无法计算出运行神经网络的密集层维度

Question

我正在尝试构建多层神经网络。我有形状的火车数据：

train[0][0].shape 
(4096,)

下面是我的密集层

from collections import OrderedDict
n_out = 8
net = nn.Sequential(OrderedDict([
                            ('hidden_linear', nn.Linear(4096, 1366)),
                            ('hidden_activation', nn.Tanh()),
                            ('hidden_linear', nn.Linear(1366, 456)),
                            ('hidden_activation', nn.Tanh()),
                            ('hidden_linear', nn.Linear(456, 100)),
                            ('hidden_activation', nn.Tanh()), 
                            ('output_linear', nn.Linear(100, n_out))
                            ]))

我使用交叉熵作为损失函数。我遇到的问题是当我使用以下代码训练模型时：

 learning_rate = 0.001
 optimizer = torch.optim.SGD(net.parameters(), lr=learning_rate)
 n_epochs = 40

for epoch in range(n_epochs):
    for snds, labels in final_train_loader:
         outputs = net(snds.view(snds.shape[0], -1))
         loss = loss_fn(outputs, labels)

         optimizer.zero_grad()
         loss.backward()
         optimizer.step()

     print("Epoch: %d, Loss: %f" % (epoch, float(loss)))

我收到的错误是矩阵乘法错误。

 RuntimeError: mat1 and mat2 shapes cannot be multiplied (100x4096 and 456x100)

我的尺寸有误，但不知道如何正确。

Answer 1

OrderedDict 包含三个 Linear 层与相同的键关联，hidden_layer（nn.Tanh 也是如此）。为了使其工作，您需要为这些层提供不同的名称：

inp = torch.rand(100, 4096)
net = nn.Sequential(OrderedDict([
                            ('hidden_linear0', nn.Linear(4096, 1366)),
                            ('hidden_activation0', nn.Tanh()),
                            ('hidden_linear1', nn.Linear(1366, 456)),
                            ('hidden_activation1', nn.Tanh()),
                            ('hidden_linear2', nn.Linear(456, 100)),
                            ('hidden_activation2', nn.Tanh()), 
                            ('output_linear', nn.Linear(100, n_out))
                            ]))
net(inp)  # now it works!

无法计算出运行神经网络的密集层维度

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无法计算出 运行 神经网络的密集层维度

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无法计算出运行神经网络的密集层维度