Python 在PyTorch中绘制神经网络的决策边界

我一直在尝试绘制我的神经网络的决策边界，我使用输出层中的sigmoid函数进行二元分类，但没有成功，我发现许多帖子讨论了scikit学习分类器的决策边界的绘制，而不是PyTorch中构建的神经网络。 下面是我的神经网络：

class NeuralNetwork(torch.nn.Module):
  def __init__(self):
    super(NeuralNetwork, self).__init__()
    self.fc1 = torch.nn.Linear(23, 16)
    self.fc2 = torch.nn.Linear(16, 14)
    self.fc3 = torch.nn.Linear(14, 10)
    self.fc4 = torch.nn.Linear(10, 5)
    self.fc5 = torch.nn.Linear(5, 1)

  def forward(self, x):
    x = torch.relu(self.fc1(x))
    x = torch.relu(self.fc2(x))
    x = torch.relu(self.fc3(x))
    x = torch.relu(self.fc4(x))
    x = torch.sigmoid(self.fc5(x))
    return x

model = NeuralNetwork().double()

CUDA = torch.cuda.is_available()
if CUDA:
  model.cuda()

criterion = torch.nn.BCELoss(reduction='mean')
optimizer = torch.optim.SGD(model.parameters(), lr=1e-2, momentum=0.9)

model_1.train()

Precision = []
Cost = []

for epoch in range(10001):

  if CUDA:
    inputs = X_train.cuda()
    label = Y_train.cuda()
  else:
    inputs = X_train
    label = Y_train

  prediction = model_1(inputs)
  loss = criterion(prediction, label)
  accuracy = ((prediction > 0.5) == label).float().mean().item()

  Cost.append(loss.item())
  Precision.append(accuracy)

  if epoch % 1000 == 0 or epoch == 30000:
    print("Epoch:", epoch, ",", "Loss:", loss.item(), ",", "Accuracy:", accuracy)

  # Backpropagation process
  optimizer.zero_grad()
  loss.backward()
  optimizer.step()

model_1.eval()

X_test = torch.from_numpy(X[27000:,:])
Y_test = torch.from_numpy(y[27000:,:]).double()

with torch.no_grad():

  y_pred = model_1(X_test)
  print("Accuracy: ", ((y_pred > 0.5) == Y_test).float().mean().item())

以下是我试图生成类似图的尝试：

但不幸的是，我得到了以下错误：

TypeError                                 Traceback (most recent call last)

<ipython-input-52-fb941749621a> in <module>()
  1 f, ax = plt.subplots(figsize=(8, 6))
  2 contour = ax.contourf(xx, yy, probs, 25, cmap="RdBu",
----> 3                       vmin=0, vmax=1)
  4 ax_c = f.colorbar(contour)
  5 ax_c.set_label("$P(y = 1)$")

5 frames

/usr/local/lib/python3.6/dist-packages/matplotlib/contour.py in _check_xyz(self, args, kwargs)
1549             raise TypeError("Input z must be a 2D array.")
1550         elif z.shape[0] < 2 or z.shape[1] < 2:
-> 1551             raise TypeError("Input z must be at least a 2x2 array.")
1552         else:
1553             Ny, Nx = z.shape

TypeError: Input z must be at least a 2x2 array.

TypeError回溯（最近一次调用）
在（）
1 f，ax=plt.子批次（图尺寸=（8,6））
2轮廓=最大轮廓f（xx，yy，probs，25，cmap=“RdBu”，
---->3 vmin=0，vmax=1）
4 ax_c=f.色条（轮廓）
5 ax_c.set_标签（$P（y=1）$）
5帧
/usr/local/lib/python3.6/dist-packages/matplotlib/contour.py in_check_xyz（self、args、kwargs）
1549 raise TypeError（“输入z必须是2D数组”）
1550 elif z.shape[0]<2或z.shape[1]<2：
->1551 raise TypeError（“输入z必须至少是2x2数组。”）
1552其他：
1553 Ny，Nx=z形
TypeError:输入z必须至少是2x2数组。

我将非常感谢你的帮助，提前谢谢

TypeError                                 Traceback (most recent call last)

<ipython-input-52-fb941749621a> in <module>()
  1 f, ax = plt.subplots(figsize=(8, 6))
  2 contour = ax.contourf(xx, yy, probs, 25, cmap="RdBu",
----> 3                       vmin=0, vmax=1)
  4 ax_c = f.colorbar(contour)
  5 ax_c.set_label("$P(y = 1)$")

5 frames

/usr/local/lib/python3.6/dist-packages/matplotlib/contour.py in _check_xyz(self, args, kwargs)
1549             raise TypeError("Input z must be a 2D array.")
1550         elif z.shape[0] < 2 or z.shape[1] < 2:
-> 1551             raise TypeError("Input z must be at least a 2x2 array.")
1552         else:
1553             Ny, Nx = z.shape

TypeError: Input z must be at least a 2x2 array.