class, m)
|
|
self.H = nn.Linear(n_step * m, n_hidden, bias=False)
|
|
self.d = nn.Parameter(torch.ones(n_hidden))
|
|
self.U = nn.Linear(n_hidden, n_class, bias=False)
|
|
self.W = nn.Linear(n_step * m, n_class, bias=False)
|
|
self.b = nn.Parameter(torch.ones(n_class))
|
|
|
|
def forward(self, X):
|
|
X = self.C(X) # X : [batch_size, n_step, m]
|
|
X = X.view(-1, n_step * m) # [batch_size, n_step * m]
|
|
tanh = torch.tanh(self.d + self.H(X)) # [batch_size, n_hidden]
|
|
output = self.b + self.W(X) + self.U(tanh) # [batch_size, n_class]
|
|
return output
|
|
|
|
if __name__ == '__main__':
|
|
n_step = 2 # number of steps, n-1 in paper
|
|
n_hidden = 2 # number of hidden size, h in paper
|
|
m = 2 # embedding size, m in paper
|
|
|
|
sentences = ["i like dog", "i love coffee", "i hate milk"]
|
|
|
|
word_list = " ".join(sentences).split()
|
|
word_list = list(set(word_list))
|
|
word_dict = {w: i for i, w in enumerate(word_list)}
|
|
number_dict = {i: w for i, w in enumerate(word_list)}
|
|
n_class = len(word_dict) # number of Vocabulary
|
|
|
|
model = NNLM()
|
|
|
|
criterion = nn.CrossEntropyLoss()
|
|
optimizer = optim.Adam(model.parameters(), lr=0.001)
|
|
|
|
input_batch, target_batch = make_batch()
|
|
input_batch = torch.LongTensor(input_batch)
|
|
target_batch = torch.LongTensor(target_batch)
|
|
|
|
# Training
|
|
for epoch in range(5000):
|
|
optimizer.zero_grad()
|
|
output = model(input_batch)
|
|
|
|
# output : [batch_size, n_class], target_batch : [batch_size]
|
|
loss = criterion(output, target_batch)
|
|
if (epoch + 1) % 1000 == 0:
|
|
print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.6f}'.format(loss))
|
|
|
|
loss.backward()
|
|
optimizer.step()
|
|
|
|
# Predict
|
|
predict = model(input_batch).data.max(1, keepdim=True)[1]
|
|
|
|
# Test
|
|
print([sen.split()[:2] for sen in sentences], '->', [number_dict[n.item()] for n in predict.squeeze()]) |