Machine learning Pytorch中显示输入大小不匹配的自定义LSTM模型

我有一个自定义的双向LSTM模型，其中自定义部分是

- extract the forward and backward last hidden state
- concat those states
- create a fully connected layer and pass it through softmax layer.

代码如下所示：

class customModel(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers, num_classes):
        super(customModel, self).__init__()
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        self.bilstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=False, bidirectional=True)
        self.fcl = nn.Linear(hidden_size, num_classes)

    def forward(self, x):
        # Set initial hidden and cell states 
        h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device)
        c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device)

        # Forward propagate LSTM
        out, hidden = self.bilstm(x, (h0, c0))  # out: tensor of shape (batch_size, seq_length, hidden_size)
        #concat hidden state of forward and backword
        fw_bilstm = out[-1, :, :self.hidden_size]
        bk_bilstm = out[0, :, :self.hidden_size]
        concat_fw_bw = torch.cat((fw_bilstm, bk_bilstm), dim = 1)
        fc = nn.Linear(concat_fw_bw, num_classes)
        x = F.relu(fc(x))
        return F.softmax(x)

for item in input_embedding:
    print(item.size())
    for epoch in range(1):  
        pred = model(item)  
        print (pred)

我使用以下参数和输入

input_size = 2
hidden_size = 32  
num_layers = 1
num_classes = 2

input_embedding = [
    torch.FloatTensor([[-0.8264],  [0.2524]]),
    torch.FloatTensor([[-0.3259],  [0.3564]])
]

然后我创建一个模型对象

model = customModel(input_size, hidden_size, num_layers, num_classes)

然后我使用如下所示：

class customModel(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers, num_classes):
        super(customModel, self).__init__()
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        self.bilstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=False, bidirectional=True)
        self.fcl = nn.Linear(hidden_size, num_classes)

    def forward(self, x):
        # Set initial hidden and cell states 
        h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device)
        c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device)

        # Forward propagate LSTM
        out, hidden = self.bilstm(x, (h0, c0))  # out: tensor of shape (batch_size, seq_length, hidden_size)
        #concat hidden state of forward and backword
        fw_bilstm = out[-1, :, :self.hidden_size]
        bk_bilstm = out[0, :, :self.hidden_size]
        concat_fw_bw = torch.cat((fw_bilstm, bk_bilstm), dim = 1)
        fc = nn.Linear(concat_fw_bw, num_classes)
        x = F.relu(fc(x))
        return F.softmax(x)

for item in input_embedding:
    print(item.size())
    for epoch in range(1):  
        pred = model(item)  
        print (pred)

当我运行它时，我看到这一行

out，hidden=self.bilstm（x，（h0，c0））

，它显示错误

RuntimeError: input must have 3 dimensions, got 2

当我明确指定

input\u size=2

我缺少什么？

您的输入中似乎缺少（批次或序列）维度

和之间有区别。前者——也就是您使用的那个——将整个序列作为输入。因此，它需要形状的三维输入（顺序、批次、输入大小）

假设您希望以批的形式将这4个字母序列（您将其编码为一个热向量）作为输入：

x0 = [a,b,c]
x1 = [c,d,e]
x2 = [e,f,g]
x3 = [h,i,j]

### input.size() should give you the following:
(3,4,8)

• seq_len

  参数是序列的大小：这里是3

• input\u size

  参数是每个输入向量的大小：这里，输入将是大小为8的一个热向量
• 批处理是您组合在一起的序列数：这里有4个序列

注意：将批次顺序放在第一位，并将

batch_first

设置为True，这样更容易掌握

另外：如果未提供（h_0，c_0），则h_0和c_0都默认为零，因此创建它们没有用处