def __init__(self, config, params):
super(Context_att, self).__init__()
self.word_num = params.word_num
self.label_num = params.label_num
self.char_num = params.char_num
self.id2word = params.word_alphabet.id2word
self.word2id = params.word_alphabet.word2id
self.padID = params.word_alphabet.word2id['<pad>']
self.unkID = params.word_alphabet.word2id['<unk>']
self.use_cuda = params.use_cuda
self.add_char = params.add_char
self.static = params.static
self.feature_count = config.shrink_feature_thresholds
self.word_dims = config.word_dims
self.char_dims = config.char_dims
self.lstm_hiddens = config.lstm_hiddens
self.attention_size = config.attention_size
self.dropout_emb = nn.Dropout(p=config.dropout_emb)
self.dropout_lstm = nn.Dropout(p=config.dropout_lstm)
self.lstm_layers = config.lstm_layers
self.batch_size = config.train_batch_size
self.embedding = nn.Embedding(self.word_num, self.word_dims)
self.embedding.weight.requires_grad = True
if self.static:
self.embedding_static = nn.Embedding(self.word_num, self.word_dims)
self.embedding_static.weight.requires_grad = False
if params.pretrain_word_embedding is not None:
# pretrain_weight = np.array(params.pretrain_word_embedding)
# self.embedding.weight.data.copy_(torch.from_numpy(pretrain_weight))
# pretrain_weight = np.array(params.pretrain_embed)
pretrain_weight = torch.FloatTensor(params.pretrain_word_embedding)
self.embedding.weight.data.copy_(pretrain_weight)
# for id in range(self.word_dims):
# self.embedding.weight.data[self.eofID][id] = 0
if params.static:
self.lstm = nn.LSTM(self.word_dims * 2,
self.lstm_hiddens // 2,
num_layers=self.lstm_layers,
bidirectional=True,
dropout=config.dropout_lstm)
else:
self.lstm = nn.LSTM(self.word_dims,
self.lstm_hiddens // 2,
num_layers=self.lstm_layers,
bidirectional=True,
dropout=config.dropout_lstm)
self.hidden2label = nn.Linear(self.lstm_hiddens, self.label_num)
self.hidden = self.init_hidden(self.batch_size, self.lstm_layers)
# self.attention = Attention(self.lstm_hiddens, self.attention_size, self.use_cuda)
# self.attention_l = Attention(self.lstm_hiddens, self.attention_size, self.use_cuda)
# self.attention_r = Attention(self.lstm_hiddens, self.attention_size, self.use_cuda)
self.attention = Attention_b(self.lstm_hiddens, self.attention_size,
self.use_cuda)
self.attention_l = Attention_b(self.lstm_hiddens, self.attention_size,
self.use_cuda)
self.attention_r = Attention_b(self.lstm_hiddens, self.attention_size,
self.use_cuda)
self.linear = nn.Linear(self.lstm_hiddens, self.label_num, bias=True)
self.linear_l = nn.Linear(self.lstm_hiddens, self.label_num, bias=True)
self.linear_r = nn.Linear(self.lstm_hiddens, self.label_num, bias=True)
def __init__(self, config, params):
super(Context_att_gate, self).__init__()
self.word_num = params.word_num
self.label_num = params.label_num
self.char_num = params.char_num
self.category_num = params.category_num
self.id2word = params.word_alphabet.id2word
self.word2id = params.word_alphabet.word2id
self.padID = params.word_alphabet.word2id['<pad>']
self.unkID = params.word_alphabet.word2id['<unk>']
self.use_cuda = params.use_cuda
self.add_char = params.add_char
self.static = params.static
self.feature_count = config.shrink_feature_thresholds
self.word_dims = config.word_dims
self.char_dims = config.char_dims
self.lstm_hiddens = config.lstm_hiddens
self.attention_size = config.attention_size
self.dropout_emb = nn.Dropout(p=config.dropout_emb)
self.dropout_lstm = nn.Dropout(p=config.dropout_lstm)
self.lstm_layers = config.lstm_layers
self.batch_size = config.train_batch_size
self.embedding = nn.Embedding(self.word_num, self.word_dims)
self.embedding.weight.requires_grad = True
self.embedding_label = nn.Embedding(self.label_num, self.word_dims)
self.embedding_label.weight.requires_grad = True
if params.pretrain_word_embedding is not None:
# pretrain_weight = np.array(params.pretrain_word_embedding)
# self.embedding.weight.data.copy_(torch.from_numpy(pretrain_weight))
# pretrain_weight = np.array(params.pretrain_embed)
pretrain_weight = torch.FloatTensor(params.pretrain_word_embedding)
self.embedding.weight.data.copy_(pretrain_weight)
# for id in range(self.word_dims):
# self.embedding.weight.data[self.eofID][id] = 0
self.lstm = nn.LSTM(self.word_dims * 2,
self.lstm_hiddens // 2,
num_layers=self.lstm_layers,
bidirectional=True,
dropout=config.dropout_lstm)
# nn.init.xavier_uniform(self.lstm.all_weights[0][0])
# nn.init.xavier_uniform(self.lstm.all_weights[0][1])
# nn.init.xavier_uniform(self.lstm.all_weights[1][0])
# nn.init.xavier_uniform(self.lstm.all_weights[1][1])
self.hidden2label = nn.Linear(self.lstm_hiddens, self.category_num)
self.hidden = self.init_hidden(self.batch_size, self.lstm_layers)
self.attention = Attention_b(self.lstm_hiddens, self.attention_size,
self.use_cuda)
self.attention_l = Attention_b(self.lstm_hiddens, self.attention_size,
self.use_cuda)
self.attention_r = Attention_b(self.lstm_hiddens, self.attention_size,
self.use_cuda)
self.w1 = Parameter(torch.randn(self.lstm_hiddens, self.lstm_hiddens))
self.w2 = Parameter(torch.randn(self.lstm_hiddens, self.lstm_hiddens))
self.w3 = Parameter(torch.randn(self.lstm_hiddens, self.lstm_hiddens))
self.u1 = Parameter(torch.randn(self.lstm_hiddens, self.lstm_hiddens))
self.u2 = Parameter(torch.randn(self.lstm_hiddens, self.lstm_hiddens))
self.u3 = Parameter(torch.randn(self.lstm_hiddens, self.lstm_hiddens))
self.b1 = Parameter(torch.randn(self.lstm_hiddens, self.batch_size))
self.b2 = Parameter(torch.randn(self.lstm_hiddens, self.batch_size))
self.b3 = Parameter(torch.randn(self.lstm_hiddens, self.batch_size))
nn.init.xavier_uniform(self.w1)
nn.init.xavier_uniform(self.w2)
nn.init.xavier_uniform(self.w3)
nn.init.xavier_uniform(self.u1)
nn.init.xavier_uniform(self.u2)
nn.init.xavier_uniform(self.u3)
nn.init.xavier_uniform(self.b1)
nn.init.xavier_uniform(self.b2)
nn.init.xavier_uniform(self.b3)
self.linear_2 = nn.Linear(self.lstm_hiddens,
self.category_num,
bias=True)
nn.init.xavier_uniform(self.linear_2.weight)