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from torch import nn
import torch.nn.functional as F
class BadNet(nn.Module):
# def __init__(self, input_channels, output_num):
def __init__(self, 3072,10):
super().__init__()
self.conv1 = nn.Sequential(
nn.Conv2d(in_channels=input_channels, out_channels=16, kernel_size=5, stride=1),
nn.ReLU(),
nn.AvgPool2d(kernel_size=2, stride=2)
)
self.conv2 = nn.Sequential(
nn.Conv2d(in_channels=16, out_channels=32, kernel_size=5, stride=1),
nn.ReLU(),
nn.AvgPool2d(kernel_size=2, stride=2)
)
fc1_input_features = 800 if input_channels == 3 else 512
self.fc1 = nn.Sequential(
nn.Linear(in_features=fc1_input_features, out_features=512),
nn.ReLU()
)
self.fc2 = nn.Sequential(
nn.Linear(in_features=512, out_features=output_num),
nn.Softmax(dim=-1)
)
self.dropout = nn.Dropout(p=.5)
def forward(self, x):
x = self.conv1(x)
x = self.conv2(x)
x = x.view(x.size(0), -1)
x = self.fc1(x)
x = self.fc2(x)
return x
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