【发布时间】：2021-03-23 08:08:00
【问题描述】：
我是神经网络的大一学生，我已经建立了一个vgg16网络。但是在每批中，所有输入都导致相同的输出。所以我检查了每一层的输出，最后发现x=x.view(batch_size ,-1) 给出相同的输出！我不知道为什么会发生这种情况。这是我的代码的一部分：
class VGG16(torch.nn.Module):
def __init__(self):
super(VGG16, self).__init__()
self.conv1 = torch.nn.Conv2d(3, 64, padding=1, kernel_size=3) #kernel
self.conv2 = torch.nn.Conv2d(64, 64, padding=1, kernel_size=3)
self.conv3 = torch.nn.Conv2d(64, 128, padding=1, kernel_size=3)
self.conv4 = torch.nn.Conv2d(128, 128, padding=1, kernel_size=3)
self.conv5 = torch.nn.Conv2d(128, 256, padding=1, kernel_size=3)
self.conv6 = torch.nn.Conv2d(256, 256, padding=1, kernel_size=3)
self.conv7 = torch.nn.Conv2d(256, 256, padding=1, kernel_size=3)
self.conv8 = torch.nn.Conv2d(256, 512, padding=1 ,kernel_size=3)
self.conv9 = torch.nn.Conv2d(512, 512, padding=1, kernel_size=3)
self.conv10 = torch.nn.Conv2d(512, 512, padding=1, kernel_size=3)
self.conv11 = torch.nn.Conv2d(512, 512, padding=1, kernel_size=3)
self.conv12 = torch.nn.Conv2d(512, 512, padding=1, kernel_size=3)
self.conv13 = torch.nn.Conv2d(512, 512, padding=1, kernel_size=3)
self.pooling = torch.nn.MaxPool2d(2) #pool
self.fc1 = torch.nn.Linear(25088, 4096) # 7 * 7 * 512 = 25088
self.fc2 = torch.nn.Linear(4096, 4096)
self.fc3 = torch.nn.Linear(4096, 2)
def forward(self,x):
batch_size = x.size(0)
x = F.relu(self.conv1(x)) #layer1
x = self.pooling(F.relu(self.conv2(x))) #layer2
x = F.relu(self.conv3(x)) #layer3
x = self.pooling(F.relu(self.conv4(x))) #layer4
x = F.relu(self.conv5(x)) #layer5
x = F.relu(self.conv6(x)) #layer6
x = self.pooling(F.relu(self.conv7(x))) #layer7
x = F.relu(self.conv8(x)) #layer8
x = F.relu(self.conv9(x)) #layer9
x = self.pooling(F.relu(self.conv10(x))) #layer10
x = F.relu(self.conv11(x)) #layer11
x = F.relu(self.conv12(x)) #layer12
x = self.pooling(F.relu(self.conv13(x))) #layer13
x = x.view(batch_size,-1) #flatten
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
这是训练部分：
def train(epoch):
running_loss = 0.0
for batch_idx, data in enumerate(train_loader,0):
inputs, true_labels = data
optimizer.zero_grad() #clear the optimizer to avoid accumulating of grad
#forward
outputs = model(inputs)
loss = criterion(outputs, true_labels)
#backward
loss.backward()
#update
optimizer.step()
running_loss += loss.item()
#output the train result every 10 loop
if (batch_idx + 1) % 10 == 0:
print('[%d %5d] loss: %.3f' %(epoch + 1, batch_idx + 1, running_loss/10 ))
running_loss = 0.0
这是 layer13 的输出（前视图）：enter image description here
这是 x.view 的输出：enter image description here
我在网上搜索了很长时间。但是没有用。有什么想法吗？ 提前致谢。
【问题讨论】：
-
为此请发布您的训练代码...不是模型...（模型很好）
-
我已经发布了我的训练代码和一些输出
-
嘿兄弟，它是正确的......！它只是改变你的尺寸......从 4D 到 2D......（即展平）
标签： machine-learning pytorch conv-neural-network vgg-net