PyTorch 深度学习实践 第2讲

1、函数forward()中，有一个变量w。这个变量最终的值是从for循环中传入的。
2、for循环中，使用了np.arange。若对numpy不太熟悉，传送门Numpy数据计算从入门到实战
3、python中zip()函数的用法

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]
def forward(x):
    return x*w
def loss(x, y):
    y_pred = forward(x)
    return (y_pred - y)**2
# 穷举法
w_list = []
mse_list = []
for w in np.arange(0.0, 4.1, 0.1):
    print("w=", w)
    l_sum = 0
    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()

# Numpy
import numpy
# For plotting
import matplotlib.pyplot as plt
from matplotlib import cm
from mpl_toolkits.mplot3d import Axes3D
x_data = [1.0, 2.0, 3.0]
y_data = [2.0, 4.0, 6.0]
def forward(w: numpy.ndarray, b: numpy.ndarray, x: float) -> numpy.ndarray:
    return w * x + b
def loss(y_hat: numpy.ndarray, y: float) -> numpy.ndarray:
    return (y_hat - y) ** 2
w_cor = numpy.arange(0.0, 4.0, 0.1)
b_cor = numpy.arange(-2.0, 2.1, 0.1)
# 此处直接使用矩阵进行计算
w, b = numpy.meshgrid(w_cor, b_cor)
mse = numpy.zeros(w.shape)
for x, y in zip(x_data, y_data):
    _y = forward(w, b, x)
    mse += loss(_y, y)
mse /= len(x_data)
h = plt.contourf(w, b, mse)
fig = plt.figure()
ax = Axes3D(fig)
plt.xlabel(r'w', fontsize=20, color='cyan')
plt.ylabel(r'b', fontsize=20, color='cyan')
ax.plot_surface(w, b, mse, rstride=1, cstride=1, cmap=plt.get_cmap('rainbow'))
plt.show()