def __init__(self,
num_classes,
max_length,
num_tokens,
embd,
emb_dim=300,
hidden_dim=100,
num_ways=3,
lr=0.001):
super(DistAttLSTM_Model, self).__init__()
self.max_length = max_length
self.num_tokens = num_tokens
self.hidden_dim = hidden_dim
self.num_classes = num_classes
self.att_dim = 50
self.depth = 10
self.num_att = num_classes
self.num_ways = num_ways
self.lstm = BiLSTM_Model(max_length, num_tokens, embd, emb_dim,
hidden_dim)
self.linear = nn.Linear(self.hidden_dim * 2,
self.num_ways * self.num_classes)
self.target_linear = nn.Linear(self.hidden_dim * 2,
self.num_att * self.num_ways)
self.target_linear_att = nn.Linear(self.hidden_dim * 2, 1)
self.loss_fn = nn.functional.cross_entropy
self.softmax = nn.Softmax()
self.sigmoid = nn.Sigmoid()
self.tanh = nn.Tanh()
self.dropout = nn.Dropout(0.1)
self.err = 1e-24
self.optimizer = optim.Adam(filter(lambda p: p.requires_grad,
self.parameters()),
lr=lr)
class DistAttLSTM_Model(nn.Module):
def __init__(self,
num_classes,
max_length,
num_tokens,
embd,
emb_dim=300,
hidden_dim=100,
num_ways=3,
lr=0.001):
super(DistAttLSTM_Model, self).__init__()
self.max_length = max_length
self.num_tokens = num_tokens
self.hidden_dim = hidden_dim
self.num_classes = num_classes
self.att_dim = 50
self.depth = 10
self.num_att = num_classes
self.num_ways = num_ways
self.lstm = BiLSTM_Model(max_length, num_tokens, embd, emb_dim,
hidden_dim)
self.linear = nn.Linear(self.hidden_dim * 2,
self.num_ways * self.num_classes)
self.target_linear = nn.Linear(self.hidden_dim * 2,
self.num_att * self.num_ways)
self.target_linear_att = nn.Linear(self.hidden_dim * 2, 1)
self.loss_fn = nn.functional.cross_entropy
self.softmax = nn.Softmax()
self.sigmoid = nn.Sigmoid()
self.tanh = nn.Tanh()
self.dropout = nn.Dropout(0.1)
self.err = 1e-24
self.optimizer = optim.Adam(filter(lambda p: p.requires_grad,
self.parameters()),
lr=lr)
def target_attention_forward(self, lstm_outputs, targets):
targets = target2distance(targets.data.numpy())
target_outputs = (targets.unsqueeze(2).expand_as(lstm_outputs) *
lstm_outputs).sum(1)
target_outputs = target_outputs.squeeze(1)
output_ = self.linear(target_outputs)
output = self.dropout(output_.view(len(output_), -1, self.num_ways))
output = self.softmax(output.view(-1, self.num_ways))
return output, output_.view(-1, self.num_ways)
def train_(self, x, y, targets, lengths):
self.zero_grad()
self.train()
lstm_outputs = self.lstm.forward(x, lengths)
output, output_ = self.target_attention_forward(lstm_outputs, targets)
y = y.view(-1)
loss = self.loss_fn(output_, y)
loss.backward()
self.optimizer.step()
return loss
def test(self, x, targets, lengths):
self.eval()
lstm_outputs = self.lstm.forward(x, lengths)
output, output_ = self.target_attention_forward(lstm_outputs, targets)
return output.view(-1, self.num_classes, self.num_ways).data.numpy()