各位先進大家好:
最近在學pytorch,我在官網看tutorial發現一個問題
這是cnn的分類問題,想請教輸出的時候為何不需要指定activation function
照理說應該要用softmax輸出啊?
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
# 1 input image channel, 6 output channels, 3x3 square convolution
# kernel
self.conv1 = nn.Conv2d(1, 6, 3)
self.conv2 = nn.Conv2d(6, 16, 3)
# an affine operation: y = Wx + b
self.fc1 = nn.Linear(16 * 6 * 6, 120) # 6*6 from image dimension
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)
def forward(self, x):
# Max pooling over a (2, 2) window
x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))
# If the size is a square you can only specify a single number
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:] # all dimensions except the batch dimension
num_features = 1
for s in size:
num_features *= s
return num_features
2个回答
是的,多元分类是需要softmax激活函数的。
但是,pytorch中的CrossEntropyLoss已经包含了softmax这个过程了,所以在pytorch中不需要softmax,直接用linear的结果就可以了。可以看下官方文档https://pytorch.org/docs/stable/nn.html#crossentropyloss