conv = nn.Conv2d(3, 16, kernel_size=3) 这行代码在pytorch中生成了一个简单的二维卷积层,kernel_size=3代表卷积层中卷积核的大小为3x3,这意味着卷积核类似于一个九宫格,每个格子上都有随机生成的权重值。 3代…
感谢反馈，我们会尽快优化报错提示
nn.Conv2d(self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True)) 参数: in_channel: 输入数据的通道数,例RGB图片通道数为3; out_channel: 输出数据的通道数,这个根据模型调整; kennel_size: 卷积核大小,可以是int,或tuple;kenn...
感谢反馈，我们会尽快优化报错提示
Conv2d(inplanes, expplanes1, kernel_size=1, bias=False) self.bn1 = nn.BatchNorm2d(expplanes1) self.act1 = HardSwish(inplace=True) self.se = SqEx(expplanes1) self.avgpool = nn.AdaptiveAvgPool2d(1) self.conv2 = nn.Conv2d(expplanes1, expplanes2, kernel_size=1, stride=1, bias...
MaxPool2d(kernel_size=2)self.cnn2=nn.Conv2d(in_channels=16,out_channels=32,kernel_size=5,...
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...
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...
🐛 Describe the bug import torch import torch.nn as nn input_tensor = torch.randn(1, 8, 14, 14) # (batch_size, in_channels, height, width) conv_transpose = nn.ConvTranspose2d( in_channels=8, out_channels=8, kernel_size=[3, 3], stride=[1, ...
classtorch.nn.Conv2d(in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True, padding_mode='zeros')[source] Applies a 2D convolution over an input signal composed of several input planes. In the simplest case, the output value of the layer with inp...