test

class MYMODEL_V11(BasicModule):
    def __init__(self, opt):
        super(MYMODEL_V11, self).__init__()
        self.opt = opt
        self.model_name = 'mymodel_v11'
        self.batch_size = opt.batch_size
        self.hidden_dim = opt.hidden_dim
        self.num_layers = opt.lstm_layers
        self.mean = opt.lstm_mean
        self.vocab_size = opt.vocab_size
        self.embedding_dim = opt.embedding_dim
        self.label_size = opt.label_size
        self.in_channel = 1
        self.kernel_nums = opt.clstm_kernel_nums
        self.kernel_sizes = opt.clstm_kernel_sizes
        self.ks2 = opt.kernel_sizes
        self.kn2 = opt.kernel_nums
        self.use_gpu = torch.cuda.is_available()
        self.max_seq_len = opt.max_seq_len
        # self.embedding = nn.Embedding(self.vocab_size + 2, self.embedding_dim, padding_idx=self.vocab_size + 1)
        self.embedding = nn.Embedding(self.vocab_size, self.embedding_dim, padding_idx=self.vocab_size-1, _weight=opt.embeddings)        
        # self.embedding.weight = nn.Parameter(opt.embeddings)

kernel size	3
channels number	256, 128, 64, 32, 32, 32
batchnorm	2
dropout	0.5
learning rate	5e-5
pooling strategy	bilstm+maxpooling

只使用LSTM、BiLSTM实现系统，并和Kim-CNN比较，说明RNN和CNN在特征提取中虽然使用不同的模型，但具有类似的表达能力

fg