用Keras模型可视化地块决策边界

Question

我试图用Keras绘制模型预测的决策图边界。然而，生成的边界似乎不正确。

这是我的模型

def base():
    model = Sequential()
    model.add(Dense(5,activation = 'relu', input_dim = 2))
    model.add(Dense(2,activation = 'relu'))
    model.add(Dense(1,activation = 'sigmoid'))
    model.compile(optimizer = optimizers.SGD(lr=0.0007, momentum=0.0, decay=0.0), loss = 'binary_crossentropy', metrics= ['accuracy'])      
    return model 

model = base()
history = model.fit(train_X,train_Y, epochs = 10000, batch_size =64, verbose = 2)

这是我的绘图函数(摘自这里)

def plot_decision_boundary(X, y, model, steps=1000, cmap='Paired'):
    """
    Function to plot the decision boundary and data points of a model.
    Data points are colored based on their actual label.
    """
    cmap = get_cmap(cmap)

    # Define region of interest by data limits
    xmin, xmax = X[:,0].min() - 1, X[:,0].max() + 1
    ymin, ymax = X[:,1].min() - 1, X[:,1].max() + 1
    steps = 1000
    x_span = linspace(xmin, xmax, steps)
    y_span = linspace(ymin, ymax, steps)
    xx, yy = meshgrid(x_span, y_span)

    # Make predictions across region of interest
    labels = model.predict(c_[xx.ravel(), yy.ravel()])

    # Plot decision boundary in region of interest
    z = labels.reshape(xx.shape)

    fig, ax = subplots()
    ax.contourf(xx, yy, z, cmap=cmap, alpha=0.5)

    # Get predicted labels on training data and plot
    train_labels = model.predict(X)
    ax.scatter(X[:,0], X[:,1], c=y.ravel(), cmap=cmap, lw=0)

    return fig, ax
plot_decision_boundary(train_X,train_Y, model, cmap = 'RdBu')

我得到了这样的情节

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这显然是一个非常错误的描述一个情节的决定边界(根本没有信息，因为有这么多的边界)。有人能指出我的错误吗？

Answer 1

由于概率是从0到1的连续值，所以我们得到了许多等值线。

如果您的可视化仅限于2个类(输出为2D softmax向量)，则可以使用以下简单代码

def plot_model_out(x,y,model):
  """
  x,y: 2D MeshGrid input
  model: Keras Model API Object
  """
  grid = np.stack((x,y))
  grid = grid.T.reshape(-1,2)
  outs = model.predict(grid)
  y1 = outs.T[0].reshape(x.shape[0],x.shape[0])
  plt.contourf(x,y,y1)
  plt.show()

这将给出等高线(不止一个)，如果您想要一条等高线，可以执行以下操作

您可以阈值的概率输出从model.predict和显示一条单一的等高线。

例如,

import numpy as np 
from matplotlib import pyplot as plt 

a = np.linspace(-5, 5, 100)
xx, yy = np.meshgrid(a,a)
z = xx**2 + yy**2
# z = z > 5 (Threshold value)
plt.contourf(xx, yy, z,)
plt.show()

对于阈值注释和未注释，我们得到了2幅图像。

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由连续值引起的多个等高线

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当z被阈值化时的单个轮廓(z =z> 5)

类似的方法可以用于输出softmax向量，如下所示

label = label > 0.5

有关可视化代码的更多信息，请参阅IITM CVI博客