神经网络 Pytorch.nn():RuntimeError

神经网络 Pytorch.nn() model =linear_model()报错 RuntimeError

一 源代码

第一步: 获取数据过程

t_c = [0.5,  14.0, 15.0, 28.0, 11.0,  8.0,  3.0, -4.0,  6.0, 13.0, 21.0]
t_u = [35.7, 55.9, 58.2, 81.9, 56.3, 48.9, 33.9, 21.8, 48.4, 60.4, 68.4]
plt.plot(t_u,t_c,'.');
t_c = torch.tensor(t_c)
t_u = torch.tensor(t_u)

第二步:定义training_loop()过程

def training_loop(n_epochs, optimizer, model, loss_fn, t_u_train, t_u_val,t_c_train, t_c_val):
    for epoch in range(1, n_epochs + 1):
        t_p_train = model(t_u_train)
        loss_train = loss_fn(t_p_train, t_c_train)
        t_p_val = model(t_u_val)
        loss_val = loss_fn(t_p_val, t_c_val)
        optimizer.zero_grad()
        loss_train.backward()
        optimizer.step()
        if epoch == 1 or epoch % 1000 == 0:
            print('Epoch {}, Training loss {}, Validation loss {}'.format(
                    epoch, float(loss_train), float(loss_val)))

第三步:传入参数、调用training_loop()过程

training_loop(
    n_epochs = 5000,
    optimizer = optimizer,
    model = linear_model,
    loss_fn = nn.MSELoss(),
    t_u_train = train_t_un,
    t_u_val = val_t_un,
    t_c_train = train_t_c,
    t_c_val = val_t_c)

调用training_loop()报错RuntimeError: mat1 and mat2 shapes cannot be multiplied (1x9 and 1x1)

二 debug

问题出在获取数据过程
改成

t_c = [0.5,  14.0, 15.0, 28.0, 11.0,  8.0,  3.0, -4.0,  6.0, 13.0, 21.0]
t_u = [35.7, 55.9, 58.2, 81.9, 56.3, 48.9, 33.9, 21.8, 48.4, 60.4, 68.4]
plt.plot(t_u,t_c,'.');
t_c = torch.tensor(t_c).unsqueeze(1)
t_u = torch.tensor(t_u).unsqueeze(1)

问题解决

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