AttributeError: 'tuple' object has no attribute 'clone'

[hanhandian]

When I explained LSTM with captum, the following error occurred： Traceback (most recent call last): File "D:\论文机器学习模型\shiyan.py", line 210, in attributions_ig, delta_ig = lig.attribute(x_test[0:128], target=0, return_convergence_delta=True) File "D:\APP\Anaconda\envs\pytorch\lib\site-packages\captum\log__init__.py", line 42, in wrapper return func(*args, *kwargs) File "D:\APP\Anaconda\envs\pytorch\lib\site-packages\captum\attr_core\layer\layer_integrated_gradients.py", line 371, in attribute inputs_layer = _forward_layer_eval( File "D:\APP\Anaconda\envs\pytorch\lib\site-packages\captum_utils\gradient.py", line 182, in _forward_layer_eval return _forward_layer_eval_with_neuron_grads( File "D:\APP\Anaconda\envs\pytorch\lib\site-packages\captum_utils\gradient.py", line 445, in _forward_layer_eval_with_neuron_grads saved_layer = _forward_layer_distributed_eval( File "D:\APP\Anaconda\envs\pytorch\lib\site-packages\captum_utils\gradient.py", line 294, in _forward_layer_distributed_eval output = _run_forward( File "D:\APP\Anaconda\envs\pytorch\lib\site-packages\captum_utils\common.py", line 531, in _run_forward output = forward_func( File "D:\APP\Anaconda\envs\pytorch\lib\site-packages\torch\nn\modules\module.py", line 1501, in _call_impl return forward_call(args, **kwargs) File "D:\论文机器学习模型\shiyan.py", line 111, in forward x, _ = self.lstm(_x) # _x is input, size (seq_len, batch, input_size) File "D:\APP\Anaconda\envs\pytorch\lib\site-packages\torch\nn\modules\module.py", line 1547, in _call_impl hook_result = hook(self, args, result) File "D:\APP\Anaconda\envs\pytorch\lib\site-packages\captum_utils\gradient.py", line 277, in forward_hook saved_layer[original_module][eval_tsrs[0].device] = tuple( File "D:\APP\Anaconda\envs\pytorch\lib\site-packages\captum_utils\gradient.py", line 278, in eval_tsr.clone() for eval_tsr in eval_tsrs AttributeError: 'tuple' object has no attribute 'clone' Has anyone encountered the same problem？ Help，please！

[hanhandian]

数据分离

def split_data(x, y, split_ratio): train_size = int(len(y) * split_ratio) test_size = len(y) - train_size

x_data = Variable(torch.Tensor(np.array(x))).to(device)
y_data = Variable(torch.Tensor(np.array(y))).to(device)

x_train = Variable(torch.Tensor(np.array(x[0:train_size]))).to(device)
y_train = Variable(torch.Tensor(np.array(y[0:train_size]))).to(device)
y_test = Variable(torch.Tensor(np.array(y[train_size:len(y)]))).to(device)
x_test = Variable(torch.Tensor(np.array(x[train_size:len(x)]))).to(device)

print('x_data.shape,y_data.shape,x_train.shape,y_train.shape,x_test.shape,y_test.shape:\n{}{}{}{}{}{}'
      .format(x_data.shape, y_data.shape, x_train.shape, y_train.shape, x_test.shape, y_test.shape))

return x_data, y_data, x_train, y_train, x_test, y_test

数据装入

def data_generator(x_train, y_train, x_test, y_test, batch_size): train_dataset = Data.TensorDataset(x_train, y_train) test_dataset = Data.TensorDataset(x_test, y_test) train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=False, drop_last=True) # 加载数据集,使数据集可迭代 test_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=False, drop_last=True)

return train_loader, test_loader

Define LSTM Neural Networks

class LSTM(nn.Module): """ Parameters：

• input_size: feature size
• hidden_size: number of hidden units
• output_size: number of output

• num_layers: layers of LSTM to stack """

  def init(self, input_size, hidden_size, output_size, num_layers): super().init() self.hidden_size = hidden_size self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True) self.dropout = nn.Dropout(p=0.2) self.linear1 = nn.Linear(hidden_size, hidden_size 2) # 全连接层 self.linear2 = nn.Linear(hidden_size 2, output_size) # 全连接层 self.num_directions = 1

  def forward(self, _x):

  batch_size, seq_len = _x.shape[0], _x.shape[1]

  # h_0 = torch.randn(self.num_directions * num_layers, batch_size, self.hidden_size).to(device)
  # c_0 = torch.randn(self.num_directions * num_layers, batch_size, self.hidden_size).to(device)
  # _x = _x.reshape((-1, 5, 7))
  x, _ = self.lstm(_x)  # _x is input, size (seq_len, batch, input_size)
  x = self.dropout(x)
  x = self.linear1(x)
  x = self.linear2(x)
  x = x[:, -1, :]
  return x

  This is the code for LSTM