LeNet, AlexNet, VGG-16, SENet.zip

资源文件列表(大概)

文件名

大小

AlexNet.py

1.5KB

AlexNet_2.png

391.04KB

lenet.png

320.05KB

lenet.py

1.2KB

SEBlock.png

40.57KB

SEBlock.py

664B

VGG-16.png

155.11KB

vgg16.py

1.82KB

资源内容介绍

LeNet, AlexNet, VGG-16, SENet.zip

import torchimport torch.nn as nnclass VGG16(nn.Module): def __init__(self): super(VGG16, self).__init__() self.features = nn.Sequential( nn.Conv2d(3, 64, 3, 1, 1), nn.ReLU(inplace=True), nn.Conv2d(64, 64, 3, 1, 1), nn.ReLU(inplace=True), nn.MaxPool2d(2, 2), nn.Conv2d(64, 128, 3, 1, 1), nn.ReLU(inplace=True), nn.Conv2d(128, 128, 3, 1, 1), nn.ReLU(inplace=True), nn.MaxPool2d(2, 2), nn.Conv2d(128, 256, 3, 1, 1), nn.ReLU(inplace=True), nn.Conv2d(256, 256, 3, 1, 1), nn.ReLU(inplace=True), nn.Conv2d(256, 256, 3, 1, 1), nn.ReLU(inplace=True), nn.MaxPool2d(2, 2), nn.Conv2d(256, 512, 3, 1, 1), nn.ReLU(inplace=True), nn.Conv2d(512, 512, 3, 1, 1), nn.ReLU(inplace=True), nn.Conv2d(512, 512, 3, 1, 1), nn.ReLU(inplace=True), nn.MaxPool2d(2, 2), nn.Conv2d(512, 512, 3, 1, 1), nn.ReLU(inplace=True), nn.Conv2d(512, 512, 3, 1, 1), nn.ReLU(inplace=True), nn.Conv2d(512, 512, 3, 1, 1), nn.ReLU(inplace=True), nn.MaxPool2d(2, 2), ) self.fc = nn.Sequential( nn.Linear(7*7*512, 4096), nn.ReLU(inplace=True), nn.Linear(4096, 4096), nn.ReLU(inplace=True), nn.Linear(4096, 1000), ) def forward(self, x): x = self.features(x) x = x.reshape(x.shape[0], -1) x = self.fc(x) return xx = torch.randn(4, 3, 224, 224).cuda()model = VGG16().cuda()output = model(x)print(output.shape)

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LeNet, AlexNet, VGG-16, SENet.zip

资源文件列表(大概)

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