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transferlearningnetwork.py
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transferlearningnetwork.py
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# encoding=utf-8
import torch.nn as nn
import torch.nn.functional as F
class Network(nn.Module):
def __init__(self):
super(Network, self).__init__()
self.conv1 = nn.Sequential(
nn.Conv2d(in_channels=3, out_channels=64, kernel_size=(1, 3)),
nn.ReLU()
)
self.conv2 = nn.Sequential(
nn.Conv2d(in_channels=64, out_channels=64, kernel_size=(1, 3)),
nn.ReLU(),
nn.Dropout(0.4),
nn.MaxPool2d(kernel_size=(1, 2), stride=2)
)
self.fc1 = nn.Sequential(
nn.Linear(in_features=64 * 98, out_features=100),
nn.ReLU()
)
self.fc2 = nn.Sequential(
nn.Linear(in_features=100, out_features=2)
)
def forward(self, src, tar):
x_src = self.conv1(src)
x_tar = self.conv1(tar)
x_src = self.conv2(x_src)
x_tar = self.conv2(x_tar)
#print(x_src.shape)
x_src = x_src.reshape(-1, 64 * 98)
x_tar = x_tar.reshape(-1, 64 * 98)
x_src_mmd = self.fc1(x_src)
x_tar_mmd = self.fc1(x_tar)
#x_src = self.fc1(x_src)
#x_tar = self.fc1(x_tar)
#x_src_mmd = self.fc2(x_src)
#x_tar_mmd = self.fc2(x_tar)
y_src = self.fc2(x_src_mmd)
return y_src, x_src_mmd, x_tar_mmd