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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.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)       
          x = F.relu(self.lin1(x))
          x = F.relu(self.lin2(x))
          x = self.lin3(x)
          return x
                 
net = Netzwerk()
net = net.cuda()
learning_rate = 0.3
batch_size = 50
optimizer = optim.SGD(net.parameters(), lr=learning_rate, weight_decay=0.003)
vis_ev = []
vis_loss = []

def train(epoch = 3000, b_s = batch_size, learning_rate = 0.8, progress = []):
          net.train()
          for i in range(epoch):
              random_batch = [np.random.randint(len(X_train)) for i in range(b_s)]
              # input-Werte
              my_in = np.swapaxes(X_train[random_batch], 1, 2)
              my_in = Variable(torch.Tensor(my_in))
              my_in.unsqueeze(2)
              # target-Werte
              ziel = y_train[y_train.index[random_batch]].tolist()
              zielw = Variable(torch.Tensor(ziel))
              zielw = zielw.type(torch.LongTensor)
              #cuda
              my_in = my_in.cuda()
              zielw = zielw.cuda()
              # forward prop
              criterion = nn.CrossEntropyLoss()    
              optimizer.zero_grad()       
              out = net(my_in)
              #cuda
              out = out.cuda()
              if net.current_epoch >= 1000 and net.current_epoch < 2500:
                  optimizer.param_groups[0]['lr'] = 0.06
              if net.current_epoch >= 2500:
                  optimizer.param_groups[0]['lr'] = 0.01
              loss = criterion(out, zielw)
              #backward prop
              loss.backward()                     
              optimizer.step()               
              net.history_loss.append(loss)
              if i%50 == 0:
                  eval = evaluate()
                  print('\r', net.current_epoch, '/', epoch, "Loss: " + "{:2.5f}".format(loss.item()), 
                                                             "Evaluation: " + "{:2.5f}".format(eval))
                  net.history_eval.append(eval)
                  net.classific_accuracy_training.append(evaluate(test_x=X_train[:2000], test_y=y_train[:2000]))
              net.current_epoch += 1          
def evaluate(test_x = X_evaluation, test_y = y_evaluation, werte_vgl=False):
  net.eval()
  """ermittelt, wie oft das Netz ein richtig zugeordnetes EKG erkennt
  mittels 'werte_vgl=True' kann man sich die Label der richtig erkannten EKGs ausgeben lassen"""
  z = 0
  
  for i in range(len(test_x)):
      x = np.swapaxes(test_x[i], 0, 1)
      x = Variable(torch.Tensor(x))
      x = x.unsqueeze(0)
      #cuda
      x = x.cuda()
      if torch.argmax(net(x)) == test_y[test_y.index[i]]:
          z += 1
      if werte_vgl:
          print(net(x)[0], test_y[test_y.index[i]])
  return z/len(test_x)
train()

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