我正在建立一个基本的神经网络来学习pytorch。尝试训练它总是失败,并显示消息“标量类型为Float的预期对象,但参数#4'mat1'的标量类型为Double”。我怀疑将数据放在一起时做错了事,但我不知道该怎么办。
有问题的数据是我生成的几个一维数字列表,应该是线性可分离的。
我在下面粘贴了我的代码。
class MyDataset(Dataset):
def __init__(self, xs, ys):
assert len(xs) == len(ys), "Input and output tensors must be the same length"
self.xs = np.array(xs, dtype=np.double)
self.ys = np.array(ys, dtype=np.double)
def __getitem__(self, idx):
return (self.xs[idx], self.ys[idx])
def __len__(self):
return len(self.xs)
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.layer1 = nn.Linear(1, 1)
def forward(self, x):
x = F.relu(self.layer1(x))
return x
def train(data, validation, net, epochs=100):
learning_rate = 0.01
optimizer = optim.SGD(net.parameters(), lr=learning_rate)
criterion = nn.MSELoss()
for epoch in range(0, epochs):
print('Beginning epoch ', epoch+1)
training_losses = []
validation_losses = []
for x_batch, y_batch in data:
optimizer.zero_grad()
yhat = net(x_batch)
loss = criterion(y_batch, yhat)
loss.backward()
optimizer.step()
optimizer.zero_grad()
training_losses.append(loss)
with torch.no_grad():
for x_batch, y_batch in validation:
net.eval()
yhat = net(x_batch)
loss = criterion(y_batch, yhat)
validation_losses.append(loss)
print('Ending epoch ', epoch+1, 'Training loss: ', np.mean(training_losses), 'Validation loss: ', np.mean(validation_losses))
这就是我生成数据并尝试对其进行训练的方式:
num_samples = 10000
foos = [100 + np.random.normal(scale=20) for x in range(0, num_samples)]
bars = [200 + np.random.normal(scale=20) for x in range(0, num_samples)]
xs = foos + bars
xs = torch.tensor([[x] for x in xs])
ys = np.concatenate([np.zeros(num_samples), np.ones(num_samples)])
ys = torch.tensor([[y] for y in ys])
dataset = MyDataset(xs, ys)
train_dataset, val_dataset = random_split(dataset, [16000, 4000])
train_loader = DataLoader(dataset=train_dataset, batch_size=16)
val_loader = DataLoader(dataset=val_dataset, batch_size=20)
net = Net()
train(train_loader, val_loader, net)
最后,这是堆栈跟踪:
<ipython-input-114-ab674ae015a5> in train(data, validation, net, epochs)
13 print('x_batch: ', type(x_batch[0].item()))
14 print('y_batch: ', type(y_batch[0].item()))
---> 15 yhat = net(x_batch)
16 loss = criterion(y_batch, yhat)
17 loss.backward()
/usr/local/lib/python3.6/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
487 result = self._slow_forward(*input, **kwargs)
488 else:
--> 489 result = self.forward(*input, **kwargs)
490 for hook in self._forward_hooks.values():
491 hook_result = hook(self, input, result)
<ipython-input-58-ec2e6d981760> in forward(self, x)
5
6 def forward(self, x):
----> 7 x = F.relu(self.layer1(x))
8 return x
/usr/local/lib/python3.6/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
487 result = self._slow_forward(*input, **kwargs)
488 else:
--> 489 result = self.forward(*input, **kwargs)
490 for hook in self._forward_hooks.values():
491 hook_result = hook(self, input, result)
/usr/local/lib/python3.6/site-packages/torch/nn/modules/linear.py in forward(self, input)
65 @weak_script_method
66 def forward(self, input):
---> 67 return F.linear(input, self.weight, self.bias)
68
69 def extra_repr(self):
/usr/local/lib/python3.6/site-packages/torch/nn/functional.py in linear(input, weight, bias)
1350 if input.dim() == 2 and bias is not None:
1351 # fused op is marginally faster
-> 1352 ret = torch.addmm(torch.jit._unwrap_optional(bias), input, weight.t())
1353 else:
1354 output = input.matmul(weight.t())
RuntimeError: Expected object of scalar type Float but got scalar type Double for argument #4 'mat1'
我试图通过在train方法中记录x_batch和y_batch的类型来进行调试,但是它们都显示为float,因此我对Double的来源感到困惑。
有什么建议吗?
答案 0 :(得分:1)
PyTorch默认使用单精度浮点数。
在行中:
self.xs = np.array(xs, dtype=np.double)
self.ys = np.array(ys, dtype=np.double)
将np.double
替换为np.float32
。