1. 先写出第一步

# 定义输入值和期望输出
x_1 = 40.0
x_2 = 80.0
expected_output = 60.0'''
初始化
'''
# 定义权重
w_1_11 = 0.5
w_1_12 = 0.5
w_1_13 = 0.5
w_1_21 = 0.5
w_1_22 = 0.5
w_1_23 = 0.5w_2_11 = 1.0
w_2_21 = 1.0
w_2_31 = 1.0'''前向传播
'''
z_1 = x_1 * w_1_11 + x_2 * w_1_21
z_2 = x_1 * w_1_12 + x_2 * w_1_22
z_3 = x_1 * w_1_13 + x_2 * w_1_23
y_pred = z_1 * w_2_11 + z_2 * w_2_21 + z_3 * w_2_31# print(z_1, z_2, z_3, y_pred)
print("前向传播预测值为： ", y_pred)# 计算损失函数值（L2损失）
loss = 0.5 * (expected_output - y_pred) ** 2
print("当前的loss值为：",loss)'''
开始计算梯度
'''# 计算输出层关于损失函数的梯度
d_loss_predicted_output = -(expected_output - y_pred)
# print(d_loss_predicted_output)
#
# 计算权重关于损失函数的梯度
d_loss_w_2_11 = d_loss_predicted_output * z_1
d_loss_w_2_21 = d_loss_predicted_output * z_2
d_loss_w_2_31 = d_loss_predicted_output * z_3
# print(d_loss_w_2_11,d_loss_w_2_21,d_loss_w_2_31)d_loss_w_1_11 = d_loss_predicted_output * w_2_11 * x_1
# print(d_loss_w_1_11)
d_loss_w_1_21 = d_loss_predicted_output * w_2_11 * x_2
# print(d_loss_w_1_21)
d_loss_w_1_12 = d_loss_predicted_output * w_2_21 * x_1
# print(d_loss_w_1_12)
d_loss_w_1_22 = d_loss_predicted_output * w_2_21 * x_2
# print(d_loss_w_1_22)
d_loss_w_1_13 = d_loss_predicted_output * w_2_31 * x_1
# print(d_loss_w_1_13)
d_loss_w_1_23 = d_loss_predicted_output * w_2_31 * x_2
# print(d_loss_w_1_23)# 使用梯度下降法更新权重
learning_rate = 1e-5
w_2_11 -= learning_rate * d_loss_w_2_11
w_2_21 -= learning_rate * d_loss_w_2_21
w_2_31 -= learning_rate * d_loss_w_2_31w_1_11 -= learning_rate * d_loss_w_1_11
w_1_12 -= learning_rate * d_loss_w_1_12
w_1_13 -= learning_rate * d_loss_w_1_13
w_1_21 -= learning_rate * d_loss_w_1_21
w_1_22 -= learning_rate * d_loss_w_1_22
w_1_23 -= learning_rate * d_loss_w_1_23'''前向传播
'''z_1 = x_1 * w_1_11 + x_2 * w_1_21
z_2 = x_1 * w_1_12 + x_2 * w_1_22
z_3 = x_1 * w_1_13 + x_2 * w_1_23y_pred = z_1 * w_2_11 + z_2 * w_2_21 + z_3 * w_2_31
print("Final: ",y_pred)# print("前向传播预测值为： ", y_pred)
loss = 0.5 * (expected_output - y_pred) ** 2
print("当前的loss值为：",loss)

2.将其封装成函数

def forward_propagation(layer_1_list, layer_2_list):w_1_11, w_1_12, w_1_13, w_1_21, w_1_22, w_1_23 = layer_1_listw_2_11, w_2_21, w_2_31 = layer_2_listz_1 = x_1 * w_1_11 + x_2 * w_1_21z_2 = x_1 * w_1_12 + x_2 * w_1_22z_3 = x_1 * w_1_13 + x_2 * w_1_23y_pred = z_1 * w_2_11 + z_2 * w_2_21 + z_3 * w_2_31return y_preddef compute_loss(y_true, y_pred):loss = 0.5 * (y_true - y_pred) ** 2return lossdef backward_propagation(layer_1_list,layer_2_list,learning_rate):w_1_11, w_1_12, w_1_13, w_1_21, w_1_22, w_1_23 = layer_1_listw_2_11, w_2_21, w_2_31 = layer_2_listz_1 = x_1 * w_1_11 + x_2 * w_1_21z_2 = x_1 * w_1_12 + x_2 * w_1_22z_3 = x_1 * w_1_13 + x_2 * w_1_23# 计算输出层关于损失函数的梯度d_loss_predicted_output = -(y_true - y_pred)# 计算权重关于损失函数的梯度d_loss_w_2_11 = d_loss_predicted_output * z_1d_loss_w_2_21 = d_loss_predicted_output * z_2d_loss_w_2_31 = d_loss_predicted_output * z_3d_loss_w_1_11 = d_loss_predicted_output * w_2_11 * x_1d_loss_w_1_21 = d_loss_predicted_output * w_2_11 * x_2d_loss_w_1_12 = d_loss_predicted_output * w_2_21 * x_1d_loss_w_1_22 = d_loss_predicted_output * w_2_21 * x_2d_loss_w_1_13 = d_loss_predicted_output * w_2_31 * x_1d_loss_w_1_23 = d_loss_predicted_output * w_2_31 * x_2# 使用梯度下降法更新权重w_2_11 -= learning_rate * d_loss_w_2_11w_2_21 -= learning_rate * d_loss_w_2_21w_2_31 -= learning_rate * d_loss_w_2_31w_1_11 -= learning_rate * d_loss_w_1_11w_1_12 -= learning_rate * d_loss_w_1_12w_1_13 -= learning_rate * d_loss_w_1_13w_1_21 -= learning_rate * d_loss_w_1_21w_1_22 -= learning_rate * d_loss_w_1_22w_1_23 -= learning_rate * d_loss_w_1_23layer_1_list = [w_1_11, w_1_12, w_1_13, w_1_21, w_1_22, w_1_23]layer_2_list = [w_2_11, w_2_21, w_2_31]return layer_1_list,layer_2_listdef parm_init():# 初始化定义权重w_1_11 = 0.5w_1_12 = 0.5w_1_13 = 0.5w_1_21 = 0.5w_1_22 = 0.5w_1_23 = 0.5w_2_11 = 1.0w_2_21 = 1.0w_2_31 = 1.0layer_1_list = [w_1_11,w_1_12,w_1_13,w_1_21,w_1_22,w_1_23]layer_2_list = [w_2_11,w_2_21,w_2_31]return layer_1_list, layer_2_listif __name__ == '__main__':# 定义输入值和期望输出x_1 = 40.0x_2 = 80.0y_true = 60.0learning_rate = 1e-5epoch = 100'''初始化'''# 初始化定义权重layer_1_list, layer_2_list = parm_init()for i in range(epoch):# 正向传播y_pred = forward_propagation(layer_1_list,layer_2_list)# 计算损失loss = compute_loss(y_true, y_pred)print(f"第{i}次 预测值为： ", y_pred, " 误差为： ",loss)# 反向传播layer_1_list,layer_2_list = backward_propagation(layer_1_list,layer_2_list,learning_rate)

3. pytorch版

import torch
import torch.optim as optim

def forward_propagation(x_1, x_2, layer_1_list, layer_2_list):
w_1_11, w_1_12, w_1_13, w_1_21, w_1_22, w_1_23 = layer_1_list
w_2_11, w_2_21, w_2_31 = layer_2_list
z_1 = x_1 * w_1_11 + x_2 * w_1_21
z_2 = x_1 * w_1_12 + x_2 * w_1_22
z_3 = x_1 * w_1_13 + x_2 * w_1_23
y_pred = z_1 * w_2_11 + z_2 * w_2_21 + z_3 * w_2_31
return y_pred

def compute_loss(y_true, y_pred):
loss = 0.5 * (y_true - y_pred) ** 2
return loss

def backward_propagation(layer_1_list, layer_2_list, optimizer):

# 清零梯度
optimizer.zero_grad()# 反向传播
loss.backward()# 使用优化器更新权重
optimizer.step()# 返回更新后的权重
return layer_1_list, layer_2_list

def parm_init():
# 初始化定义权重
w_1_11 = torch.tensor(0.5, requires_grad=True)
w_1_12 = torch.tensor(0.5, requires_grad=True)
w_1_13 = torch.tensor(0.5, requires_grad=True)
w_1_21 = torch.tensor(0.5, requires_grad=True)
w_1_22 = torch.tensor(0.5, requires_grad=True)
w_1_23 = torch.tensor(0.5, requires_grad=True)

w_2_11 = torch.tensor(1.0, requires_grad=True)
w_2_21 = torch.tensor(1.0, requires_grad=True)
w_2_31 = torch.tensor(1.0, requires_grad=True)layer_1_list = [w_1_11, w_1_12, w_1_13, w_1_21, w_1_22, w_1_23]
layer_2_list = [w_2_11, w_2_21, w_2_31]
return layer_1_list, layer_2_list

if name == ‘main’:
# 定义输入值和期望输出
x_1 = torch.tensor([40.0])
x_2 = torch.tensor([80.0])
y_true = torch.tensor([60.0])
learning_rate = 1e-5

epoch = 100'''
初始化
'''
# 初始化定义权重
layer_1_list, layer_2_list = parm_init()# 使用SGD优化器进行权重更新
optimizer = optim.SGD(layer_1_list + layer_2_list, lr=learning_rate)for i in range(epoch):# 正向传播y_pred = forward_propagation(x_1, x_2, layer_1_list, layer_2_list)# 计算损失loss = compute_loss(y_true, y_pred)# 反向传播layer_1_list, layer_2_list = backward_propagation(layer_1_list, layer_2_list, optimizer)print(f"第{i}次 预测值为： ", y_pred.item(), " 误差为： ",loss.item())