Question

我想要一个RNN，输入大小为7，隐藏大小为10，输出大小为2。因此，对于形状为99x1x7的输入，我希望输出形状为99x1x2。仅对于RNN，我得到：

model = nn.RNN(input_size=7, hidden_size=10, num_layers=1)

output,hn=model(torch.rand(99,1,7))
print(output.shape) #torch.Size([99, 1, 10])
print(hn.shape)     #torch.Size([ 1, 1, 10])

所以我认为我仍然必须在其后加上Linear：

model = nn.Sequential(nn.RNN(input_size=7, hidden_size=10, num_layers=1),
                      nn.Linear(in_features=10, out_features=2))
model(torch.rand(99,1,7))

Traceback (most recent call last):
  File "train_rnn.py", line 80, in <module>
    main()
  File "train_rnn.py", line 25, in main
    model(torch.rand(99,1,7))
  File "/home/.../virtual-env/lib/python3.6/site-packages/torch/nn/modules/module.py", line 493, in __call__
    result = self.forward(*input, **kwargs)
  File "/home/.../virtual-env/lib/python3.6/site-packages/torch/nn/modules/container.py", line 92, in forward
    input = module(input)
  File "/home/.../virtual-env/lib/python3.6/site-packages/torch/nn/modules/module.py", line 493, in __call__
    result = self.forward(*input, **kwargs)
  File "/home/.../virtual-env/lib/python3.6/site-packages/torch/nn/modules/linear.py", line 92, in forward
    return F.linear(input, self.weight, self.bias)
  File "/home/.../virtual-env/lib/python3.6/site-packages/torch/nn/functional.py", line 1404, in linear
    if input.dim() == 2 and bias is not None:
AttributeError: 'tuple' object has no attribute 'dim'

我猜这是因为Linear接收到RNN.forward产生的元组。但是我应该如何将两者结合起来？

Answer 1

来自pytorch文档https://pytorch.org/docs/stable/nn.html?highlight=rnn#torch.nn.RNN

输出的形状为seq_len, batch, num_directions * hidden_size

因此，根据您的需要，您可以添加fc层以获得大小为2的输出。基本上，Sequential会将每个模型应用到next_one的输出之上，因此，您不得使用Sequential或创建可用于序列的特殊线性层，以下应可工作：

class seq_Linear(nn.module):
  def __init__(self, linear):
    self.linear = linear
  # To apply on every hidden state
  def forward(self, x):
    return torch.stack([self.linear(hs) for hs in x])
  # To apply on the last hidden state
  def forward(self, x):
    return self.linear(x[-1])

，然后在代码中用seq_Linear（nn.Linear）替换nn.Linear。

编辑：如果要创建大小为2的输出序列，最好的方法可能是在第一个RNN的顶部堆叠另一个input_size 10和output_size 2的RNN，它们应该可堆叠在{{1 }}，没有任何麻烦。

如何设置RNN的输出大小？

1 个答案: