#Не работает
def tryer(i):
    body = []
    header = [nn.Linear(len(feature_columns), 100),
        nn.ReLU(),
        nn.Dropout(p=0.5)]
    tail = [
        nn.Linear(100, 3)]
    for _ in range(i):
        body += [
        nn.Linear(100, 100),
        nn.ReLU(),
        nn.BatchNorm1d(100)]

    K = [*header, *body, *tail]
    model = nn.Sequential(*K)
    loss_fn = torch.nn.CrossEntropyLoss()
    optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
    train_losses, test_losses = train(20)
    plt.plot(train_losses)
    plt.show()
    m = min(train_losses)
    return [m, i]

mi = []
it = []
for i in range(5):
    torch.manual_seed(42)
    np.random.seed(42)
    if(i == 0):
      continue
    T = tryer(i)
    mi.append(T[0])
    it.append(T[1])
plt.plot(mi)

#Работает

mi = []
for i in range(10):
  torch.manual_seed(42)
  np.random.seed(42)
  body = []
  header = [nn.Linear(len(feature_columns), 100),
        nn.ReLU(),
        nn.Dropout(p=0.5)]
  tail = [
    nn.Linear(100, 3)]
  for _ in range(i):
      body += [
      nn.Linear(100, 100),
      nn.ReLU(),
      nn.BatchNorm1d(100)]

  K = [*header, *body, *tail]
  model = nn.Sequential(*K)
  loss_fn = torch.nn.CrossEntropyLoss()
  optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
  train_losses, test_losses = train(20)
  m = min(train_losses)
  mi.append(m)
  plt.plot(train_losses)
  plt.show()
plt.plot(mi)
plt.show()