out = self.fc(inp) return o
代码
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
torch.manual_seed(1)
CONTEXT_SIZE = 2
EMBEDDING_DIM = 30
EPOCH = 100
VERVOSE = 5
corpus_text = "This tutorial will walk you through the key ideas of deep learning programming using Pytorch."
" Many of the concepts (such as the computation graph abstraction and autograd) "
"are not unique to Pytorch and are relevant to any deep learning tool kit out there.".split(' ')
class CBOW(nn.Module):
def __init__(self, vocab_size, embedding_size, context_size):
super(CBOW, self).__init__()
self.vocab_size = vocab_size
self.embedding_size = embedding_size
self.context_size = context_size
self.embeddings = nn.Embedding(self.vocab_size, self.embedding_size)
self.lin1 = nn.Linear(self.context_size * 2 * self.embedding_size, 512)
self.lin2 = nn.Linear(512, self.vocab_size)
def forward(self, inp):
out = self.embeddings(inp).view(1, -1)
out = out.view(1, -1)
out = self.lin1(out)
out = F.relu(out)
out = self.lin2(out)
out = F.log_softmax(out, dim=1)
return out
def get_word_vector(self, word_idx):
word = torch.LongTensor([word_idx])
print('self.embeddings = ', next(self.embeddings.parameters()))
return self.embeddings(word).view(1, -1)
def train_cbow(data, unique_vocab, word_to_idx):
cbow = CBOW(len(unique_vocab), EMBEDDING_DIM, CONTEXT_SIZE)
nll_loss = nn.NLLLoss()
optimizer = optim.SGD(cbow.parameters(), lr=0.001)
print(len(data))
for epoch in range(EPOCH):
total_loss = 0
for context, target in data:
inp_var = torch.tensor([word_to_idx[word] for word in context], dtype=torch.long)
target_var = torch.tensor([word_to_idx[target]], dtype=torch.long)
cbow.zero_grad()
log_prob = cbow(inp_var)
loss = nll_loss(log_prob, target_var)
loss.backward()
optimizer.step()
total_loss += loss.data
if epoch % VERVOSE == 0:
loss_avg = float(total_loss / len(data))
print("{}/{} loss {:.2f}".format(epoch, EPOCH, loss_avg))
return cbow
def main():
data = list()
for i in range(CONTEXT_SIZE, len(corpus_text) - CONTEXT_SIZE):
data_context = list()
for j in range(CONTEXT_SIZE):
data_context.append(corpus_text[i-CONTEXT_SIZE+j])
for j in range(1, CONTEXT_SIZE+1):
data_context.append(corpus_text[i+j])
data_target = corpus_text[i]
data.append((data_context, data_target))
print("some data: ", data[:3])
unique_vocab = list(set(corpus_text))
word_to_idx = {w:i for i, w in enumerate(unique_vocab)}
print('word_to_idx: ', word_to_idx)
cbow = train_cbow(data, unique_vocab, word_to_idx)
if __name__ == "__main__":
main()
结果
some data: [(['This', 'tutorial', 'walk', 'you'], 'will'), (['tutorial', 'will', 'you', 'through'], 'walk'), (['will', 'walk', 'through', 'the'], 'you')]
word_to_idx: {'using': 0, 'relevant': 1, 'unique': 2, 'tutorial': 3, 'concepts': 4, 'graph': 5, 'Many': 6, 'the': 7, 'are': 8, 'deep': 9, 'key': 10, 'ideas': 11, 'there.': 12, 'out': 13, 'Pytorch': 14, 'learning': 15, 'of': 16, 'not': 17, 'autograd)': 18, 'tool': 19, 'will': 20, '(such': 21, 'you': 22, 'This': 23, 'through': 24, 'Pytorch.': 25, 'as': 26, 'kit': 27, 'any': 28, 'computation': 29, 'and': 30, 'to': 31, 'abstraction': 32, 'programming': 33, 'walk': 34}
39
0/100 loss 3.60
5/100 loss 3.14
10/100 loss 2.73
15/100 loss 2.33
20/100 loss 1.97
25/100 loss 1.65
30/100 loss 1.36
35/100 loss 1.11
40/100 loss 0.91
45/100 loss 0.74
50/100 loss 0.61
55/100 loss 0.51
60/100 loss 0.42
65/100 loss 0.36
70/100 loss 0.31
75/100 loss 0.27
80/100 loss 0.24
85/100 loss 0.21
90/100 loss 0.19
95/100 loss 0.17
Process finished with exit code 0
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