"""NCES architectures."""
from ontolearn.nces_modules import *
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class LSTM(nn.Module):
"""LSTM module."""
def __init__(self, knowledge_base_path, vocab, inv_vocab, max_length, input_size, proj_dim, rnn_n_layers,
drop_prob):
super().__init__()
self.name = 'LSTM'
self.max_len = max_length
self.proj_dim = proj_dim
self.vocab = vocab
self.inv_vocab = inv_vocab
self.loss = nn.CrossEntropyLoss()
self.lstm = nn.LSTM(input_size, proj_dim, rnn_n_layers, dropout=drop_prob, batch_first=True)
self.bn = nn.BatchNorm1d(proj_dim)
self.fc1 = nn.Linear(2*proj_dim, proj_dim)
self.fc2 = nn.Linear(proj_dim, proj_dim)
self.fc3 = nn.Linear(proj_dim, len(self.vocab)*max_length)
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def forward(self, x1, x2, target_scores=None):
seq1, _ = self.lstm(x1)
seq2, _ = self.lstm(x2)
out1 = seq1.sum(1).view(-1, self.proj_dim)
out2 = seq2.sum(1).view(-1, self.proj_dim)
x = torch.cat([out1, out2], 1)
x = F.gelu(self.fc1(x))
x = x + F.relu(self.fc2(x))
x = self.bn(x)
x = self.fc3(x)
x = x.reshape(-1, len(self.vocab), self.max_len)
aligned_chars = self.inv_vocab[x.argmax(1).cpu()]
return aligned_chars, x
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class GRU(nn.Module):
"""GRU module."""
def __init__(self, knowledge_base_path, vocab, inv_vocab, max_length, input_size, proj_dim, rnn_n_layers,
drop_prob):
super().__init__()
self.name = 'GRU'
self.max_len = max_length
self.proj_dim = proj_dim
self.vocab = vocab
self.inv_vocab = inv_vocab
self.loss = nn.CrossEntropyLoss()
self.gru = nn.GRU(input_size, proj_dim, rnn_n_layers, dropout=drop_prob, batch_first=True)
self.bn = nn.BatchNorm1d(proj_dim)
self.fc1 = nn.Linear(2*proj_dim, proj_dim)
self.fc2 = nn.Linear(proj_dim, proj_dim)
self.fc3 = nn.Linear(proj_dim, len(self.vocab)*max_length)
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def forward(self, x1, x2, target_scores=None):
seq1, _ = self.gru(x1)
seq2, _ = self.gru(x2)
out1 = seq1.sum(1).view(-1, self.proj_dim)
out2 = seq2.sum(1).view(-1, self.proj_dim)
x = torch.cat([out1, out2], 1)
x = F.gelu(self.fc1(x))
x = x + F.relu(self.fc2(x))
x = self.bn(x)
x = self.fc3(x)
x = x.reshape(-1, len(self.vocab), self.max_len)
aligned_chars = self.inv_vocab[x.argmax(1).cpu()]
return aligned_chars, x