Python PYTORCH张量问题Mat1和Mat2形状不能相乘(8x10和8x8)

[我知道这个问题经常出现，但我找不到任何与我的用例匹配的答案]

[编辑:如果你知道 torch ，我知道矩阵乘法，请回答这个问题，这不是这里的问题]

I将由8个浮点组成的N=10个输入馈送到8个输入层

但我明白这个错误

Mat1 and mat2 shapes cannot be multiplied (8x10 and 8x8) 

我错过了什么？

我在数据集中生成随机数据

class MyDataset(Dataset):
    def __init__(self):
        self.data = []
        self.input_size = 0
        for i in range(0,10):
            label = random.randint(0, 1)
            data = [random.uniform(0.0, 1.0) for _ in range(8)]
            self.data.append((data , label))
            self.input_size = len(data ) if self.input_size < len(encoded_text) else self.input_size
  
    def __len__(self):
        return len(self.data)
    
    def __getitem__(self, idx):
        text, label = self.data[idx]
        return text, label

这是我的模型

class MyModel(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(MyModel, self).__init__()
        self.input = nn.Linear(input_size,hidden_size)
        self.hidden = nn.Linear(hidden_size, output_size)
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        # padding is there as original dataset does not have full 8 floats inputs
        x_padded = pad_sequence(x, batch_first=True, padding_value=0).float()  
        output = self.input(x_padded) >>>>>>>> ERROR
        return torch.sigmoid(output)

def train_model(model, train_loader, criterion, optimizer, num_epochs):
    for epoch in range(num_epochs):
        for inputs, labels in train_loader:
            outputs = model(inputs)

初始化部分

if __name__ == "__main__":
    hidden_size = 8  # hidden size 
    output_size = 1  # binary classification 
    learning_rate = 0.001
    num_epochs = 10
    
    dataset = MyDataset()
    train_loader = DataLoader(dataset, batch_size=64, shuffle=True)
    
    input_size = dataset.input_size
    
    model = MyModel(input_size, hidden_size, output_size)
    criterion = nn.BCEWithLogitsLoss()
    optimizer = optim.Adam(model.parameters(), lr=learning_rate)
    train_model(model, train_loader, criterion, optimizer, num_epochs)`

这是一个非常简单的初学者用例，没什么特别的

谢谢你的帮忙

[编辑:x输入由10个值的8个张量组成，但它应该是8个值的10个张量]