pytorch 去掉全连接层_在PyTorch中建立神经网络的三种方法
时间: 2024-03-07 11:48:07 浏览: 29
好的,关于你的问题,我知道了。现在回答你的问题:
要在 PyTorch 中去掉全连接层,可以通过以下方法之一实现:
1.使用nn.Sequential()重构模型,从而去掉全连接层。例如,如果模型是以下结构:
```
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16 * 5 * 5, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)
def forward(self, x):
x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))
x = F.max_pool2d(F.relu(self.conv2(x)), 2)
x = x.view(-1, self.num_flat_features(x))
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
def num_flat_features(self, x):
size = x.size()[1:]
num_features = 1
for s in size:
num_features *= s
return num_features
```
可以通过以下方式去掉最后一层全连接层:
```
new_model = nn.Sequential(
*list(model.children())[:-1]
)
```
2.通过子类化 nn.Module 类并重写 forward() 方法来去掉全连接层。例如,
```
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16 * 5 * 5, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)
def forward(self, x):
x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))
x = F.max_pool2d(F.relu(self.conv2(x)), 2)
x = x.view(-1, self.num_flat_features(x))
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
def num_flat_features(self, x):
size = x.size()[1:]
num_features = 1
for s in size:
num_features *= s
return num_features
class NewNet(Net):
def __init__(self):
super(NewNet, self).__init__()
self.fc3 = None
def forward(self, x):
x = super().forward(x)
return x
```
这将新建一个名为 NewNet 的模型,其中最后一层全连接层已被删除。
3.通过使用 nn.ModuleList() 或 nn.Sequential() 来创建一个新的层列表,其中不包括全连接层。例如,
```
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16 * 5 * 5, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)
def forward(self, x):
x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))
x = F.max_pool2d(F.relu(self.conv2(x)), 2)
x = x.view(-1, self.num_flat_features(x))
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
def num_flat_features(self, x):
size = x.size()[1:]
num_features = 1
for s in size:
num_features *= s
return num_features
class NewNet(nn.Module):
def __init__(self):
super(NewNet, self).__init__()
self.layers = nn.ModuleList([
nn.Conv2d(3, 6, 5),
nn.Conv2d(6, 16, 5),
nn.Linear(16 * 5 * 5, 120),
nn.Linear(120, 84)
])
def forward(self, x):
for layer in self.layers:
x = F.relu(layer(x))
return x
```
这将创建一个名为 NewNet 的模型,其中全连接层已被删除,并以 nn.ModuleList() 的形式存储其余层。