《PyTorch深度学习实践》刘二大人 线性模型 作业

import numpy as np import matplotlib.pyplot as plt from matplotlib import cm from mpl_toolkits.mplot3d import Axes3D # y = x*2.5-1 构造训练数据 x_data = [1.0, 2.0, 3.0] y_data = [1.5, 4.0, 6.5] W, B = np.arange(0.0, 4.1, 0.1), np.arange(-2.0, 2.1, 0.1) # 规定 W,B 的区间 w, b = np.meshgrid(W, B, indexing='ij') # 构建矩阵坐标 def forward(x): return x*w+b def loss(y_pred, y): return (y_pred-y)*(y_pred-y) # Make data. mse_lst = [] l_sum = 0. for x_val, y_val in zip(x_data, y_data): y_pred_val = forward(x_val) loss_val = loss(y_pred_val, y_val) l_sum += loss_val mse_lst.append(l_sum/3) # 定义figure fig = plt.figure(figsize=(10,10), dpi=300) # 将figure变为3d ax = Axes3D(fig) # 绘图，rstride:行之间的跨度 cstride:列之间的跨度 surf = ax.plot_surface(w, b, np.array(mse_lst[0]), rstride=1, cstride=1, cmap=cm.coolwarm, linewidth=0, antialiased=False) # Customize the z axis. ax.set_zlim(0, 40) # 设置坐标轴标签 ax.set_xlabel("w") ax.set_ylabel("b") ax.set_zlabel("loss") ax.text(0.2, 2, 43, "Cost Value", color='black') # Add a color bar which maps values to colors. fig.colorbar(surf, shrink=0.5, aspect=5) plt.show()