I get the error. My code is like this :
X_train, X_test = train_test_split(transactionData, test_size=0.2, random_state=RANDOM_SEED)
X_train = X_train[X_train.Class == 0]
X_train = X_train.drop([‘Class’], axis=1)
#print(type(X_train))
y_test = X_test[‘Class’]
X_test = X_test.drop([‘Class’], axis=1)
X_train = X_train.values
#print(type(X_train))
X_test = X_test.values
y_test = y_test.values
print(y_test.size)
and my model class is :
class Autoencoder(nn.Module):
def init(self):
super(Autoencoder, self).init()
self.encoder = nn.Sequential(
nn.Linear(29, 14),
nn.Tanh(),
nn.Linear(14, 7),
nn.LeakyReLU(),
)
    self.decoder = nn.Sequential(
       nn.Linear(7, 7),
       nn.Tanh(),
       nn.Linear(7, 29),
       nn.LeakyReLU()
    )

def forward(self, x):
    x = self.encoder(x)
    x = self.decoder(x)
    return x

When i write :
model.eval()
with torch.no_grad():
pred_losses = model([y_test.size, y_test])
I get the following errors :

AttributeError                            Traceback (most recent call last)
 in 
10 #         #print(loss)
11 #         pred_losses[‘pred_loss’].append(loss)
—> 12           pred_losses = model([y_test.size, y_test])
13 reconstructionErrorDF = pd.DataFrame(pred_losses)
14 reconstructionErrorDF[‘Class’] = y_test
/data/anaconda/envs/vimal_pytorch/lib/python3.7/site-packages/torch/nn/modules/module.py in call(self, *input, **kwargs)
491             result = self._slow_forward(*input, **kwargs)
492         else:
–> 493             result = self.forward(*input, **kwargs)
494         for hook in self._forward_hooks.values():
495             hook_result = hook(self, input, result)
 in forward(self, x)
17
18     def forward(self, x):
—> 19         x = self.encoder(x)
20         x = self.decoder(x)
21         return x
/data/anaconda/envs/vimal_pytorch/lib/python3.7/site-packages/torch/nn/modules/module.py in call(self, *input, **kwargs)
491             result = self._slow_forward(*input, **kwargs)
492         else:
–> 493             result = self.forward(*input, **kwargs)
494         for hook in self._forward_hooks.values():
495             hook_result = hook(self, input, result)
/data/anaconda/envs/vimal_pytorch/lib/python3.7/site-packages/torch/nn/modules/container.py in forward(self, input)
90     def forward(self, input):
91         for module in self._modules.values():
—> 92             input = module(input)
93         return input
94
/data/anaconda/envs/vimal_pytorch/lib/python3.7/site-packages/torch/nn/modules/module.py in call(self, *input, **kwargs)
491             result = self._slow_forward(*input, **kwargs)
492         else:
–> 493             result = self.forward(*input, **kwargs)
494         for hook in self._forward_hooks.values():
495             hook_result = hook(self, input, result)
/data/anaconda/envs/vimal_pytorch/lib/python3.7/site-packages/torch/nn/modules/linear.py in forward(self, input)
90     @weak_script_method
91     def forward(self, input):
—> 92         return F.linear(input, self.weight, self.bias)
93
94     def extra_repr(self):
/data/anaconda/envs/vimal_pytorch/lib/python3.7/site-packages/torch/nn/functional.py in linear(input, weight, bias)
1402         - Output: :math:(N, *, out\_features)
1403     “”"
-> 1404     if input.dim() == 2 and bias is not None:
1405         # fused op is marginally faster
1406         ret = torch.addmm(bias, input, weight.t())
AttributeError: ‘list’ object has no attribute ‘dim’