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import torch
import torch.nn as nn
import torch.nn.functional as F
class StrongTextCNN(nn.Module):
def __init__(
self,
vocab_size,
embed_dim=300,
num_filters=128,
dropout=0.4
):
super().__init__()
self.embedding = nn.Embedding(
vocab_size,
embed_dim,
padding_idx=0
)
self.convs = nn.ModuleList([
nn.Conv1d(embed_dim, num_filters, k)
for k in [2,3,4,5]
])
self.bn = nn.BatchNorm1d(num_filters * 4)
self.fc1 = nn.Linear(num_filters * 4, 256)
self.fc2 = nn.Linear(256, 64)
self.out = nn.Linear(64, 1)
self.dropout = nn.Dropout(dropout)
def forward(self, x):
x = self.embedding(x)
x = x.permute(0,2,1)
conv_outputs = []
for conv in self.convs:
c = F.relu(conv(x))
p = F.max_pool1d(
c,
kernel_size=c.shape[2]
).squeeze(2)
conv_outputs.append(p)
x = torch.cat(conv_outputs, dim=1)
x = self.bn(x)
x = self.dropout(x)
x = F.relu(self.fc1(x))
x = self.dropout(x)
x = F.relu(self.fc2(x))
x = self.dropout(x)
x = self.out(x)
return x.squeeze(1)
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