# pretrained=True 加载网络结构和预训练参数,False 时代表只加载网络结构,不加载预训练参数,即不需要用预训练模型的参数来初始化 # pretrained 参数默认是False,为了代码清晰,最好还是加上参数赋值 self.model = models.resnet50(pretrained=True) # 调用模型 fc_features = self.model.fc.in_features # 提取 f...
fc(x) return x def SE_ResNet50(): return SE_ResNet([3, 4, 6, 3]) if __name__=='__main__': model = SE_ResNet50() print(model) input = torch.randn(1, 3, 224, 224) out = model(input) print(out.shape) 文章来源: wanghao.blog.csdn.net,作者:AI浩,版权归原作者所有,...
SE-ResNet的pytorch实现 残差块: class Resiual_block(nn.Module): def __init__(self, in, middle_out, out, kernel_size=3, padding=1): self.out_channel = middle_out super(Resiual_block, self).__init__() self.shortcut = nn.Sequential( nn.Conv2d(nin, nout, kernel_size=1), nn....
注意在input与输出相加前,这里需要一个shortcut层来调整input的通道大小 SE-NET: class SE(nn.Module): def __init__(self, in, middle_out, out, reduce=16): super(SE, self).__init__() self.block = Resiual_block(in, middle_out, out) self.shortcut = nn.Sequential( nn.Conv2d(nin, n...
1.2 SE-ResNet 插到ResNet中,是enhance residual的特征: SE-ResNet class SEBasicBlock(nn.Module): expansion = 1 def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1, base_width=64, dilation=1, norm_layer=None, r=16): ...
1.ResNet18四层残差块: 2. SeNet块设计: 3. 嵌入到四个残差块中: 训练情况 4 这里截取训练过程中的一些结果展示,本次训练显卡为:1080Ti(显存11G)相对较小,批数量bachsize为8。 代码托管至码云:https://gitee.com/fengyuxiexie/pytorch_-se-net ...
SE-Resnet50_33epoch: 1. SE-Resnet,ImageNet2017的冠军 2. 网络模型,50层,训练了33个epoch。 3. top1-96。 Adam: 1. 了解adam和sgd的区别 https://blog.csdn.net/jiachen0212/article/details/80086926 sgd是最初的一种优化算法,深度学习优化算法经历了 SGD -> SGDM -> NAG ->AdaGrad -> AdaDelta...
__all__ = ['SENet', 'se_resnet_18', 'se_resnet_34', 'se_resnet_50', 'se_resnet_101', 'se_resnet_152'] def conv3x3(in_planes, out_planes, stride=1): """3x3 convolution with padding""" return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1,...
StickCui/PyTorch-SE-ResNetPublic NotificationsYou must be signed in to change notification settings Fork40 Star124 master 1Branch 0Tags Code Folders and files Name Last commit message Last commit date Latest commit StickCui Merge branch 'master' ofhttps://github.com/StickCui/PyTorch-SE-ResNet ...
1 ResNet模型 在深度学习领域中,模型越深意味着拟合能力越强,出现过拟合问题是正常的,训练误差越来越大却是不正常的。 1.1 训练误差越来越大的原因 在反向传播中,每一层的梯度都是在上一层的基础上计算的。随着层数越来越多,梯度在多层传播时会越来越小,直到梯度消失,于是随着层数越来越多,训练误差会越来越大...