(1): Conv2d(1, 6, kernel_size=(5, 5), stride=(1, 1)) (2): ReLU() (3): AvgPool2d(kernel_size=2, stride=2, padding=0) ) (conv2): Sequential( (0): Conv2d(6, 16, kernel_size=(5, 5), stride=(1, 1)) (1): ReLU() (2): AvgPool2d(kernel_size=2, stride=2, pa...
nn.MaxPool2d(kernel_size=3, stride=2), # output[48, 27, 27] nn.Conv2d(48, 128, kernel_size=5, padding=2), # output[128, 27, 27] nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=3, stride=2), # output[128, 13, 13] nn.Conv2d(128, 192, kernel_size=3, padding=1), ...
(in_channels=12, out_channels=12, kernel_size=5, stride=1, padding=1) self.bn2 = nn.BatchNorm2d(12) self.pool = nn.MaxPool2d(2,2) self.conv4 = nn.Conv2d(in_channels=12, out_channels=24, kernel_size=5, stride=1, padding=1) self.bn4 = nn.BatchNorm2d(24) self.conv5 =...
和LeNet-5相比,这里去掉了最后一层的高斯激活。 代码语言:javascript 代码运行次数:0 运行 AI代码解释 importtorch from torchimportnn from d2limporttorchasd2l net=nn.Sequential(nn.Conv2d(1,6,kernel_size=5,padding=2),nn.Sigmoid(),nn.AvgPool2d(kernel_size=2,stride=2),nn.Conv2d(6,16,kernel_si...
self.branch_pool = nn.MaxPool2d(kernel_size=3, stride=1, padding=1)self.relu = nn.ReLU()self.concat = nn.Conv2d(out_channels * 4, out_channels, kernel_size=1)def forward(self, x):out1 = self.branch1x1(x)out2 = self.branch3x3(x)out3 = self.branch5x5(x)out4 = self....
kernel_size 卷积核的大小,一般我们会使用5×5、3×3这种左右两个数相同的卷积核,因此这种情况只需要写kernel_size = 5这样的就行了。如果左右两个数不同,比如3×5的卷积核,那么写作kernel_size = (3, 5),注意需要写一个tuple,而不能写一个列表(list)。
自适应池化Adaptive Pooling与标准的Max/AvgPooling区别在于,自适应池化Adaptive Pooling会根据输入的参数来控制输出output_size,而标准的Max/AvgPooling是通过kernel_size,stride与padding来计算output_size。 adaverage_pool=nn.AdaptiveAvgPool2d(output_size=(100,100))# 输出大小的尺寸指定为100*100 ...
nn.Conv2d(num_channels, 64, kernel_size=5, padding=5//2), nn.Tanh(), nn.Conv2d(64, 32, kernel_size=3, padding=3//2), nn.Tanh(), ) self.last_part = nn.Sequential( nn.Conv2d(32, num_channels * (scale_factor ** 2), kernel_size=3, padding=3 // 2), ...
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) out = out.reshape(out.size(0...
()self.gate_conv = nn.Conv2d(gate_in_channel, gate_in_channel, kernel_size=1, stride=1)self.residual_conv = nn.Conv2d(residual_in_channel, gate_in_channel, kernel_size=1, stride=1)self.in_conv = nn.Conv2d(gate_in_channel, 1, kernel_size...