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e8aab00 2e4aa16 e8aab00 2e4aa16 | 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 | import torch
import torch.nn as nn
from typing import Tuple
class Encoder(nn.Module):
def __init__(
self,
vocab_size: int,
embed_size: int,
hidden_size: int,
num_layers: int = 2,
dropout: float = 0.3
):
super(Encoder, self).__init__()
self.vocab_size = vocab_size
self.embed_size = embed_size
self.hidden_size = hidden_size
self.num_layers = num_layers
self.embedding = nn.Embedding(
num_embeddings=vocab_size,
embedding_dim=embed_size,
padding_idx=0
)
self.lstm = nn.LSTM(
input_size=embed_size,
hidden_size=hidden_size,
num_layers=num_layers,
batch_first=True,
dropout=dropout if num_layers > 1 else 0,
bidirectional=True
)
self.dropout = nn.Dropout(dropout)
self.layer_norm = nn.LayerNorm(hidden_size * 2)
def forward(
self,
input_seq: torch.Tensor,
input_lengths: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
embedded = self.dropout(self.embedding(input_seq))
packed_embedded = nn.utils.rnn.pack_padded_sequence(
embedded,
input_lengths.cpu(),
batch_first=True,
enforce_sorted=False
)
packed_output, (hidden, cell) = self.lstm(packed_embedded)
outputs, _ = nn.utils.rnn.pad_packed_sequence(
packed_output,
batch_first=True
)
outputs = self.layer_norm(outputs)
return outputs, hidden, cell
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