import numpy as np
import matplotlib.pyplot as plt
#导入数据
x_data = [1.0, 2.0, 3.0]
y_data = [2.0, 4.0, 6.0]
w = 1.0
#定义模型、损失函数和梯度下降公式
def forward(x):
return w * x
def cost(xs, ys):
cost = 0
for x, y in zip(xs, ys):
y_pred = forward(x)
cost += (y_pred - y) ** 2
return cost / len(xs)
def gradient(xs, ys):
grad = 0
for x, y in zip(xs, ys):
grad += 2 * (x * w - y) * x
return grad / len(xs)
epoch_list = []
cost_list = []
#进行训练
print('predict (before training)', 4, forward(4))
for epoch in range(100):
cost_val = cost(x_data, y_data)
grad_val = gradient(x_data, y_data)
w -= 0.01 * grad_val
epoch_list.append(epoch)
cost_list.append(cost_val / 3)
print('epoch:', epoch, 'w=', w, 'loss=', cost_val)
print('predict(after training)', 4, forward(4))
#绘制损失图
plt.plot(epoch_list, cost_list)
plt.ylabel('cost')
plt.xlabel('epoch')
plt.show()
