《PyTorch深度学习实践》-刘二大人 第二讲

import numpy as np
import matplotlib.pyplot as plt

x_data = [1.0, 2.0, 3.0]
y_data = [2.0, 4.0, 6.0]

#前馈函数
def forward(x):
    return x * w

#损失函数
def loss(x, y):
    y_pred = forward(x)
    return (y_pred - y) * (y_pred - y)

w_list = []#参数值w
mse_list = []#随着参数值变化产生的均方差


for w in np.arange(0.0, 4.1, 0.1):
    print('w=', w)
    l_sum = 0
    #将x_data, y_data打包成一个个元组（x_val, y_val）
    # 其实就是每次对每个list取一个值放入x_val和y_val
    for x_val, y_val in zip(x_data, y_data):
        y_pred_val = forward(x_val)
        loss_val = loss(x_val, y_val)
        l_sum += loss_val
        print('\t', x_val, y_val, y_pred_val, loss_val)
    print('MSE=', l_sum / 3)
    w_list.append(w)
    mse_list.append(l_sum / 3)
plt.plot(w_list, mse_list)
plt.ylabel('Loss')
plt.xlabel('w')
plt.show()

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

x_data = [1.0, 2.0, 3.0]
y_data = [2.0, 4.0, 6.0]

def forward(x):
    return x * w + b

def loss(x, y):
    y_pred = forward(x)
    return (y_pred - y) * (y_pred - y)

w_list = np.arange(0.0, 4.0, 0.1)
b_list = np.arange(-2, 2.0, 0.1)
w, b = np.meshgrid(w_list, b_list)

l_sum = 0
for x_val, y_val in zip(x_data, y_data):
    loss_val = loss(x_val, y_val)
    l_sum += loss_val
    print(l_sum)
mse = l_sum / 3

fig = plt.figure()
ax = Axes3D(fig,auto_add_to_figure=False)
fig.add_axes(ax)
surf = ax.plot_surface(w, b, mse, rstride=1, cstride=1, cmap='coolwarm',
                       linewidth=0, antialiased=False)
#fig.colorbar(surf, shrink=0.5, aspect=5)
ax.set_xlabel("w")
ax.set_ylabel("b")
plt.title("loss")
plt.show()