class AttenCNN(nn.Module): def __init__(self, vocab, num_classes, char_alphabet): super(AttenCNN,self).__init__() self.embed_size = opt.word_emb_size self.embedding = vocab.init_embed_layer() self.hidden_size = opt.hidden_size self.char_hidden_dim = 10 self.char_embedding_dim = 20 self.char_feature = CharCNN(len(char_alphabet), None, self.char_embedding_dim, self.char_hidden_dim, opt.dropout, opt.gpu) self.input_size = self.embed_size + self.char_hidden_dim self.W = nn.Linear(self.input_size, 1, bias=False) self.hidden = nn.Linear(self.input_size, self.hidden_size) self.out = nn.Linear(self.hidden_size, num_classes) self.dropout = nn.Dropout(opt.dropout) def forward(self, input, char_inputs): """ inputs: (unpacked_padded_output: batch_size x seq_len x hidden_size, lengths: batch_size) """ entity_words, _, entity_lengths, entity_seq_recover = input entity_words = autograd.Variable(entity_words) entity_words_embeds = self.embedding(entity_words) batch_size, max_len, _ = entity_words_embeds.size() char_inputs, _, char_seq_lengths, char_seq_recover = char_inputs char_features = self.char_feature.get_last_hiddens(char_inputs) char_features = char_features[char_seq_recover] char_features = char_features.view(batch_size, max_len, -1) input_embeds = torch.cat((entity_words_embeds, char_features), 2) flat_input = input_embeds.contiguous().view(-1, self.input_size) logits = self.W(flat_input).view(batch_size, max_len) alphas = functional.softmax(logits, dim=1) # computing mask tmp = torch.LongTensor(max_len) if opt.gpu >= 0 and torch.cuda.is_available(): tmp = tmp.cuda(opt.gpu) idxes = torch.arange(0, max_len, out=tmp).unsqueeze(0) # idxes = torch.arange(0, max_len, out=torch.LongTensor(max_len)).unsqueeze(0).cuda(opt.gpu) mask = autograd.Variable((idxes < entity_lengths.unsqueeze(1)).float()) alphas = alphas * mask alphas = alphas / torch.sum(alphas, 1).view(-1, 1) atten_input = torch.bmm(alphas.unsqueeze(1), input_embeds).squeeze(1) atten_input = self.dropout(atten_input) hidden = self.hidden(atten_input) output = self.out(hidden) return output
class WordRep(nn.Module):
def __init__(self, data):
super(WordRep, self).__init__()
self.char_hidden_dim = data.char_hidden_dim # 50
self.char_embedding_dim = data.char_emb_dim # 300
self.char_feature = CharCNN(data.char_alphabet_size,
data.pretrain_char_embedding,
self.char_embedding_dim,
self.char_hidden_dim, data.dropout)
self.drop = nn.Dropout(data.dropout)
self.word_embedding = nn.Embedding(data.word_alphabet_size,
data.word_emb_dim)
self.word_embedding.weight.data.copy_(
torch.from_numpy(
self.random_embedding(data.word_alphabet_size,
data.word_emb_dim)))
self.feature_embedding = nn.Embedding(data.feat_alphabet_size,
data.feature_emb_dim)
self.feature_embedding.weight.data.copy_(
torch.from_numpy(data.pretrain_feature_embeddings))
def random_embedding(self, vocab_size, embedding_dim):
pretrain_emb = np.empty([vocab_size, embedding_dim])
scale = np.sqrt(3.0 / embedding_dim)
for index in range(vocab_size):
pretrain_emb[index, :] = np.random.uniform(-scale, scale,
[1, embedding_dim])
return pretrain_emb
def forward(self, word_inputs, feature_inputs, word_seq_lengths,
char_inputs, char_seq_lengths, char_seq_recover):
batch_size = word_inputs.size(0)
sent_len = word_inputs.size(1)
word_embs = self.word_embedding(word_inputs)
word_list = [word_embs]
word_list.append(self.feature_embedding(feature_inputs))
char_features = self.char_feature.get_last_hiddens(char_inputs)
char_features = char_features[char_seq_recover]
char_features = char_features.view(batch_size, sent_len, -1)
word_list.append(char_features)
word_embs = torch.cat(word_list, 2)
word_represent = self.drop(word_embs)
return word_represent
class WordRep(nn.Module):
def __init__(self, data):
super(WordRep, self).__init__()
print("build word representation...")
self.gpu = data.HP_gpu
self.use_char = data.use_char
self.use_trans = data.use_trans
self.batch_size = data.HP_batch_size
self.char_hidden_dim = 0
self.char_all_feature = False
self.w = nn.Linear(data.word_emb_dim, data.HP_trans_hidden_dim)
if self.use_trans:
self.trans_hidden_dim = data.HP_trans_hidden_dim
self.trans_embedding_dim = data.trans_emb_dim
self.trans_feature = TransBiLSTM(data.translation_alphabet.size(),
self.trans_embedding_dim,
self.trans_hidden_dim,
data.HP_dropout,
data.pretrain_trans_embedding,
self.gpu)
if self.use_char:
self.char_hidden_dim = data.HP_char_hidden_dim
self.char_embedding_dim = data.char_emb_dim
if data.char_seq_feature == "CNN":
self.char_feature = CharCNN(data.char_alphabet.size(),
self.char_embedding_dim,
self.char_hidden_dim,
data.HP_dropout, self.gpu)
elif data.char_seq_feature == "LSTM":
self.char_feature = CharBiLSTM(data.char_alphabet.size(),
self.char_embedding_dim,
self.char_hidden_dim,
data.HP_dropout,
data.pretrain_char_embedding,
self.gpu)
elif data.char_seq_feature == "GRU":
self.char_feature = CharBiGRU(data.char_alphabet.size(),
self.char_embedding_dim,
self.char_hidden_dim,
data.HP_dropout, self.gpu)
elif data.char_seq_feature == "ALL":
self.char_all_feature = True
self.char_feature = CharCNN(data.char_alphabet.size(),
self.char_embedding_dim,
self.char_hidden_dim,
data.HP_dropout, self.gpu)
self.char_feature_extra = CharBiLSTM(data.char_alphabet.size(),
self.char_embedding_dim,
self.char_hidden_dim,
data.HP_dropout, self.gpu)
else:
print(
"Error char feature selection, please check parameter data.char_seq_feature (CNN/LSTM/GRU/ALL)."
)
exit(0)
self.embedding_dim = data.word_emb_dim
self.drop = nn.Dropout(data.HP_dropout)
self.word_embedding = nn.Embedding(data.word_alphabet.size(),
self.embedding_dim)
if data.pretrain_word_embedding is not None:
self.word_embedding.weight.data.copy_(
torch.from_numpy(data.pretrain_word_embedding))
else:
self.word_embedding.weight.data.copy_(
torch.from_numpy(
self.random_embedding(data.word_alphabet.size(),
self.embedding_dim)))
self.feature_num = data.feature_num
self.feature_embedding_dims = data.feature_emb_dims
self.feature_embeddings = nn.ModuleList()
for idx in range(self.feature_num):
self.feature_embeddings.append(
nn.Embedding(data.feature_alphabets[idx].size(),
self.feature_embedding_dims[idx]))
for idx in range(self.feature_num):
if data.pretrain_feature_embeddings[idx] is not None:
self.feature_embeddings[idx].weight.data.copy_(
torch.from_numpy(data.pretrain_feature_embeddings[idx]))
else:
self.feature_embeddings[idx].weight.data.copy_(
torch.from_numpy(
self.random_embedding(
data.feature_alphabets[idx].size(),
self.feature_embedding_dims[idx])))
if self.gpu:
self.drop = self.drop.cuda()
self.word_embedding = self.word_embedding.cuda()
for idx in range(self.feature_num):
self.feature_embeddings[idx] = self.feature_embeddings[
idx].cuda()
def random_embedding(self, vocab_size, embedding_dim):
pretrain_emb = np.empty([vocab_size, embedding_dim])
scale = np.sqrt(3.0 / embedding_dim)
for index in range(vocab_size):
pretrain_emb[index, :] = np.random.uniform(-scale, scale,
[1, embedding_dim])
return pretrain_emb
def forward(self, word_inputs, feature_inputs, word_seq_lengths,
char_inputs, char_seq_lengths, char_seq_recover, trans_inputs,
trans_seq_length, trans_seq_recover):
"""
input:
word_inputs: (batch_size, sent_len)
features: list [(batch_size, sent_len), (batch_len, sent_len),...]
word_seq_lengths: list of batch_size, (batch_size,1)
char_inputs: (batch_size*sent_len, word_length)
char_seq_lengths: list of whole batch_size for char, (batch_size*sent_len, 1)
char_seq_recover: variable which records the char order information, used to recover char order
output:
Variable(batch_size, sent_len, hidden_dim)
"""
batch_size = word_inputs.size(0)
sent_len = word_inputs.size(1)
word_embs = self.word_embedding(word_inputs)
word_list = [word_embs]
for idx in range(self.feature_num):
word_list.append(self.feature_embeddings[idx](feature_inputs[idx]))
if self.use_char:
# calculate char lstm last hidden
char_features, _ = self.char_feature.get_last_hiddens(
char_inputs,
char_seq_lengths.cpu().numpy())
char_features = char_features[char_seq_recover]
char_features = char_features.view(batch_size, sent_len, -1)
# concat word and char together
word_list.append(char_features)
# word_embs = torch.cat([word_embs, char_features], 2)
if self.char_all_feature:
char_features_extra, _ = self.char_feature_extra.get_last_hiddens(
char_inputs,
char_seq_lengths.cpu().numpy())
char_features_extra = char_features_extra[char_seq_recover]
char_features_extra = char_features_extra.view(
batch_size, sent_len, -1)
# concat word and char together
word_list.append(char_features_extra)
if self.use_trans:
trans_features, trans_rnn_length = self.trans_feature.get_last_hiddens(
trans_inputs,
trans_seq_length.cpu().numpy())
trans_features_wc = trans_features
if self.gpu:
trans_features_wc.cuda()
trans_features_wc = trans_features_wc[trans_seq_recover]
trans_inputs = trans_inputs[trans_seq_recover]
word_embs_temp = word_embs.view(batch_size * sent_len, -1)
for index, line in enumerate(trans_inputs):
if line[0].data.cpu().numpy()[0] == 0:
trans_features_wc[index] = self.w(word_embs_temp[index])
trans_features_wc_temp = trans_features_wc
trans_features_wc = trans_features_wc.view(batch_size, sent_len,
-1)
word_list.append(trans_features_wc)
word_embs = torch.cat(word_list, 2)
word_represent = self.drop(word_embs)
return word_represent, self.w(word_embs_temp), trans_features_wc_temp
class CNNCNN_SentLSTM(nn.Module): def __init__(self, vocab, num_classes, char_alphabet): super(CNNCNN_SentLSTM,self).__init__() self.embedding = vocab.init_embed_layer() self.hidden_size = opt.hidden_size # charcnn self.char_hidden_dim = 10 self.char_embedding_dim = 20 self.char_feature = CharCNN(len(char_alphabet), None, self.char_embedding_dim, self.char_hidden_dim, opt.dropout, opt.gpu) self.embedding_size = self.embedding.weight.size(1) self.hidden_size = opt.hidden_size Ci = 1 Co = opt.kernel_num Ks = [int(k) for k in list(opt.kernel_sizes) if k != ","] # mention char_cnn D = self.embedding_size + self.char_hidden_dim self.convs1 = nn.ModuleList([nn.Conv2d(in_channels=Ci, out_channels=Co, kernel_size=(K, D), stride=(1, 1), padding=(K // 2, 0), dilation=1, bias=False) for K in Ks]) self.mention_hidden = nn.Linear(len(Ks) * Co, self.hidden_size) #sentence lstm self.lstm_hidden = opt.hidden_size self.lstm = nn.GRU(self.embedding_size, self.lstm_hidden, num_layers=1, batch_first=True, bidirectional=True) self.sent_hidden_size = opt.sent_hidden_size self.sent_hidden = nn.Linear(self.lstm_hidden*2, self.sent_hidden_size) self.hidden = nn.Linear(self.hidden_size + self.sent_hidden_size, self.hidden_size) # mention_hidden_size + sentence_hidden_size self.out = nn.Linear(self.hidden_size, num_classes) self.dropout = nn.Dropout(opt.dropout) def conv_and_pool(self, x, conv): x = F.relu(conv(x)).squeeze(3) # (N, Co, W) x = F.max_pool1d(x, x.size(2)).squeeze(2) return x def forward(self, mention_inputs, char_inputs, sent_inputs): inputs, lengths, seq_recover = mention_inputs mention_embedding = self.embedding(inputs) # (N, W, D) batch_size, max_len = inputs.size() char_inputs, char_seq_lengths, char_seq_recover = char_inputs char_features = self.char_feature.get_last_hiddens(char_inputs) char_features = char_features[char_seq_recover] char_features = char_features.view(batch_size, max_len, -1) mention_char = torch.cat((mention_embedding, char_features), 2) mention_char = mention_char.unsqueeze(1) # (N, Ci, W, D) mention_char = [F.relu(conv(mention_char)).squeeze(3) for conv in self.convs1] # [(N, Co, W), ...]*len(Ks) mention_char = [F.max_pool1d(i, i.size(2)).squeeze(2) for i in mention_char] # [(N, Co), ...]*len(Ks) mention_char = torch.cat(mention_char, 1) mention_hidden = self.mention_hidden(mention_char) sent_inputs, sent_seq_lengths = sent_inputs sent_embedding = self.embedding(sent_inputs) packed_words = pack_padded_sequence(sent_embedding, sent_seq_lengths.cpu().numpy(), True) hidden = None lstm_out, hidden = self.lstm(packed_words, hidden) lstm_out, _ = pad_packed_sequence(lstm_out) hid_size = lstm_out.size(2) // 2 sents_bilstm_out = torch.cat([lstm_out[0, :, :hid_size], lstm_out[-1, :, hid_size:]], dim=1) sent_hidden = self.sent_hidden(sents_bilstm_out) x = torch.cat((mention_hidden, sent_hidden), 1) x = self.dropout(x) # (N, len(Ks)*Co) hidden = self.hidden(x) # (N, hidden) output = self.out(hidden) return output
class WordRep(nn.Module):
def __init__(self, data, use_position, use_cap, use_postag, use_char):
super(WordRep, self).__init__()
self.gpu = data.HP_gpu
self.use_char = use_char
self.batch_size = data.HP_batch_size
self.char_hidden_dim = 0
self.char_all_feature = False
if self.use_char:
self.char_hidden_dim = data.HP_char_hidden_dim
self.char_embedding_dim = data.char_emb_dim
self.char_feature = CharCNN(data.char_alphabet.size(),
data.pretrain_char_embedding,
self.char_embedding_dim,
self.char_hidden_dim, data.HP_dropout,
self.gpu)
self.embedding_dim = data.word_emb_dim
self.drop = nn.Dropout(data.HP_dropout)
self.word_embedding = nn.Embedding(data.word_alphabet.size(),
self.embedding_dim)
if data.pretrain_word_embedding is not None:
self.word_embedding.weight.data.copy_(
torch.from_numpy(data.pretrain_word_embedding))
else:
self.word_embedding.weight.data.copy_(
torch.from_numpy(
self.random_embedding(data.word_alphabet.size(),
self.embedding_dim)))
self.feature_num = 0
self.feature_embedding_dims = data.feature_emb_dims
self.feature_embeddings = nn.ModuleList()
if use_cap:
self.feature_num += 1
alphabet_id = data.feature_name2id['[Cap]']
emb = nn.Embedding(data.feature_alphabets[alphabet_id].size(),
self.feature_embedding_dims[alphabet_id])
emb.weight.data.copy_(
torch.from_numpy(
self.random_embedding(
data.feature_alphabets[alphabet_id].size(),
self.feature_embedding_dims[alphabet_id])))
self.feature_embeddings.append(emb)
if use_postag:
self.feature_num += 1
alphabet_id = data.feature_name2id['[POS]']
emb = nn.Embedding(data.feature_alphabets[alphabet_id].size(),
self.feature_embedding_dims[alphabet_id])
emb.weight.data.copy_(
torch.from_numpy(
self.random_embedding(
data.feature_alphabets[alphabet_id].size(),
self.feature_embedding_dims[alphabet_id])))
self.feature_embeddings.append(emb)
self.use_position = use_position
if self.use_position:
position_alphabet_id = data.re_feature_name2id['[POSITION]']
self.position_embedding_dim = data.re_feature_emb_dims[
position_alphabet_id]
self.position1_emb = nn.Embedding(
data.re_feature_alphabet_sizes[position_alphabet_id],
self.position_embedding_dim, data.pad_idx)
self.position1_emb.weight.data.copy_(
torch.from_numpy(
self.random_embedding(
data.re_feature_alphabet_sizes[position_alphabet_id],
self.position_embedding_dim)))
self.position2_emb = nn.Embedding(
data.re_feature_alphabet_sizes[position_alphabet_id],
self.position_embedding_dim, data.pad_idx)
self.position2_emb.weight.data.copy_(
torch.from_numpy(
self.random_embedding(
data.re_feature_alphabet_sizes[position_alphabet_id],
self.position_embedding_dim)))
if torch.cuda.is_available():
self.drop = self.drop.cuda(self.gpu)
self.word_embedding = self.word_embedding.cuda(self.gpu)
for idx in range(self.feature_num):
self.feature_embeddings[idx] = self.feature_embeddings[
idx].cuda(self.gpu)
if self.use_position:
self.position1_emb = self.position1_emb.cuda(self.gpu)
self.position2_emb = self.position2_emb.cuda(self.gpu)
def random_embedding(self, vocab_size, embedding_dim):
pretrain_emb = np.empty([vocab_size, embedding_dim])
scale = np.sqrt(3.0 / embedding_dim)
for index in range(vocab_size):
pretrain_emb[index, :] = np.random.uniform(-scale, scale,
[1, embedding_dim])
return pretrain_emb
def forward(self, word_inputs, feature_inputs, word_seq_lengths,
char_inputs, char_seq_lengths, char_seq_recover,
position1_inputs, position2_inputs):
"""
input:
word_inputs: (batch_size, sent_len)
features: list [(batch_size, sent_len), (batch_len, sent_len),...]
word_seq_lengths: list of batch_size, (batch_size,1)
char_inputs: (batch_size*sent_len, word_length)
char_seq_lengths: list of whole batch_size for char, (batch_size*sent_len, 1)
char_seq_recover: variable which records the char order information, used to recover char order
output:
Variable(batch_size, sent_len, hidden_dim)
"""
batch_size = word_inputs.size(0)
sent_len = word_inputs.size(1)
word_embs = self.word_embedding(word_inputs)
word_list = [word_embs]
for idx in range(self.feature_num):
word_list.append(self.feature_embeddings[idx](feature_inputs[idx]))
if self.use_char:
## calculate char lstm last hidden
char_features = self.char_feature.get_last_hiddens(
char_inputs,
char_seq_lengths.cpu().numpy())
char_features = char_features[char_seq_recover]
char_features = char_features.view(batch_size, sent_len, -1)
## concat word and char together
word_list.append(char_features)
word_embs = torch.cat([word_embs, char_features], 2)
if self.char_all_feature:
char_features_extra = self.char_feature_extra.get_last_hiddens(
char_inputs,
char_seq_lengths.cpu().numpy())
char_features_extra = char_features_extra[char_seq_recover]
char_features_extra = char_features_extra.view(
batch_size, sent_len, -1)
## concat word and char together
word_list.append(char_features_extra)
if self.use_position:
position1_feature = self.position1_emb(position1_inputs)
position2_feature = self.position2_emb(position2_inputs)
word_list.append(position1_feature)
word_list.append(position2_feature)
word_embs = torch.cat(word_list, 2)
word_represent = self.drop(word_embs)
return word_represent
class CNNCNN_SentATTEN(nn.Module):
def __init__(self, vocab, num_classes, char_alphabet):
super(CNNCNN_SentATTEN, self).__init__()
self.embedding = vocab.init_embed_layer()
self.hidden_size = opt.hidden_size
# charcnn
self.char_hidden_dim = 10
self.char_embedding_dim = 20
self.char_feature = CharCNN(len(char_alphabet), None,
self.char_embedding_dim,
self.char_hidden_dim, opt.dropout, opt.gpu)
self.embedding_size = self.embedding.weight.size(1)
self.hidden_size = opt.hidden_size
Ci = 1
Co = opt.kernel_num
Ks = [int(k) for k in list(opt.kernel_sizes) if k != ","]
# mention char_cnn
D = self.embedding_size + self.char_hidden_dim
self.convs1 = nn.ModuleList([
nn.Conv2d(in_channels=Ci,
out_channels=Co,
kernel_size=(K, D),
stride=(1, 1),
padding=(K // 2, 0),
dilation=1,
bias=False) for K in Ks
])
self.mention_hidden = nn.Linear(len(Ks) * Co, self.hidden_size)
#sentence atten
self.atten_W = nn.Linear(self.embedding_size, 1, bias=False)
self.sent_hidden_size = opt.sent_hidden_size
self.sent_hidden = nn.Linear(self.embedding_size,
self.sent_hidden_size)
self.hidden = nn.Linear(
self.hidden_size + self.sent_hidden_size,
self.hidden_size) # mention_hidden_size + sentence_hidden_size
self.out = nn.Linear(self.hidden_size, num_classes)
self.dropout = nn.Dropout(opt.dropout)
def conv_and_pool(self, x, conv):
x = F.relu(conv(x)).squeeze(3) # (N, Co, W)
x = F.max_pool1d(x, x.size(2)).squeeze(2)
return x
def forward(self, mention_inputs, char_inputs, sent_inputs):
inputs, lengths, seq_recover = mention_inputs
mention_embedding = self.embedding(inputs) # (N, W, D)
batch_size, max_len = inputs.size()
char_inputs, char_seq_lengths, char_seq_recover = char_inputs
char_features = self.char_feature.get_last_hiddens(char_inputs)
char_features = char_features[char_seq_recover]
char_features = char_features.view(batch_size, max_len, -1)
mention_char = torch.cat((mention_embedding, char_features), 2)
mention_char = mention_char.unsqueeze(1) # (N, Ci, W, D)
mention_char = [
F.relu(conv(mention_char)).squeeze(3) for conv in self.convs1
] # [(N, Co, W), ...]*len(Ks)
mention_char = [
F.max_pool1d(i, i.size(2)).squeeze(2) for i in mention_char
] # [(N, Co), ...]*len(Ks)
mention_char = torch.cat(mention_char, 1)
mention_hidden = self.mention_hidden(mention_char)
sent_inputs, sent_seq_lengths = sent_inputs
sent_embedding = self.embedding(sent_inputs)
sent_batch_size, sent_max_len, _ = sent_embedding.size()
flat_input = sent_embedding.contiguous().view(-1, self.embedding_size)
logits = self.atten_W(flat_input).view(sent_batch_size, sent_max_len)
alphas = F.softmax(logits, dim=1)
# computing mask
idxes = torch.arange(
0, sent_max_len,
out=torch.LongTensor(sent_max_len)).unsqueeze(0).cuda(opt.gpu)
mask = autograd.Variable(
(idxes < sent_seq_lengths.unsqueeze(1)).float())
alphas = alphas * mask
# renormalize
alphas = alphas / torch.sum(alphas, 1).view(-1, 1)
sent_atten_input = torch.bmm(alphas.unsqueeze(1),
sent_embedding).squeeze(1)
sent_atten_input = self.dropout(sent_atten_input)
sent_hidden = self.sent_hidden(sent_atten_input)
x = torch.cat((mention_hidden, sent_hidden), 1)
x = self.dropout(x)
hidden = self.hidden(x) # (N, hidden)
output = self.out(hidden)
return output
class CNNCNN(nn.Module):
def __init__(self, vocab, num_classes, char_alphabet):
super(CNNCNN, self).__init__()
self.embedding = vocab.init_embed_layer()
self.hidden_size = opt.hidden_size
# charcnn
self.char_hidden_dim = 10
self.char_embedding_dim = 20
self.char_feature = CharCNN(len(char_alphabet), None,
self.char_embedding_dim,
self.char_hidden_dim, opt.dropout, opt.gpu)
D = self.embedding.weight.size(1)
self.hidden_size = opt.hidden_size
D = D + self.char_hidden_dim
#mention cnn
Ci = 1
Co = opt.kernel_num
Ks = [int(k) for k in list(opt.kernel_sizes) if k != ","]
self.convs1 = nn.ModuleList([
nn.Conv2d(in_channels=Ci,
out_channels=Co,
kernel_size=(K, D),
stride=(1, 1),
padding=(K // 2, 0),
dilation=1,
bias=False) for K in Ks
])
self.hidden = nn.Linear(len(Ks) * Co, self.hidden_size)
self.out = nn.Linear(self.hidden_size, num_classes)
self.dropout = nn.Dropout(opt.dropout)
def conv_and_pool(self, x, conv):
x = F.relu(conv(x)).squeeze(3) # (N, Co, W)
x = F.max_pool1d(x, x.size(2)).squeeze(2)
return x
def forward(self, x, char_inputs):
inputs, lengths, seq_recover = x
x = self.embedding(inputs) # (N, W, D)
batch_size, max_len = inputs.size()
char_inputs, char_seq_lengths, char_seq_recover = char_inputs
char_features = self.char_feature.get_last_hiddens(char_inputs)
char_features = char_features[char_seq_recover]
char_features = char_features.view(batch_size, max_len, -1)
x = torch.cat((x, char_features), 2)
x = x.unsqueeze(1) # (N, Ci, W, D)
x = [F.relu(conv(x)).squeeze(3)
for conv in self.convs1] # [(N, Co, W), ...]*len(Ks)
x = [F.max_pool1d(i, i.size(2)).squeeze(2)
for i in x] # [(N, Co), ...]*len(Ks)
x = torch.cat(x, 1)
'''
x1 = self.conv_and_pool(x,self.conv13) #(N,Co)
x2 = self.conv_and_pool(x,self.conv14) #(N,Co)
x3 = self.conv_and_pool(x,self.conv15) #(N,Co)
x = torch.cat((x1, x2, x3), 1) # (N,len(Ks)*Co)
'''
x = self.dropout(x) # (N, len(Ks)*Co)
hidden = self.hidden(x) # (N, hidden)
output = self.out(hidden)
return output