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import torch
import torch.nn as nn
import numpy as np
# Positional encoding
class PositionalEncoding(nn.Module):
def __init__(self, d_model, max_len=32):
super().__init__()
pe = torch.zeros(max_len, d_model)
pos = torch.arange(0, max_len).unsqueeze(1)
div = torch.exp(torch.arange(0, d_model, 2) * (-np.log(10000.0) / d_model))
pe[:, 0::2] = torch.sin(pos * div)
pe[:, 1::2] = torch.cos(pos * div)
self.pe = pe.unsqueeze(0)
def forward(self, x):
return x + self.pe[:, :x.size(1)].to(x.device)
# Transformer emotion classifier
class EmotionTransformer(nn.Module):
def __init__(self, vocab_size, embed_dim=64, num_heads=4, num_classes=5, dropout=0.3):
super().__init__()
self.embedding = nn.Embedding(vocab_size, embed_dim, padding_idx=0)
self.pos_encoder = PositionalEncoding(embed_dim)
encoder_layer = nn.TransformerEncoderLayer(
d_model=embed_dim, nhead=num_heads, batch_first=True
)
self.transformer = nn.TransformerEncoder(encoder_layer, num_layers=2)
self.dropout = nn.Dropout(dropout)
self.fc = nn.Linear(embed_dim, num_classes)
def forward(self, x):
mask = (x == 0)
x = self.embedding(x)
x = self.pos_encoder(x)
x = self.transformer(x, src_key_padding_mask=mask)
x = self.dropout(x.mean(dim=1))
return self.fc(x) |