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…
torch.nn.Conv2d(in_channels,out_channels,kernel_size,stride=1,padding=0,dilation=1,groups=1,bias=True,padding_mode='zeros',device=None,dtype=None) 官方示例 # With square kernels and equal stridem=nn.Conv2d(16,33,3,stride=2)# non-square kernels and unequal stride and with paddingm=nn....
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...
SimpleCNN( (layer1): Sequential( (conv1): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (relu1): ReLU(inplace=True) (pool1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) ) (layer2): Sequential( (conv2): Conv2d(32...
简介:这篇文章是关于PyTorch中nn.Conv2d函数的详解,包括其函数语法、参数解释、具体代码示例以及与其他维度卷积函数的区别。 1.函数语法格式 nn. Conv2d(in_channels, out_channels, kernel_size,stride=1,padding=0,dilation=1,groups=1,bias=True, padding_mode='zeros') ...
super(ConvNet, self).__init__() self.layer1 = nn.Sequential( nn.Conv2d(1, 16, kernel_size=5, stride=1, padding=2), 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...
calculate 3-dim input, like this: conv2d_circular = torch.nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, padding=0, padding_mode="circular") conv2d_zeros = torch.nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, padding=0, padding_mode="zeros") conv2d_refle...
classNet(nn.Module):def__init__(self):nn.Module.__init__(self)self.conv2d=nn.Conv2d(in_channels=3,out_channels=64,kernel_size=4,stride=2,padding=1)defforward(self,x):print(x.requires_grad)x=self.conv2d(x)returnxprint(net.conv2d.weight)print(net.conv2d.bias) ...
2.2 nn.Conv2d nn.Conv2d: 对多个二维信号进行二维卷积 主要参数: in_channels: 输入通道数 out_channels: 输出通道数, 等价于卷积核个数 kernel_size: 卷积核尺寸, 这个代表着卷积核的大小 stride: 步长, 这个指的卷积核滑动的时候,每一次滑动几个像素。下面看个动图来理解步长的概念:左边那个的步长是 1,...
3. nn.ConvTranspose2d nn.ConvTranspose2d的功能是进行反卷积操作 (1)输入格式 nn.ConvTranspose2d(in_channels, out_channels, kernel_size, stride=1, padding=0, output_padding=0, groups=1, bias=True, dilation=1) (2)参数的含义 in_channels(int) – 输入信号的通道数 ...