pytorch中的model.eval()和BN层
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class ConvNet(nn.module): -
def __init__(self, num_class=10): -
super(ConvNet, self).__init__() -
self.layer1 = nn.Sequential(nn.Conv2d(1, 16, kernel_size=5, stride=1, padding=2), -
nn.BatchNorm2d(16), -
nn.ReLU(), -
nn.MaxPool2d(kernel_size=2, stride=2)) -
self.layer2 = nn.Sequential(nn.Conv2d(16, 32, kernel_size=5, stride=1, padding=2), -
nn.BatchNorm2d(32), -
nn.ReLU(), -
nn.MaxPool2d(kernel_size=2, stride=2)) -
self.fc = nn.Linear(7*7*32, num_classes) -
def forward(self, x): -
out = self.layer1(x) -
out = self.layer2(out) -
print(out.size()) -
out = out.reshape(out.size(0), -1) -
out = self.fc(out) -
return out
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# Test the model -
model.eval() # eval mode (batchnorm uses moving mean/variance instead of mini-batch mean/variance) -
with torch.no_grad(): -
correct = 0 -
total = 0 -
for images, labels in test_loader: -
images = images.to(device) -
labels = labels.to(device) -
outputs = model(images) -
_, predicted = torch.max(outputs.data, 1) -
total += labels.size(0) -
correct += (predicted == labels).sum().item()
如果网络模型model中含有BN层，则在预测时应当将模式切换为评估模式，即model.eval()。
评估模拟下BN层的均值和方差应该是整个训练集的均值和方差，即 moving mean/variance。
训练模式下BN层的均值和方差为mini-batch的均值和方差，因此应当特别注意。
转载于:https://www.cnblogs.com/jiangkejie/p/9983451.html
