一个最简单的神经网络实现流程

#!/usr?bin/env/ python
# -*- coding:utf-8 -*-
# author: lai zheng laing
# datetime: 2020/10/15 16:53
# software: PyCharm

# -----------------最简单的卷积神经网络，LeNet,实现一个前向传播的网络----------------------
import torch
import matplotlib
import matplotlib.pyplot as plt
batch_n = 100
hidden_layer = 100
input_data = 1000
output_data = 10

x = torch.randn(batch_n, input_data)
y = torch.randn(batch_n, output_data)

w1 = torch.randn(input_data, hidden_layer)
w2 = torch.randn(hidden_layer, output_data)

epoch_n = 20
learning_rate = 1e-6

for epoch in range(epoch_n):
    h1 = x.mm(w1)  # 100*1000
    h1 = h1.clamp(min=0)
    y_pred = h1.mm(w2)  # 100*10
    # print(y_pred)

    loss = (y_pred - y).pow(2).sum()
    print("Epoch:{} , Loss:{:.4f}".format(epoch, loss))


    gray_y_pred = 2 * (y_pred - y)
    gray_w2 = h1.t().mm(gray_y_pred)

    grad_h = gray_y_pred.clone()
    grad_h = grad_h.mm(w2.t())
    grad_h.clamp_(min=0)
    grad_w1 = x.t().mm(grad_h)

    w1 -= learning_rate * grad_w1
    w2 -= learning_rate * gray_w2