self).__init__()##源图像为1*28*28##从一层channel转为输出5层,卷积和是3,所以输出的宽和高就是28-3+1=26##输出的图像就是5*26*26,然后做pooling下采样2, 变为5*13*13self.conv1=nn.Sequential(nn.Conv2d(1,5,kernel_size=3),nn.MaxPool2d(2),nn.ReLU...
kernel_size:卷积核的尺寸,常见的有1、3、5、7。 stride:步长,即卷积核在特征图上滑动的步长,一般为1。如果大于1,则输出特征图的尺寸会小于输入特征图的尺寸。 padding:填充,常见的有零填充、边缘填充等,PyTorch默认为零填充。 dilation:空洞卷积,当大于1时可以增大感受野的同时保持特征图的尺寸(后面会细讲),默...
卷积操作的步长为 1(stride=1),核为 3 (kernel_size=3),不做额外补齐(padding="valid"),输入通道数是 128(in_channels=128),输出通道数是 64(out_channels=64)。channel 可以理解为特征,N 维的特征向量,即包含 N 个通道。有几个输出通道,即有几组滤波器(filters)。 目标输出的序列长度为 seq_length-...
out_channel:输出数据的通道数,也就是kernel数量; kernel_size: 卷积核大小,可以是int,或tuple;kernel_size=2,意味着卷积大小(2,2),kernel_size=(2,3),意味着卷积大小(2,3)即非正方形卷积 stride:步长,默认为1,与kernel_size类似,stride=2,意味着步长上下左右扫描皆为2, stride=(2,3),左右扫描...
in_channels (int) – Number of channels in the input image out_channels (int) – Number of channels produced by the convolution kernel_size (int or tuple) – Size of the convolving kernel stride (int or tuple, optional) – Stride of the convolution. Default: 1 padding (int, tuple or...
kernel_size:卷积核尺寸 stride:步长 padding:填充个数 dilation:空洞卷积大小 groups:分组卷积设置 bias:偏置,在卷积求和之后加上偏置的值 conv_layer = nn.Conv2d(3, 1, 3) # input:(i, o, size) weights:(o, i , h, w),输入为三个通道,卷积核个数1个,输出通道为1 ...
super(MobileNetV1, self).__init__() self.num_classes = num_classes self.entry = nn.Sequential(nn.Conv2d(input_channel, 32, kernel_size=3, stride=1, padding=1, bias=False), nn.BatchNorm2d(32), nn.ReLU6(inplace=True)) self.stage1 = nn.Sequential( ...
(conv2): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1)) (dropout1): Dropout(p=0.25, inplace=False) (dropout2): Dropout(p=0.5, inplace=False) (fc1): Linear(in_features=9216, out_features=128, bias=True) (fc2): Linear(in_features=128, out_features=10, bias=True) )...
net.add_module("conv2",nn.Conv2d(in_channels=64,out_channels=512,kernel_size =3)) net.add_module("pool2",nn.MaxPool2d(kernel_size =2,stride =2)) net.add_module("dropout",nn.Dropout2d(p =0.1)) net.add_module("adaptive_pool",nn.AdaptiveMaxPool2d((1,1))) ...
self.bratch3x3_2= torch.nn.Conv2d(16,24,kernel_size=3,padding=1) self.bratch3x3_3= torch.nn.Conv2d(24,24,kernel_size=3,padding=1) self.pooling= torch.nn.Conv2d(in_channels,24,kernel_size=1)defforward(self,x): bratch1x1=self.bratch1x1(x) ...