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08b3b18 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 | import torch
import torch.nn as nn
import torch.nn.functional as F
from src.config import (
glove_dim, rnn_hidden, rnn_layers, dropout,
num_numeric_features, max_vocab_size,
cnn_filters, cnn_num_filters,
)
class NumericNet(nn.Module):
def __init__(self, num_features=num_numeric_features, hidden=128, dropout=dropout):
super().__init__()
self.net = nn.Sequential(
nn.Linear(num_features, hidden),
nn.ReLU(),
nn.BatchNorm1d(hidden),
nn.Dropout(dropout),
nn.Linear(hidden, hidden),
nn.ReLU(),
nn.Dropout(dropout),
)
def forward(self, x):
return self.net(x)
class BiGRU_LSTM(nn.Module):
def __init__(self, vocab_size=max_vocab_size, embed_dim=glove_dim,
rnn_hidden=rnn_hidden, rnn_layers=rnn_layers,
num_numeric=num_numeric_features, dropout=dropout,
pretrained_embeddings=None):
super().__init__()
self.embedding = nn.Embedding(vocab_size, embed_dim, padding_idx=0)
if pretrained_embeddings is not None:
self.embedding.weight = nn.Parameter(
torch.tensor(pretrained_embeddings, dtype=torch.float32), requires_grad=False)
self.bigru = nn.GRU(embed_dim, rnn_hidden, num_layers=rnn_layers,
batch_first=True, bidirectional=True,
dropout=dropout if rnn_layers > 1 else 0)
self.lstm = nn.LSTM(rnn_hidden * 2, rnn_hidden, num_layers=rnn_layers,
batch_first=True, dropout=dropout if rnn_layers > 1 else 0)
self.numeric_net = NumericNet(num_numeric, hidden=128, dropout=dropout)
fused_dim = 128
self.text_proj = nn.Linear(rnn_hidden, fused_dim)
self.gate = nn.Linear(fused_dim * 2, fused_dim)
self.dropout = nn.Dropout(dropout)
self.classifier = nn.Linear(fused_dim, 1)
def forward(self, input_ids, numeric):
embedded = self.embedding(input_ids)
gru_out, _ = self.bigru(embedded)
_, (h_n, _) = self.lstm(gru_out)
text_repr = self.text_proj(h_n.squeeze(0))
num_repr = self.numeric_net(numeric)
gate = torch.sigmoid(self.gate(torch.cat([text_repr, num_repr], dim=1)))
fused = gate * text_repr + (1 - gate) * num_repr
x = self.dropout(F.relu(fused))
return self.classifier(x)
class CNN_BiLSTM(nn.Module):
def __init__(self, vocab_size=max_vocab_size, embed_dim=glove_dim,
filter_sizes=cnn_filters, num_filters=cnn_num_filters,
rnn_hidden=rnn_hidden, rnn_layers=rnn_layers,
num_numeric=num_numeric_features, dropout=dropout,
pretrained_embeddings=None):
super().__init__()
self.embedding = nn.Embedding(vocab_size, embed_dim, padding_idx=0)
if pretrained_embeddings is not None:
self.embedding.weight = nn.Parameter(
torch.tensor(pretrained_embeddings, dtype=torch.float32), requires_grad=False)
self.convs = nn.ModuleList([nn.Conv1d(embed_dim, num_filters, fs) for fs in filter_sizes])
cnn_out = num_filters * len(filter_sizes)
self.bilstm = nn.LSTM(cnn_out, rnn_hidden, num_layers=rnn_layers,
batch_first=True, bidirectional=True,
dropout=dropout if rnn_layers > 1 else 0)
self.numeric_net = NumericNet(num_numeric, hidden=128, dropout=dropout)
fused_dim = 128
self.text_proj = nn.Linear(rnn_hidden * 2, fused_dim)
self.gate = nn.Linear(fused_dim * 2, fused_dim)
self.dropout = nn.Dropout(dropout)
self.classifier = nn.Linear(fused_dim, 1)
def forward(self, input_ids, numeric):
embedded = self.embedding(input_ids).permute(0, 2, 1)
conv_outs = [F.max_pool1d(F.relu(conv(embedded)), conv(embedded).size(2)).squeeze(2)
for conv in self.convs]
cnn_out = torch.cat(conv_outs, dim=1).unsqueeze(1)
_, (h_n, _) = self.bilstm(cnn_out)
text_repr = self.text_proj(torch.cat([h_n[-2], h_n[-1]], dim=1))
num_repr = self.numeric_net(numeric)
gate = torch.sigmoid(self.gate(torch.cat([text_repr, num_repr], dim=1)))
fused = gate * text_repr + (1 - gate) * num_repr
x = self.dropout(F.relu(fused))
return self.classifier(x) |