def __init__(self, config):
'''
LSTM+Aspect
'''
super(AspectSent, self).__init__()
self.config = config
input_dim = config.l_hidden_size
kernel_num = config.l_hidden_size
self.conv = nn.Conv1d(input_dim, kernel_num, 3, padding=1)
#self.conv = nn.Conv1d(input_dim, kernel_num, 3, dilation=2, padding=2)
self.bilstm = biLSTM(config)
self.feat2tri = nn.Linear(kernel_num, 2+2)
self.inter_crf = LinearChainCrf(2+2)
self.feat2label = nn.Linear(kernel_num, 3)
self.loss = nn.NLLLoss()
self.tanh = nn.Tanh()
self.dropout = nn.Dropout(config.dropout2)
#Modified by Richard Sun
self.cat_layer = SimpleCat(config)
self.cat_layer.load_vector()
def __init__(self, config):
'''
LSTM+Aspect
'''
super(AspectSent, self).__init__()
self.config = config
self.input_dim = config.l_hidden_size # + config.pos_dim
kernel_num = config.l_hidden_size # + config.pos_dim
reduced_size = int(config.l_hidden_size / 4)
self.conv = nn.Conv1d(self.input_dim, kernel_num, 3,
padding=1) # not used
self.bilstm = biLSTM(config)
self.feat2tri = nn.Linear(reduced_size, 2 + 2)
self.inter_crf = LinearChainCrf(2 + 2)
self.h1linear = nn.Linear(self.input_dim, reduced_size)
self.feat2tri2 = nn.Linear(reduced_size, 2 + 2)
self.inter_crf2 = LinearChainCrf(2 + 2)
self.h2linear = nn.Linear(self.input_dim, reduced_size)
self.feat2tri3 = nn.Linear(reduced_size, 2 + 2)
self.inter_crf3 = LinearChainCrf(2 + 2)
self.h3linear = nn.Linear(self.input_dim, reduced_size)
self.feat2tri4 = nn.Linear(reduced_size, 2 + 2)
self.inter_crf4 = LinearChainCrf(2 + 2)
self.h4linear = nn.Linear(self.input_dim, reduced_size)
self.feat2label = nn.Linear(kernel_num, 3)
self.feat2label2 = nn.Linear(self.input_dim * 4, 3)
# gcn - not used for current model
self.W = nn.ModuleList()
self.W.append(nn.Linear(config.l_hidden_size, config.l_hidden_size))
self.W.append(nn.Linear(config.l_hidden_size, config.l_hidden_size))
self.W.append(nn.Linear(200, 100))
# cnn - not used for current model
self.filters = [3]
self.convs = nn.ModuleList([
nn.Conv1d(in_channels=1,
out_channels=config.l_hidden_size,
kernel_size=(k, config.l_hidden_size + 2),
padding=1) for k in self.filters
])
self.loss = nn.NLLLoss()
self.tanh = nn.Tanh()
self.dropout = nn.Dropout(config.dropout2)
self.dropout2 = nn.Dropout(config.dropout)
self.cat_layer = SimpleCat(config)
self.cat_layer.load_vector()
self.sigmoid = nn.Sigmoid()
def __init__(self, config):
super(AspectSent, self).__init__()
self.config = config
self.cat_layer = SimpleCat(config)
self.lstm = MLSTM(config)
self.feat2tri = nn.Linear(config.l_hidden_size, 2)
self.inter_crf = LinearCRF(config)
self.feat2label = nn.Linear(config.l_hidden_size, 3)
self.cri = nn.CrossEntropyLoss()
self.cat_layer.load_vector()
if not config.if_update_embed: self.cat_layer.word_embed.weight.requires_grad = False