RuntimeError: Expected hidden[0] size (1, 1, 512), got (1, 128, 512) for LSTM pytorch

Posted 2023-03-25

【中文标题】RuntimeError: Expected hidden[0] size (1, 1, 512), got (1, 128, 512) for LSTM pytorch

【英文标题】：

【发布时间】：2021-02-14 04:01:21

【问题描述】：

我以 128 的批大小训练 LSTM，在测试期间我的批大小为 1，为什么会出现此错误？我想在测试时初始化隐藏大小？

这是我正在使用的代码，我将隐藏状态 init_hidden 函数初始化为 (number_of_layers, batch_size, hidden_​​size) 因为 batch_first=True

class ImageLSTM(nn.Module):
def __init__(self,  n_inputs:int=49, 
                    n_outputs:int=4096, 
                    n_hidden:int=256, 
                    n_layers:int=1, 
                    bidirectional:bool=False):
    """
    Takes a 1D flatten images.
    """
    super(ImageLSTM, self).__init__()
    self.n_inputs   = n_inputs
    self.n_hidden   = n_hidden
    self.n_outputs  = n_outputs
    self.n_layers   = n_layers
    self.bidirectional = bidirectional
    self.lstm       = nn.LSTM(  input_size=self.n_inputs, 
                                hidden_size=self.n_hidden, 
                                num_layers=self.n_layers, 
                                dropout  = 0.5 if self.n_layers>1 else 0,
                                bidirectional=self.bidirectional,
                                batch_first=True)
    if (self.bidirectional):
        self.FC         = nn.Sequential(
                                        nn.Linear(self.n_hidden*2, self.n_outputs),
                                        nn.Dropout(p=0.5),
                                        nn.Sigmoid()
                                            )

    else:
        self.FC         = nn.Sequential(
                                        nn.Linear(self.n_hidden, self.n_outputs),
                                        # nn.Dropout(p=0.5),
                                        nn.Sigmoid()
                                        )

    
def init_hidden(self, batch_size, device=None): # input 4D tensor: (batch size, channels, width, height)
    # initialize the hidden and cell state to zero
    # vectors:(number of layer, batch size, number of hidden nodes)
    if (self.bidirectional):
        h0 = torch.zeros(2*self.n_layers,  batch_size, self.n_hidden)
        c0 = torch.zeros(2*self.n_layers,  batch_size, self.n_hidden)
    else:
        h0 = torch.zeros(self.n_layers,  batch_size, self.n_hidden)
        c0 = torch.zeros(self.n_layers,  batch_size, self.n_hidden)

    if device is not None:
        h0 = h0.to(device)
        c0 = c0.to(device)
    self.hidden = (h0,c0)

def forward(self, X): # X: tensor of shape (batch_size, channels, width, height)
    # forward propagate LSTM
    lstm_out, self.hidden = self.lstm(X, self.hidden) # lstm_out: tensor of shape (batch_size, seq_length, hidden_size)
    # Decode the hidden state of the last time step 
    out = self.FC(lstm_out[:, -1, :])
    return out

【参考方案1】：

请编辑您的帖子并添加代码。你是如何初始化隐藏状态的？你的模型是什么样子的。

hidden[0] 不是你的隐藏大小，它是 lstm 的隐藏状态。隐藏状态的形状必须像这样初始化：

hidden = ( torch.zeros((batch_size, layers, hidden_size)), torch.zeros((layers, batch_size, hidden_size)) )

您似乎做得正确。但是错误告诉您，您提供了一批大小为 1 的批次（因为正如您所说，您只想使用一个样本进行测试），但隐藏状态是使用 batch-size=128 初始化的。

所以我猜（请添加代码）您硬编码批次大小 = 128。不要那样做。由于您必须在每次前向传递中重新初始化隐藏状态，您可以这样做：

...

def forward(self, x):
    batch_size = x.shape[0]
    hidden = (torch.zeros(self.layers, batch_size, self.hidden_size).to(device=device), torch.zeros(self.layers, batch_size, self.hidden_size).to(device=device))

    output, hidden = lstm(x, hidden)
    
    # then do what every you want with the output

我猜这就是导致此错误的原因，但也请发布您的代码！