根据提示,在右侧编辑器补充代码,构建神经网络模型,返回模型针对输入张量的输出。 模型结构如下: (0): Conv2d(3, 5, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2)) (1): ReLU() (2): Conv2d(5,10, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0)) (3): ReLU() from torch import nn def net(x): ################################################################################ ############################ END OF YOUR CODE ################################## return m(x)
时间: 2023-09-11 16:12:14 浏览: 86
from torch import nn
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 5, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2))
self.relu1 = nn.ReLU()
self.conv2 = nn.Conv2d(5, 10, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0))
self.relu2 = nn.ReLU()
def forward(self, x):
out = self.conv1(x)
out = self.relu1(out)
out = self.conv2(out)
out = self.relu2(out)
return out
def net(x):
m = Net()
return m(x)