我有一个简单的分类网络，它接受一个长度为2的向量，并进行整数预测。我试图更好地理解预测的决策边界。我有一个10,000个点的数据集，训练集的大小为9,000。特征是框[-1，1]^2，标签是长度为3的独热编码矢量。从link，我得到了一种绘制判定边界的方法，代码如下：

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 = plt.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 = np.linspace(xmin, xmax, steps)
    y_span = np.linspace(ymin, ymax, steps)
    xx, yy = np.meshgrid(x_span, y_span)

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

    # Plot decision boundary in region of interest
    z = labels.reshape(xx.shape)
    
    fig, ax = plt.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, cmap=cmap, lw=0)
    return(fig,ax)

在这里，我调用plot_decision_boundary(train_features, train_labels_onehot, model, cmap='RdBu')来创建决策边界图（参见上面链接中的示例）。