构建网络 练习题三道

构建网络1：
import torch.nn as nn
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 16, 3, padding=1)
        self.conv2 = nn.Conv2d(16, 32, 3, padding=1)
        self.conv3 = nn.Conv2d(32, 32, 3, padding=1)
        self.conv4 = nn.Conv2d(32, 32, 3, padding=1)
        self.conv5 = nn.Conv2d(32, 64, 3, padding=1)
        self.fc1 = nn.Linear(64, 10)
    def forward(self, x):
        x = nn.functional.relu(self.conv1(x))
        x= nn.functional.max_pool2d(nn.functional.relu
(self.conv2(x)), 2)
        x = nn.functional.relu(self.conv3(x))
        x = nn.functional.max_pool2d(nn.functional.relu
(self.conv4(x)), 2)
        x = nn.functional.max_pool2d(nn.functional.relu
(self.conv5(x)), 2)
        x = x.view(-1, 64)
        x = self.fc1(x)
        return x
构建网络2：
import torch.nn as nn
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 16, 3, padding=1)
        self.conv2 = nn.Conv2d(16, 32, 3, padding=1)
        self.conv3 = nn.Conv2d(32, 32, 3, padding=1)
        self.conv4 = nn.Conv2d(32, 10, 3, padding=1)
        self.fc1 = nn.Linear(10, 10)
    def forward(self, x):
        x = nn.functional.relu(self.conv1(x))
        x = nn.functional.max_pool2d(nn.functional.relu
(self.conv2(x)), 2)
        x = nn.functional.relu(self.conv3(x))
        x = nn.functional.max_pool2d(nn.functional.relu
(self.conv4(x)), 2)
        x = nn.functional.adaptive_avg_pool2d(x, (1, 1))
        x = x.view(-1, 10)
        x = self.fc1(x)
        return x
构建网络3：
import torch.nn as nn
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 16, 3, padding=1)
        self.conv2 = nn.Conv2d(16, 128, 3, padding=1)
        self.conv3 = nn.Conv2d(128, 256, 3, padding=1)
        self.conv4 = nn.Conv2d(256, 256, 3, padding=1)
        self.conv5 = nn.Conv2d(256, 256, 3, padding=1)
        self.fc1 = nn.Linear(256, 10)
    def forward(self, x):
        x = nn.functional.relu(self.conv1(x))
        x = nn.functional.max_pool2d(nn.functional.relu
(self.conv2(x)), 2)
        x = nn.functional.relu(self.conv3(x))
        x = nn.functional.max_pool2d(nn.functional.relu
(self.conv4(x)), 2)
        x = nn.functional.relu(self.conv5(x))
        x = nn.functional.adaptive_avg_pool2d(x, (1, 1))
        x = x.view(-1, 256)
        x = self.fc1(x)
        return x