python v3.7.3， pytorch v0.4.1， using jupyter
我正在努力使用迁移学习创建图像分类器，并以 ResNet50 为基本模型。 我遇到了一个错误，即使在线搜索解决方案，我也不知道如何调试。 我不确定为什么我的尺寸如此偏离。 任何关于该做什么的帮助将不胜感激。 此外，任何关于使用 Pytorch 进行深度学习的书籍都会很棒:)
ResNet 最后一层的代码：
fc_inputs = model.fc.in_features
model.fc = nn.Sequential(OrderedDict([
('fc1', nn.Linear(fc_inputs, 256)),
('relu', nn.ReLU()),
('dropout', nn.Dropout(0.2)),
('fc2', nn.Linear(256, 25)),
('output', nn.LogSoftmax(dim=1))
]))
model.classifier = classifier
print(model)
print(model.classifier[0])
print(model.classifier[3])
optimizer = torch.optim.Adam(model.classifier.parameters(), lr = 0.001)
criterion = nn.CrossEntropyLoss()
scheduler = lr_scheduler.StepLR(optimizer, step_size=5, gamma=0.1)
训练代码：
epochs = 1
steps = 0
running_loss = 0
print_every = 10
for epoch in range(epochs):
for images, labels in dataloaders['train']:
images = images.view(images.shape[0], -1) #this flattens it?
steps += 1
images, labels = images.to(device), labels.to(device)
optimizer.zero_grad()
logps = model(images)
loss = criterion(logps, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
if step % print_every == 0:
model.eval()
test_loss = 0
accuracy = 0
for images, labels in dataloader:
images, labels = images.to(device), labels.to(device)
logps = model(images)
loss = criterion(logps, labels)
test_loss += loss.item()
ps = torch.exp(logps)
top_ps, top_class = ps.topk(1, dim=1)
equality = top_class == labels.view(*top_class.shape)
accuracy += torch.mean(equality.type(torch.FloatTensor)).item()
train_losses.append(running_loss / len(dataloader['train']))
test_losses.append(test_loss / len(dataloader['test']))
print(f"Epoch {epoch + 1}/{epochs}.."
f"Train loss: {running_loss / print_every:.3f}.."
f"Test loss: {test_loss/len(dataloader['test']):.3f}.."
f"Test accuracy: {accuracy/len(dataloader['test']):.3f}")
running_loss = 0
model.train()
错误：
RuntimeError Traceback (most recent call last)
<ipython-input-47-8f88694d7909> in <module>
16 optimizer.zero_grad()
17
---> 18 logps = model(images)
19
20 loss = criterion(logps, labels)
~Anaconda3libsite-packagestorchnnmodulesmodule.py in __call__(self, *input, **kwargs)
475 result = self._slow_forward(*input, **kwargs)
476 else:
--> 477 result = self.forward(*input, **kwargs)
478 for hook in self._forward_hooks.values():
479 hook_result = hook(self, input, result)
~Anaconda3libsite-packagestorchvisionmodelsresnet.py in forward(self, x)
137
138 def forward(self, x):
--> 139 x = self.conv1(x)
140 x = self.bn1(x)
141 x = self.relu(x)
~Anaconda3libsite-packagestorchnnmodulesmodule.py in __call__(self, *input, **kwargs)
475 result = self._slow_forward(*input, **kwargs)
476 else:
--> 477 result = self.forward(*input, **kwargs)
478 for hook in self._forward_hooks.values():
479 hook_result = hook(self, input, result)
~Anaconda3libsite-packagestorchnnmodulesconv.py in forward(self, input)
299 def forward(self, input):
300 return F.conv2d(input, self.weight, self.bias, self.stride,
--> 301 self.padding, self.dilation, self.groups)
302
303
RuntimeError: Expected 4-dimensional input for 4-dimensional weight [64, 3, 7, 7], but got input of size [8, 196608] instead
编辑：我删除了images = images.view.shape[0], -1，它将我的张量转换为[8, 196608]。 但是现在我收到一个新错误：
RuntimeError: size mismatch, m1: [8 x 8192], m2: [2048 x 256] at c:programdataminiconda3conda-bldpytorch_1532509700152workatensrcthgeneric/THTensorMath.cpp:2070
什么是classifier？为什么要在output之后向模型添加另一层(classifier(？
我的猜测是你不需要classifier，因为你已经有了fc层的softmax输出。尝试：
fc_inputs = model.fc.in_features
model.fc = nn.Sequential(OrderedDict([
('fc1', nn.Linear(fc_inputs, 256)),
('relu', nn.ReLU()),
('dropout', nn.Dropout(0.2)),
('fc2', nn.Linear(256, 25)),
('output', nn.LogSoftmax(dim=1))
]))
optimizer = torch.optim.Adam(model.fc.parameters(), lr = 0.001)
criterion = nn.CrossEntropyLoss()
scheduler = lr_scheduler.StepLR(optimizer, step_size=5, gamma=0.1)