LSTM 隐藏状态维度有错误：RuntimeError: Expected hidden[0] size (4, 1, 256), got (1, 256)

我正在 PyTorch 中试验 seq2seq_tutorial。编码器的 lstm 隐藏状态大小似乎存在维度错误。

使用bidirectional=True和num_layers = 2，隐藏状态的形状应该是(num_layers*2, batch_size, hidden_size)。

但是，出现错误并显示以下消息：

RuntimeError: Expected hidden[0] size (4, 1, 256), got (1, 256)

我尝试重塑隐藏状态以初始化具有不同形状的隐藏状态，一开始，但似乎没有任何效果。

这是我的代码的火车方法：

def train(self, input, target, encoder, decoder, encoder_optim, decoder_optim, criterion):
    enc_optimizer = encoder_optim
    dec_optimizer = decoder_optim
    enc_optimizer.zero_grad()
    dec_optimizer.zero_grad()

    pair = (input, target)
    input_len = input.size(0)
    target_len = target.size(0)
    enc_output_tensor = torch.zeros(self.opt['max_seq_len'], encoder.hidden_size, device=device)
    enc_hidden = encoder.cuda().initHidden(device)

    for word_idx in range(input_len):
        print('Input:', input[word_idx], '\nHidden shape:', enc_hidden.size())
        enc_output, enc_hidden = encoder(input[word_idx], enc_hidden)
        enc_output_tensor[word_idx] = enc_output[0,0]

这是我的代码的编码器方法：

class EncoderBRNN(nn.Module):
    # A bidirectional rnn based encoder
    def __init__(self, input_size, hidden_size, emb_size, batch_size=1, num_layers=2, bidir=True):
        super(EncoderBRNN, self).__init__()
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.batch_size = batch_size
        self.embedding_dim = emb_size
        self.num_layers = num_layers
        self.bidir = bidir
        self.embedding_layer = nn.Embedding(self.input_size, self.embedding_dim)
        self.enc_layer = nn.LSTM(self.embedding_dim, self.hidden_size, num_layers=self.num_layers, bidirectional=self.bidir)

    def forward(self, input, hidden):
        embed = self.embedding_layer(input).view(1, 1, -1)
        output, hidden = self.enc_layer(embed, hidden)
        return output, hidden

    def initHidden(self, device):
        if self.bidir:
            num_stacks = self.num_layers * 2
        else:
            num_stacks = self.num_layers
        return torch.zeros(num_stacks, self.batch_size, self.hidden_size, device=device)