import matplotlib.pyplot as plt #############################################################梯度下降算法 # prepare the training set x_data [1.0, 2.0, 3.0] y_data [2.0, 4.0, 6.0] # initial guess of weight w 1.0 # define the model linear model y w*x def forward(x): return x * w # define the cost function MSE 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) # define the gradient function gd def gradient(xs, ys): grad 0 for x, y in zip(xs, ys): grad 2 * x * (x * w - y) return grad / len(xs) epoch_list [] cost_list [] 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 # 0.01 learning rate print(epoch:, epoch, w, w, cost, cost_val) epoch_list.append(epoch) cost_list.append(cost_val) print(predict (after training), 4, forward(4)) plt.plot(epoch_list, cost_list) plt.ylabel(cost) plt.xlabel(epoch) plt.show()