所以我在训练一个神经网络。以下是基本细节:
我错在哪里?
培训:
EPOCHS = 5
LEARNING_RATE = 0.0001
BATCH_SIZE = 64
net = Net().to(device)
optimizer = optim.Adam(net.parameters(), lr=LEARNING_RATE)
loss_log = []
loss_log = train(net, trainSet, loss_log, EPOCHS, LEARNING_RATE, BATCH_SIZE)
列车功能:
def train(net, train_set, loss_log=[], EPOCHS=5, LEARNING_RATE=0.001, BATCH_SIZE=32):
print('Initiating Training..')
loss_func = nn.CrossEntropyLoss()
# Iteration Begins
for epoch in tqdm(range(EPOCHS)):
# Iterate over every sample in the batch
for data in tqdm(trainSet, desc=f'Iteration > {epoch+1}/{EPOCHS} : ', leave=False):
x, y = data
net.zero_grad()
#Compute the output
output, sm = net(x)
# Compute Train Loss
loss = loss_func(output, y.to(device))
# Backpropagate
loss.backward()
# Update Parameters
optimizer.step()
# LEARNING_RATE -= LEARNING_RATE*0.0005
loss_log.append(loss)
lr_log.append(LEARNING_RATE)
return loss_log, lr_log
完全错误:
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-20-8deb9a27d3b4> in <module>()
13
14 total_epochs += EPOCHS
---> 15 loss_log = train(net, trainSet, loss_log, EPOCHS, LEARNING_RATE, BATCH_SIZE)
16
17 plt.plot(loss_log)
4 frames
<ipython-input-9-59e1d2cf0c84> in train(net, train_set, loss_log, EPOCHS, LEARNING_RATE, BATCH_SIZE)
21 # Compute Train Loss
22 # print(output, y.to(device))
---> 23 loss = loss_func(output, y.to(device))
24
25 # Backpropagate
/usr/local/lib/python3.6/dist-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)
/usr/local/lib/python3.6/dist-packages/torch/nn/modules/loss.py in forward(self, input, target)
914 def forward(self, input, target):
915 return F.cross_entropy(input, target, weight=self.weight,
--> 916 ignore_index=self.ignore_index, reduction=self.reduction)
917
918
/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py in cross_entropy(input, target, weight, size_average, ignore_index, reduce, reduction)
2019 if size_average is not None or reduce is not None:
2020 reduction = _Reduction.legacy_get_string(size_average, reduce)
-> 2021 return nll_loss(log_softmax(input, 1), target, weight, None, ignore_index, None, reduction)
2022
2023
/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py in nll_loss(input, target, weight, size_average, ignore_index, reduce, reduction)
1836 .format(input.size(0), target.size(0)))
1837 if dim == 2:
-> 1838 ret = torch._C._nn.nll_loss(input, target, weight, _Reduction.get_enum(reduction), ignore_index)
1839 elif dim == 4:
1840 ret = torch._C._nn.nll_loss2d(input, target, weight, _Reduction.get_enum(reduction), ignore_index)
RuntimeError: multi-target not supported at /pytorch/aten/src/THCUNN/generic/ClassNLLCriterion.cu:15
你自己写下了这个问题:
您需要将目标更改为一个热编码。此外,如果你在做二元分类,我建议改变模型,返回单个输出单元,并使用二元交叉熵作为损失函数
问题是你的目标张量是二维的(
[64,1]
而不是[64]
),这使得PyTorch认为你每个数据都有一个以上的基本真理标签。这很容易通过loss_func(output, y.flatten().to(device))
解决。希望这有帮助相关问题 更多 >
编程相关推荐