Python 运行时错误：输入必须有3个维度，得到2个维度

嗨，我有以下代码

class GRU(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers, num_classes ):
        super(GRU, self).__init__()
        
        #WE STORE THE LAYERS AND HIDDENSIZE
        self.num_layers = num_layers
        self.hidden_size = hidden_size
        
        self.gru = nn.GRU(input_size, hidden_size, num_layers, batch_first=True)
        self.fc = nn.Linear(hidden_size* sequence_length, num_classes)
        
    def forward(self, x):
        # Set initial hidden states (and cell states for LSTM)
        h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device) 
        # Forward propagate RNN
        out, _ = self.gru(x, h0)  
        out=out.reshape(out.shape[0], -1)
        out = self.fc(out)
        return out

以及以下超参数

# Hyper-parameters 
num_classes = 6
batch_size = 32
learning_rate = 0.0025
dropout=0.2
epochs = 10

input_size = len(X_train[0][0])
sequence_length = len(X_train[0])
hidden_size = 64
num_layers = 2

print (input_size)
print (sequence_length)
6
100

我的数据是三维的

X_train.shape
torch.Size([4078, 100, 6])
X_test.shape
torch.Size([1020, 100, 6])

然而，当我试图运行代码时，我收回了描述中的错误。我查看了PyTorch的官方网站和其他相同的问题，但找不到解决方案

如果有人能帮我写代码，我将不胜感激

这是完整的错误消息

RuntimeError                              Traceback (most recent call last)
<ipython-input-47-21beb733af1e> in <module>
      1 test_accs, confusion_mtxes = [], []
      2 for epoch in range(1, epochs + 1):
----> 3     train(X_train, y_train)
      4     test_acc, confusion_mtx = test(X_test, y_test)
      5     test_accs.append(test_acc)

<ipython-input-45-d10728615dab> in train(X_train, y_train)
      4         data, target = data, target
      5         optimizer.zero_grad()
----> 6         output = model(data)
      7         loss = criterion(output, target)
      8         loss.backward()

~\AppData\Roaming\Python\Python37\site-packages\torch\nn\modules\module.py in __call__(self, *input, **kwargs)
    530             result = self._slow_forward(*input, **kwargs)
    531         else:
--> 532             result = self.forward(*input, **kwargs)
    533         for hook in self._forward_hooks.values():
    534             hook_result = hook(self, input, result)

<ipython-input-42-afe206da09cb> in forward(self, x)
     14         h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device)
     15         # Forward propagate RNN
---> 16         out, _ = self.gru(x, h0)
     17         out=out.reshape(out.shape[0], -1)
     18         out = self.fc(out)

~\AppData\Roaming\Python\Python37\site-packages\torch\nn\modules\module.py in __call__(self, *input, **kwargs)
    530             result = self._slow_forward(*input, **kwargs)
    531         else:
--> 532             result = self.forward(*input, **kwargs)
    533         for hook in self._forward_hooks.values():
    534             hook_result = hook(self, input, result)

~\AppData\Roaming\Python\Python37\site-packages\torch\nn\modules\rnn.py in forward(self, input, hx)
    711             hx = self.permute_hidden(hx, sorted_indices)
    712 
--> 713         self.check_forward_args(input, hx, batch_sizes)
    714         if batch_sizes is None:
    715             result = _VF.gru(input, hx, self._flat_weights, self.bias, self.num_layers,

~\AppData\Roaming\Python\Python37\site-packages\torch\nn\modules\rnn.py in check_forward_args(self, input, hidden, batch_sizes)
    178     def check_forward_args(self, input, hidden, batch_sizes):
    179         # type: (Tensor, Tensor, Optional[Tensor]) -> None
--> 180         self.check_input(input, batch_sizes)
    181         expected_hidden_size = self.get_expected_hidden_size(input, batch_sizes)
    182 

~\AppData\Roaming\Python\Python37\site-packages\torch\nn\modules\rnn.py in check_input(self, input, batch_sizes)
    153             raise RuntimeError(
    154                 'input must have {} dimensions, got {}'.format(
--> 155                     expected_input_dim, input.dim()))
    156         if self.input_size != input.size(-1):
    157             raise RuntimeError(

RuntimeError: input must have 3 dimensions, got 2

---

我尝试在这里使用相同的逻辑，这意味着我将序列长度与输入大小相乘，但仍然无法解决问题

我认为一个问题是您在nn.GRU中错误地设置了batch\u first=True。输入数据应为batch_first=False。在这里，您的培训和测试数据集（X_train和X_test）的形状已经是（seq_len，batch_size，features），您不需要为nn.GRU设置batch_first=True。它将给出形状输出（顺序、批次大小、隐藏大小）。仅当数据的形状为（批次大小、顺序、特征）时，才使用batch_first=True。希望有帮助。@HVD不，它不起作用。我还发布了完整的错误消息。谢谢你的尝试

model = Sequential()
model.add(GRU(units=64, return_sequences=True,recurrent_regularizer=l2(0.0015), input_shape=(timesteps, input_dim), activation="tanh"))
model.add(GRU(units=64, recurrent_regularizer=l2(0.0015), input_shape=(timesteps, input_dim), activation="tanh"))
model.add(Dropout(0.5))

model.add(Dense(64, activation='tanh'))
model.add(Dense(64, activation='tanh'))

model.add(Dense(n_classes, activation='softmax'))
model.summary()

model.compile(optimizer=Adam(learning_rate = 0.0025), loss = 'sparse_categorical_crossentropy', metrics = ['accuracy'])

history =model.fit(X_train, y_train, batch_size=32, epochs=100)