深度学习|2. 刘二大人《PyTorch深度学习实践》作业--梯度下降深度学习|python

这里，我在刘老师的基础上做了改进，将线性函数改为了y = w x + b y = wx+b y=wx+b，以下实现都是基于此线性函数做的。
1. 梯度下降

import numpy as np import matplotlib.pyplot as plt x_data = https://www.it610.com/article/[1.0, 2.0, 3.0] y_data = [3.0, 5.0, 7.0]w = 1.0 b = 1.0def forward(x): return x * w + b# 损失函数 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)# 迭代，计算损失值 cost_list = [] print('Predict (before training)', 4, forward(4)) for epoch in range(100): cost_val = cost(x_data, y_data) grad_w, grad_b = gradient(x_data, y_data) w -= 0.01 * grad_w b -= 0.01 * grad_b cost_list.append(cost_val) print('Epoch:', epoch, 'w=', w,'b=', b, 'loss=', cost_val) print('Predict (after training)', 4, forward(4))# 绘制图像 epoches = np.arange(0, 100, 1) plt.xlabel('epoch') plt.ylabel('cost') plt.plot(epoches, cost_list) plt.grid() plt.show()

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2. 随机梯度下降

x_data = https://www.it610.com/article/[1.0, 2.0, 3.0] y_data = [3.0, 5.0, 7.0]w = 1.0 b = 1.0def forward(x): return x * w + b# 随机梯度下降算法 def sgd(x, y): y_pred = forward(x) grad_w = 0 grad_b = 0 grad_w += 2 * x * (y_pred - y) grad_b += 2 * (y_pred - y) return grad_w, grad_b# 损失函数 def cost_sgd(x, y): y_pred = forward(x) return (y_pred - y) ** 2# 迭代，计算损失值 cost_list = [] print('Predict (before training)', 4, forward(4)) for epoch in range(100): for x, y in zip(x_data, y_data): cost_val = cost_sgd(x, y) grad_w, grad_b = sgd(x, y) w -= 0.01 * grad_w b -= 0.01 * grad_b cost_list.append(cost_val) print('Epoch:', epoch, 'w=', w, 'b=', b, 'loss=', cost_val) print('Predict (after training)', 4, forward(4))# 绘制图像 epoches = np.arange(0, 300, 1) plt.xlabel('epoch') plt.ylabel('cost') plt.plot(epoches, cost_list) plt.grid() plt.show()

【深度学习|2. 刘二大人《PyTorch深度学习实践》作业--梯度下降】