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<code> class Netzwerk(nn.Module):

  def __init__(self):
      super(Netzwerk, self).__init__()
      self.conv1 = nn.Conv1d(12, 24, kernel_size=15, stride=1, padding=1)
      self.pool1 = nn.MaxPool1d(kernel_size=3, stride=2, padding=1)
      self.conv2 = nn.Conv1d(24, 32, kernel_size=10, padding=1)
      self.pool2 = nn.MaxPool1d(2, stride=2, padding=1)
      self.conv3 = nn.Conv1d(32, 24, kernel_size=5, padding=1)
      self.pool3 = nn.MaxPool1d(3, stride=2, padding=1)
      self.conv4 = nn.Conv1d(24, 12, kernel_size=3, padding=1)
      self.pool4 = nn.MaxPool1d(3, stride=2, padding=1)

      self.lin1 = nn.Linear(12*61, 40)
      #self.lin2 = nn.Linear(800, 40)
      self.lin3 = nn.Linear(40, 10)
      self.lin4 = nn.Linear(10, 2)

      self.history_loss = []
      self.history_eval = []
      self.classific_accuracy_training = []
      self.current_epoch = 0

  def forward(self, x):
      x = self.pool1(F.relu(self.conv1(x)))
      x = self.pool2(F.relu(self.conv2(x)))
      x = self.pool3(F.relu(self.conv3(x)))
      x = self.pool4(F.relu(self.conv4(x)))
      
      x = x.view(-1, 12*61)       # umwandeln der Shape, sodass Uebergang von Conv. Layer zu linearen layers moeglich
      '''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 num_flat_features(self, x):
    """eigentlich irrelevant???"""
    size = x.size()[1:]
    num = 1
    for i in size:
      num *= i
    return num