nn1.py 文件源码

def __init__(self, batch_size, num_tokens, embed_size, word_gru_hidden, bidirectional= True, init_range=0.1, use_lstm=False):        

        super(AttentionWordRNN, self).__init__()

        self.batch_size = batch_size
        self.num_tokens = num_tokens
        self.embed_size = embed_size
        self.word_gru_hidden = word_gru_hidden
        self.bidirectional = bidirectional
        self.use_lstm = use_lstm       

        self.lookup = nn.Embedding(num_tokens, embed_size)
        if bidirectional == True:
            if use_lstm:
                print("inside using LSTM")
                self.word_gru = nn.LSTM(embed_size, word_gru_hidden, bidirectional= True)
            else:
                self.word_gru = nn.GRU(embed_size, word_gru_hidden, bidirectional= True)
            self.weight_W_word = nn.Parameter(torch.Tensor(2* word_gru_hidden, 2*word_gru_hidden))
            self.bias_word = nn.Parameter(torch.Tensor(2* word_gru_hidden,1))
            self.weight_proj_word = nn.Parameter(torch.Tensor(2*word_gru_hidden, 1))
        else:
            if use_lstm:
                self.word_gru = nn.LSTM(embed_size, word_gru_hidden, bidirectional= False)
            else:
                self.word_gru = nn.GRU(embed_size, word_gru_hidden, bidirectional= False)
            self.weight_W_word = nn.Parameter(torch.Tensor(word_gru_hidden, word_gru_hidden))
            self.bias_word = nn.Parameter(torch.Tensor(word_gru_hidden,1))
            self.weight_proj_word = nn.Parameter(torch.Tensor(word_gru_hidden, 1))

        self.softmax_word = nn.Softmax()
        self.weight_W_word.data.uniform_(-init_range, init_range)
        self.weight_proj_word.data.uniform_(-init_range, init_range)