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narm.py
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narm.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
class NARM(nn.Module):
"""Neural Attentive Session Based Recommendation Model Class
Args:
n_items(int): the number of items
hidden_size(int): the hidden size of gru
embedding_dim(int): the dimension of item embedding
batch_size(int):
n_layers(int): the number of gru layers
"""
def __init__(self, n_items, hidden_size, embedding_dim, batch_size, n_layers = 1):
super(NARM, self).__init__()
self.n_items = n_items
self.hidden_size = hidden_size
self.batch_size = batch_size
self.n_layers = n_layers
self.embedding_dim = embedding_dim
self.emb = nn.Embedding(self.n_items, self.embedding_dim, padding_idx = 0)
self.emb_dropout = nn.Dropout(0.25)
self.gru = nn.GRU(self.embedding_dim, self.hidden_size, self.n_layers)
self.a_1 = nn.Linear(self.hidden_size, self.hidden_size, bias=False)
self.a_2 = nn.Linear(self.hidden_size, self.hidden_size, bias=False)
self.v_t = nn.Linear(self.hidden_size, 1, bias=False)
self.ct_dropout = nn.Dropout(0.5)
self.b = nn.Linear(self.embedding_dim, 2 * self.hidden_size, bias=False)
#self.sf = nn.Softmax()
self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
def forward(self, seq, lengths):
hidden = self.init_hidden(seq.size(1))
embs = self.emb_dropout(self.emb(seq))
embs = pack_padded_sequence(embs, lengths)
gru_out, hidden = self.gru(embs, hidden)
gru_out, lengths = pad_packed_sequence(gru_out)
# fetch the last hidden state of last timestamp
ht = hidden[-1]
gru_out = gru_out.permute(1, 0, 2)
c_global = ht
q1 = self.a_1(gru_out.contiguous().view(-1, self.hidden_size)).view(gru_out.size())
q2 = self.a_2(ht)
mask = torch.where(seq.permute(1, 0) > 0, torch.tensor([1.], device = self.device), torch.tensor([0.], device = self.device))
q2_expand = q2.unsqueeze(1).expand_as(q1)
q2_masked = mask.unsqueeze(2).expand_as(q1) * q2_expand
alpha = self.v_t(torch.sigmoid(q1 + q2_masked).view(-1, self.hidden_size)).view(mask.size())
c_local = torch.sum(alpha.unsqueeze(2).expand_as(gru_out) * gru_out, 1)
c_t = torch.cat([c_local, c_global], 1)
c_t = self.ct_dropout(c_t)
item_embs = self.emb(torch.arange(self.n_items).to(self.device))
scores = torch.matmul(c_t, self.b(item_embs).permute(1, 0))
# scores = self.sf(scores)
return scores
def init_hidden(self, batch_size):
return torch.zeros((self.n_layers, batch_size, self.hidden_size), requires_grad=True).to(self.device)