我正在训练一个 CNN 模型。我在为我的模型进行训练迭代时遇到了问题。代码如下:
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
#convo layers
self.conv1 = nn.Conv2d(3,32,3)
self.conv2 = nn.Conv2d(32,64,3)
self.conv3 = nn.Conv2d(64,128,3)
self.conv4 = nn.Conv2d(128,256,3)
self.conv5 = nn.Conv2d(256,512,3)
#pooling layer
self.pool = nn.MaxPool2d(2,2)
#linear layers
self.fc1 = nn.Linear(512*5*5,2048)
self.fc2 = nn.Linear(2048,1024)
self.fc3 = nn.Linear(1024,133)
#dropout layer
self.dropout = nn.Dropout(0.3)
def forward(self, x):
#first layer
x = self.conv1(x)
x = F.relu(x)
x = self.pool(x)
#x = self.dropout(x)
#second layer
x = self.conv2(x)
x = F.relu(x)
x = self.pool(x)
#x = self.dropout(x)
#third layer
x = self.conv3(x)
x = F.relu(x)
x = self.pool(x)
#x = self.dropout(x)
#fourth layer
x = self.conv4(x)
x = F.relu(x)
x = self.pool(x)
#fifth layer
x = self.conv5(x)
x = F.relu(x)
x = self.pool(x)
#x = self.dropout(x)
#reshape tensor
x = x.view(-1,512*5*5)
#last layer
x = self.dropout(x)
x = self.fc1(x)
x = F.relu(x)
x = self.dropout(x)
x = self.fc2(x)
x = F.relu(x)
x = self.fc3(x)
return x
#loss func
criterion = nn.MSELoss()
optimizer = optim.Adam(net.parameters(), lr = 0.0001)
#criterion = nn.CrossEntropyLoss()
#optimizer = optim.SGD(net.parameters(), lr = 0.05)
def train(n_epochs,model,loader,optimizer,criterion,save_path):
for epoch in range(n_epochs):
train_loss = 0
valid_loss = 0
#training
net.train()
for batch, (data,target) in enumerate(loaders['train']):
optimizer.zero_grad()
outputs = net(data)
#print(outputs.shape)
loss = criterion(outputs,target)
loss.backward()
optimizer.step()
当我使用 CrossEntropy Loss 函数和 SGD 优化器时,我能够毫无错误地训练模型。
当我使用 MSE 损失函数和 Adam 优化器时,我面临以下错误:
RuntimeError Traceback (most recent call last) <ipython-input-20-2223dd9058dd> in <module>
1 #train the model
2 n_epochs = 2
----> 3 train(n_epochs,net,loaders,optimizer,criterion,'saved_model/dog_model.pt')
<ipython-input-19-a93d145ef9f7> in train(n_epochs, model, loader, optimizer, criterion, save_path)
22
23 #calculate loss
---> 24 loss = criterion(outputs,target)
25
26 #backward prop
RuntimeError: The size of tensor a (133) must match the size of tensor b (10) at non-singleton dimension 1.
选择的损失函数和优化器是否影响模型的训练?任何人都可以帮忙吗?
最佳答案
好吧,错误是因为 nn.MSELoss()
和 nn.CrossEntropyLoss()
期待不同 input
/target
组合。您不能在不适当更改输入和目标的情况下简单地更改标准函数。从文档:
nn.CrossEntropyLoss
:
- Input:
- (N, C) where C = number of classes, or
- (N, C, d_1, d_2, ..., d_K) with K >= 1 in the case of K-dimensional loss.
- Target:
- (N) where each value is in range [0, C-1] or
- (N, d_1, d_2, ..., d_K) with K >= 1 in the case of K-dimensional loss.
nn.MSELoss
:
- Input:
- (N,∗) where ∗ means, any number of additional dimensions.
- Target:
- (N,∗), same shape as the input
如您所见,在 MSELoss 中,Target 期望与输入具有相同的形状,而在 CrossEntropyLoss 中,
C
维度下降。您不能使用 MSELoss 作为 CrossEntropyLoss 的替代品。
关于python-3.x - 运行时错误 : The size of tensor a (133) must match the size of tensor b (10) at non-singleton dimension 1,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/56783182/