matplotlib作图不显示3D图，怎么办？

大家好，我是Python进阶者。

一、前言

前几天在Python白银交流群【Kim】问了一个Python作图的问题，问题如下：他不显示3D图咋办？

代码如下：

from sklearn.linear_model import LogisticRegression
import numpy as np
from matplotlib import pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

# 加载数据
data = load_breast_cancer()
X, y = data['data'][:,:2], data['target']

# 求出两个维度对应的数据在逻辑回归算法下的最优解
lr = LogisticRegression(fit_intercept=False)    # 是否计算截距项
lr.fit(X, y)
# 分别把两个维度所对应的参数w1和w2取出来
theta1 = lr.coef_[0, 0]
theta2 = lr.coef_[0, 1]
print(theta1)
print(theta2)

def p_theta_function(features, w1, w2):
    """
    已知w1和w2的情况下，传进来数据X，返回数据的y_predict
    :param features:xi=(x1, x2)
    :param w1:
    :param w2:
    :return:  y_pred
    """
    # z = $\theta^Tx=w1*x1+w2*x2$
    z = w1 * features[0] + w2 * features[1]
    return 1 / (1 + np.exp(-z))

def loss_function(sample_features, sample_labels, w1, w2):
    """
    传入一份已知数据，如果已知w1和w2，计算对应这份数据的loss损失
    :param sample_features:
    :param sample_labels:
    :param w1:
    :param w2:
    :return:
    """
    result = 0
    # 遍历数据集中的每一条样本，并且计算每条样本的损失，加到result身上得到整体数据集
    for feature, label in zip(sample_features, sample_labels):
        # 计算一条样本的y_pred
        p_result = p_theta_function(feature, w1, w2)
        loss_result = -1 * label * np.log(p_result) - (1 - label) * np.log(1 - p_result)
        result += loss_result
    return result

theta1_space = np.linspace(theta1-0.6, theta1+0.6, 50)
theta2_space = np.linspace(theta2-0.6, theta2+0.6, 50)
result1 = np.array([loss_function(X, y, i, theta2) for i in theta1_space])
result2 = np.array([loss_function(X, y, theta1, i) for i in theta2_space])

fig1 = plt.figure(figsize=(8, 6))
plt.subplot(221)
plt.plot(theta1_space, result1)

plt.subplot(222)
plt.plot(theta2_space, result2)

plt.subplot(223)
theta1_grid, theta2_grid = np.meshgrid(theta1_space, theta2_space)
loss_grid = loss_function(X, y, theta1_grid, theta2_grid)
plt.contour(theta1_grid, theta2_grid, loss_grid)

plt.subplot(224)
plt. contour(theta1_grid, theta2_grid, loss_grid, 30)

fig2 = plt.figure()
ax = Axes3D(fig2)
ax.plot_surface(theta1_grid, theta2_grid, loss_grid)

plt.show()