NEG loss implemented in pytorch

Related tags

Deep Learning python word2vec pytorch
Overview

Pytorch Negative Sampling Loss

Negative Sampling Loss implemented in PyTorch.

NEG Loss Equation

Usage

neg_loss = NEG_loss(num_classes, embedding_size)
    
optimizer = SGD(neg_loss.parameters(), 0.1)
    
for i in range(num_iterations):
    ''' 
    input is [batch_size] shaped tensors of Long type
    while target has shape of [batch_size, window_size]
    '''
    input, target = next_batch(batch_size)
        
    loss = neg_loss(input, target, num_sample)
    
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()
    
word_embeddings = neg_loss.input_embeddings()
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Comments
  • how does the negative sampling work?

    how does the negative sampling work?

    Hi, sorry for a newbie question, how does the negative sampling work in neg.py, in particular line 69? I cannot find any documentations on calling neg in nn.embedding...

    opened by marcoleewow 3
  • Why using two embedding layers?

    Why using two embedding layers?

    I noticed that two embedding layers are used in your code, 'out_embed' and 'in_embed', for output and input respectively, and only 'in_embed' weights are saved.

    But can they just share one embedding layer since they are in the same corpus？

    opened by nasusu 2
  • How is sum_log_sampled implemented?

    How is sum_log_sampled implemented?

    According to the original word2vec paper (https://arxiv.org/pdf/1310.4546.pdf), Negative sampling loss is implemented as: image

    Your implementation for the sum log sample is:

    sum_log_sampled = t.bmm(noise, input.unsqueeze(2)).sigmoid().log().sum(1).squeeze()

    It's the same as described in the paper except the minus sign, isn't it look like below

    sum_log_sampled = (-1* t.bmm(noise, input.unsqueeze(2))).sigmoid().log().sum(1).squeeze()

    Am I right ? https://github.com/kefirski/pytorch_NEG_loss/blob/master/NEG_loss/neg.py#L75

    opened by trungkak 3
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Daniil Gavrilov
The Last AI Bender
Daniil Gavrilov
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