1、热身 numpy
# -*- coding:utf-8 -*-
import numpy as np
# N是批量大小；D_in是输入维度。
# 49/5000 H是隐藏的维度；D_out是输出的维度
N, D_in, H, D_out = 64, 1000, 100, 10# 创建随机输入和输出数据
x = np.random.randn(N, D_in)
y = np.random.randn(N, D_out)# 随机初始化权重
w1 = np.random.randn(D_in, H)
w2 = np.random.randn(H, D_out)learning_rate = 1e-6
for t in range(500):# 前向传递：计算预测值yh = x.dot(w1)h_relu = np.maximum(h, 0)y_pred = h_relu.dot(w2)# 计算和打印损失lossloss = np.square(y_pred - y).sum()print(t, loss)# 反向传播，计算w1和w2对loss的梯度grad_y_pred = 2.0 * (y_pred - y)grad_w2 = h_relu.T.dot(grad_y_pred)grad_h_relu = grad_y_pred.dot(w2.T)grad_h = grad_h_relu.copy()grad_h[h < 0] = 0grad_w1 = x.T.dot(grad_h)# 更新权重w1 -= learning_rate * grad_w1w2 -= learning_rate * grad_w2