我是pytorch的新手。我正在尝试进行交叉验证，我发现了skorch库，该库允许用户将sklearn函数与割炬模型一起使用。因此，我定义了一个神经网络类：
torch.manual_seed(42)
class Netcross(nn.Module):
def __init__(self):
super().__init__()
self.fc1 = nn.Linear(5,30)
self.sig1 = nn.Tanh()
#self.dout = nn.Dropout(0.2)
self.fc2 = nn.Linear(30,30)
self.sig2 = nn.Sigmoid()
self.out = nn.Linear(30, 1)
self.out_act = nn.Sigmoid()
#self.fc1.weight = torch.nn.Parameter(torch.rand(50,5))
def forward(self, x):
x = self.fc1(x)
x = self.sig1(x)
#x = self.dout(x)
x = self.fc2(x)
x = self.sig2(x)
x = self.out(x)
y = self.out_act(x)
return y
crossnet1 = NeuralNet(
Netcross,
max_epochs = 5,
criterion=torch.nn.BCELoss,
#user defined coeff.
callbacks = [epoch_acc, epoch_f1, epoch_phi],
optimizer=torch.optim.SGD,
optimizer__momentum=0.9,
lr=0.85,
)
inputs = Variable(x_traintensor)
labels = Variable(y_traintensor)
crossnet1.fit(inputs, labels)
到目前为止，一切都很好，该函数返回可靠的结果，没有任何错误。当我尝试使用GridSearchCV函数时出现问题：
from sklearn.model_selection import GridSearchCV
param_grid = {'max_epochs':[5, 10, 20],
'lr': [0.1, 0.65, 0.8],
}
gs = GridSearchCV(estimator = crossnet1, param_grid = param_grid, refit = False, cv = 3, scoring = "accuracy")
gs.fit(inputs, labels)
我收到以下错误：
TypeError Traceback (most recent call last)
<ipython-input-41-e1f3dbd9a2b0> in <module>
3 labels1 = torch.from_numpy(np.array(labels))
4
----> 5 gs.fit(inputs1, labels1)
~\Anaconda3\lib\site-packages\sklearn\model_selection\_search.py in fit(self, X, y, groups, **fit_params)
720 return results_container[0]
721
--> 722 self._run_search(evaluate_candidates)
723
724 results = results_container[0]
~\Anaconda3\lib\site-packages\sklearn\model_selection\_search.py in _run_search(self, evaluate_candidates)
1189 def _run_search(self, evaluate_candidates):
1190 """Search all candidates in param_grid"""
-> 1191 evaluate_candidates(ParameterGrid(self.param_grid))
1192
1193
~\Anaconda3\lib\site-packages\sklearn\model_selection\_search.py in evaluate_candidates(candidate_params)
709 for parameters, (train, test)
710 in product(candidate_params,
--> 711 cv.split(X, y, groups)))
712
713 all_candidate_params.extend(candidate_params)
~\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in __call__(self, iterable)
915 # remaining jobs.
916 self._iterating = False
--> 917 if self.dispatch_one_batch(iterator):
918 self._iterating = self._original_iterator is not None
919
~\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in dispatch_one_batch(self, iterator)
757 return False
758 else:
--> 759 self._dispatch(tasks)
760 return True
761
~\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in _dispatch(self, batch)
714 with self._lock:
715 job_idx = len(self._jobs)
--> 716 job = self._backend.apply_async(batch, callback=cb)
717 # A job can complete so quickly than its callback is
718 # called before we get here, causing self._jobs to
~\Anaconda3\lib\site-packages\sklearn\externals\joblib\_parallel_backends.py in apply_async(self, func, callback)
180 def apply_async(self, func, callback=None):
181 """Schedule a func to be run"""
--> 182 result = ImmediateResult(func)
183 if callback:
184 callback(result)
~\Anaconda3\lib\site-packages\sklearn\externals\joblib\_parallel_backends.py in __init__(self, batch)
547 # Don't delay the application, to avoid keeping the input
548 # arguments in memory
--> 549 self.results = batch()
550
551 def get(self):
~\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in __call__(self)
223 with parallel_backend(self._backend, n_jobs=self._n_jobs):
224 return [func(*args, **kwargs)
--> 225 for func, args, kwargs in self.items]
226
227 def __len__(self):
~\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in <listcomp>(.0)
223 with parallel_backend(self._backend, n_jobs=self._n_jobs):
224 return [func(*args, **kwargs)
--> 225 for func, args, kwargs in self.items]
226
227 def __len__(self):
~\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py in _fit_and_score(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, return_estimator, error_score)
516 start_time = time.time()
517
--> 518 X_train, y_train = _safe_split(estimator, X, y, train)
519 X_test, y_test = _safe_split(estimator, X, y, test, train)
520
~\Anaconda3\lib\site-packages\sklearn\utils\metaestimators.py in _safe_split(estimator, X, y, indices, train_indices)
201 X_subset = X[np.ix_(indices, train_indices)]
202 else:
--> 203 X_subset = safe_indexing(X, indices)
204
205 if y is not None:
~\Anaconda3\lib\site-packages\sklearn\utils\__init__.py in safe_indexing(X, indices)
214 indices.dtype.kind == 'i'):
215 # This is often substantially faster than X[indices]
--> 216 return X.take(indices, axis=0)
217 else:
218 return X[indices]
TypeError: take(): argument 'index' (position 1) must be Tensor, not numpy.ndarray
怎么了？
答案 0 :(得分：2)
将您的input和labels更改为np.ndarray（请参见示例here）。
torch.Tensor在需要时会自动将其强制转换为skorch。
全部改变您的
inputs = Variable(x_traintensor)
labels = Variable(y_traintensor)
收件人：
inputs = x_traintensor.numpy() # assuming x is torch.Tensor
labels = y_traintensor.numpy() # assuming y is torch.Tensor
顺便说一句。 torch.Variable已过时，您应该使用torch.Tensor(data, requires_grad=True)。在这种情况下，输入和标签不需要不需要渐变，因此Variable甚至不合适。