Mathesis Wiki

class ResNet(nn.Module):
    def __init__(self, block, layers, image_channels):
          super(ResNet, self).__init__()
          
          self.history_loss = []
          self.history_eval = []
          self.classific_accuracy_training = []
          self.current_epoch = 0
          
          self.in_channels = 12
          self.conv1 = nn.Conv1d(12, 12, kernel_size=7, stride=2, padding=3)
          self.bn1 = nn.BatchNorm1d(12)
          self.relu = nn.ReLU()
          self.maxpool = nn.MaxPool1d(kernel_size=3, stride=2, padding=1)

          self.layer1 = self._make_layer(block, layers[0], out_channels=12, stride=1)
          self.layer2 = self._make_layer(block, layers[1], out_channels=24, stride=2)
          self.layer3 = self._make_layer(block, layers[2], out_channels=24*2, stride=2)
          self.layer4 = self._make_layer(block, layers[3], out_channels=24*4, stride=2)

          self.avgpool = nn.AdaptiveAvgPool1d(25)
          self.lin1 = nn.Linear(4800, 800)
          self.lin2 = nn.Linear(800, 40)
          self.lin3 = nn.Linear(40, 10)
          self.lin4 = nn.Linear(10, 2)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)
        
        x = self.layer1(x)
        x = self.layer2(x)
        
        x = self.layer3(x)
        x = self.layer4(x)
        
        x = self.avgpool(x)
        x = x.reshape(x.shape[0], -1)   # eventuell auf x.view Funktion umsteigen
        
        x = F.relu(self.lin1(x))
        x = F.relu(self.lin2(x))
        x = F.relu(self.lin3(x))
        x = self.lin4(x)
        return x

    def _make_layer(self, block, num_residual_blocks, out_channels, stride):
        identity_downsample = None
        layers = []

        if stride != 1 or self.in_channels != out_channels*2:
            identity_downsample = nn.Sequential(nn.Conv1d(self.in_channels, out_channels*2, kernel_size=1, 
            stride=stride),
                                                nn.BatchNorm1d(out_channels*2))

        layers.append(block(self.in_channels, out_channels, identity_downsample, stride=stride))

        
        self.in_channels = out_channels*2
        
        for i in range(num_residual_blocks -1):
            layers.append(block(self.in_channels, out_channels))
        
        return nn.Sequential(*layers)
    
    
def ResNet50(img_channels=12):
    return ResNet(block, [3, 4, 6, 3], img_channels)

def test():
    net = ResNet50()
    x = torch.randn(30, 12, 1000)
    y = net(x) 
    return y

net = ResNet50()
learning_rate = 0.15
batch_size = 50
optimizer = optim.SGD(net.parameters(), lr=learning_rate)