mathematician={ "can run" : 2.3, "likes coffee" : 9.4,"majored in Physics" : ?5.5,…}
physicist={ "can run" : 2.5 "likes coffee" : 9.1,"majored in Physics" : 6.4,…}

import torch
import torch.nn as nn

word_to_ix = {"hello": 0, "world": 1, "nice": 2, "to": 3, "see": 4, "you": 5}
embeds = nn.Embedding(6, 2)  # 構(gòu)建模型
lookup_tensor = torch.tensor([word_to_ix["nice"]], dtype=torch.long)
print(lookup_tensor)
nice_embed = embeds(lookup_tensor)     # 進(jìn)行編碼
print(nice_embed)
'''
tensor([2])
tensor([[ 0.7950, -0.3999]], grad_fn=<EmbeddingBackward>)
'''

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim

CONTEXT_SIZE = 2
EMBEDDING_DIM = 10  # 編碼向量的維度

test_sentence = """When forty winters shall besiege thy brow,
And dig deep trenches in thy beauty's field,
Thy youth's proud livery so gazed on now,
Will be a totter'd weed of small worth held:
Then being asked, where all thy beauty lies,
Where all the treasure of thy lusty days;
To say, within thine own deep sunken eyes,
Were an all-eating shame, and thriftless praise.
How much more praise deserv'd thy beauty's use,
If thou couldst answer 'This fair child of mine
Shall sum my count, and make my old excuse,'
Proving his beauty by succession thine!
This were to be new made when thou art old,
And see thy blood warm when thou feel'st it cold.""".split()
# 構(gòu)建訓(xùn)練集數(shù)據(jù) ([ 第一個(gè)單詞, 第二個(gè)單詞 ], 預(yù)測(cè)目標(biāo))
trigrams = [([test_sentence[i], test_sentence[i + 1]], test_sentence[i + 2])
            for i in range(len(test_sentence) - 2)]
# 構(gòu)建測(cè)試集數(shù)據(jù)
vocab = set(test_sentence)
word_to_ix = {word: i for i, word in enumerate(vocab)}

# 定義模型
class NGramLanguageModeler(nn.Module):

    def __init__(self, vocab_size, embedding_dim, context_size):
        super(NGramLanguageModeler, self).__init__()
        self.embeddings = nn.Embedding(vocab_size, embedding_dim)
        self.linear1 = nn.Linear(context_size * embedding_dim, 128)
        self.linear2 = nn.Linear(128, vocab_size)

    def forward(self, inputs):
        embeds = self.embeddings(inputs).view((1, -1))  # 進(jìn)行embedding
        out = F.relu(self.linear1(embeds))  # 經(jīng)過第一個(gè)全連接層
        out = self.linear2(out)  # 經(jīng)過第二個(gè)全連接層
        log_probs = F.log_softmax(out, dim=1)
        return log_probs

# 進(jìn)行訓(xùn)練
losses = []
loss_function = nn.NLLLoss()
model = NGramLanguageModeler(len(vocab), EMBEDDING_DIM, CONTEXT_SIZE)
optimizer = optim.SGD(model.parameters(), lr=0.001)

for epoch in range(10):
    total_loss = 0
    for context, target in trigrams:
        # 準(zhǔn)備輸入模型的數(shù)據(jù)
        context_idxs = torch.tensor([word_to_ix[w] for w in context], dtype=torch.long)

        model.zero_grad()  # 清零梯度緩存

        # 進(jìn)行訓(xùn)練得到預(yù)測(cè)結(jié)果
        log_probs = model(context_idxs)

        # 計(jì)算損失值
        loss = loss_function(log_probs, torch.tensor([word_to_ix[target]], dtype=torch.long))

        # 反向傳播更新梯度
        loss.backward()
        optimizer.step()

        total_loss += loss.item()  # 累計(jì)損失
    losses.append(total_loss)
print(losses)