class Test: def __init__(self, classes, path=0): self.net = AlexNet() self.classes = classes self.load(path) def load(self, path=0): if isinstance(path, int): name = self.net.__class__.__name__ path = "./paras/{0}{1}.pt".format( name, path ) #加载参数, map_location...
Abstract 摘要 We trained a large, deep convolutional neural network to classify the1.2 million high-resolution images in the ImageNet LSVRC-2010 contestinto the 1000 different classes. On the test data, we achieved top-1 and top5 error rates of 37.5% and 17.0% which is considerably better th...
Abstract 摘要 We trained a large, deep convolutional neural network to classify the1.2 million high-resolution images in the ImageNet LSVRC-2010 contestinto the 1000 different classes. On the test data, we achieved top-1 and top5 error rates of 37.5% and 17.0% which is considerably b...
4096),nn.ReLU(inplace=True),nn.Dropout(),nn.Linear(4096,4096),nn.ReLU(inplace=True),nn.Linear(4096,num_classes),)defforward(self,x):x=self.features(x)x=self.avgpool(x)x=x.view(x.size(0),-1)x=self.classifier(x)returnxdefalexnet(pretrain=False,progress=True,**kwargs):r""...
We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. 我们训练了一个大型的深卷积神经网络,将ImageNet LSVRC-2010竞赛中的120万张高分辨率图像分成1000个不同的类别。
我们的神经网络架构有6000万参数。尽管ILSVRC的1000类使每个训练样本从图像到标签的映射上强加了10比特的约束,但这不足以学习这么多的参数而没有相当大的过拟合。下面,我们会描述我们用来克服过拟合的两种主要方式。 4.1 数据增强 图像数据上最简单常用的用来减少过拟合的方法是使用标签保留变换(例如[25, 4, 5])...
AlexNet 上图是论文的网络的结构图,包括5个卷积层和3个全连接层,作者还特别强调,depth的重要性,少一层结果就会变差,所以这种超参数的调节可真是不简单. 激活函数 ...
Our neural network architecture has 60 million parameters. Although the 1000 classes of ILSVRC make each training example impose 10 bits of constraint on the mapping from image to label, this turns out to be insufficient to learn so many parameters without considerable overfitting. Below, we descri...
Our neural network architecture has 60 million parameters. Although the 1000 classes of ILSVRC make each training example impose 10 bits of constraint on the mapping from image to label, this turns out to be insufficient to learn so many parameters without considerable overfitting. Below, we...
class AlexNet(nn.Module): def __init__(self, num_classes=1000, init_weights=False): super(AlexNet, self).__init__() self.features = nn.Sequential( nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=3, stride=2), nn.Conv...