pytorch ---神经网络语言模型 NNLM 《A Neural Probabilistic Language Model》

论文地址：http://www.iro.umontreal.ca/~vincentp/Publications/lm_jmlr.pdf

论文给出了NNLM的框架图：

　　　　　　

针对论文，实现代码如下（https://github.com/graykode/nlp-tutorial）：

 # -*- coding: utf-8 -*-

 # @time : 2019/10/26  12:20

 import numpy as np

 import torch

 import torch.nn as nn

 import torch.optim as optim

 from torch.autograd import Variable

 dtype = torch.FloatTensor

 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)} # {'i': 0, 'like': 1, 'love': 2, 'hate': 3, 'milk': 4, 'dog': 5, 'coffee': 6}}

 number_dict = {i: w for i, w in enumerate(word_list)}

 n_class = len(word_dict) # number of Vocabulary

 # NNLM Parameter

 n_step = 2 # n-1 in paper    ->3gram

 n_hidden = 2 # h in paper   ->number hidden unit

 m = 2 # m in paper   ->embedding size

 # make data batch (input,target)

 # input: [[0,1],[0,2],[0,3]]

 # target: [5,6,4]

 def make_batch(sentences):

     input_batch = []

     target_batch = []

     for sen in sentences:

         word = sen.split()

         input = [word_dict[n] for n in word[:-1]]

         target = word_dict[word[-1]]

         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.Parameter(torch.randn(n_step * m, n_hidden).type(dtype))

         self.W = nn.Parameter(torch.randn(n_step * m, n_class).type(dtype))

         self.d = nn.Parameter(torch.randn(n_hidden).type(dtype))

         self.U = nn.Parameter(torch.randn(n_hidden, n_class).type(dtype))

         self.b = nn.Parameter(torch.randn(n_class).type(dtype))

     def forward(self, X):

         X = self.C(X)

         X = X.view(-1, n_step * m) # [batch_size, n_step * m]

         tanh = torch.tanh(self.d + torch.mm(X, self.H)) # [batch_size, n_hidden]

         output = self.b + torch.mm(X, self.W) + torch.mm(tanh, self.U) # [batch_size, n_class]

         return output

 model = NNLM()

 criterion = nn.CrossEntropyLoss()

 optimizer = optim.Adam(model.parameters(), lr=0.001)

 input_batch, target_batch = make_batch(sentences)

 input_batch = Variable(torch.LongTensor(input_batch))

 target_batch = Variable(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] (LongTensor, not one-hot)

     loss = criterion(output, target_batch)

     if (epoch + 1)%1000 == 0:

         print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.6f}'.format(loss))

     loss.backward()

     optimizer.step()

 # Predict [5,6,4]   (equal with target)

 predict = model(input_batch).data.max(1, keepdim=True)[1]

 # print to visual

 print([sen.split()[:2] for sen in sentences], '->', [number_dict[n.item()] for n in predict.squeeze()])