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