def evaluate_accuracy(data_iter, net):
acc_sum, n = 0.0, 0
for X, y in data_iter:
acc_sum += (net(X).argmax(dim=1) == y).float().sum().item()
n += y.shape[0]
return acc_sum / n
print(evaluate_accuracy(test_iter, net))
RuntimeError Traceback (most recent call last)
in
6 n += y.shape[0]
7 return acc_sum / n
----> 8 print(evaluate_accuracy(test_iter, net))
in evaluate_accuracy(data_iter, net)
3 acc_sum, n = 0.0, 0
4 for X, y in data_iter:
----> 5 acc_sum += (net(X).argmax(dim=1) == y).float().sum().item()
6 n += y.shape[0]
7 return acc_sum / n
in net(X)
1 def net(X):
----> 2 return softmax(torch.mm(X.view((-1, num_inputs)), W) + b)
RuntimeError: The size of tensor a (10) must match the size of tensor b (3) at non-singleton dimension 1
**版本信息**
pytorch:
torchvision:
torchtext:
...
正常copy，出现下面问题，不知道怎么处理，求帮助
def evaluate_accuracy(data_iter, net): acc_sum, n = 0.0, 0 for X, y in data_iter: acc_sum += (net(X).argmax(dim=1) == y).float().sum().item() n += y.shape[0] return acc_sum / n print(evaluate_accuracy(test_iter, net))
RuntimeError Traceback (most recent call last)