1. numpy로 워밍업
# -*- 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y
h = x.dot(w1)
h_relu = np.maximum(h, 0)
y_pred = h_relu.dot(w2)
# 计算和打印损失loss
loss = 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] = 0
grad_w1 = x.T.dot(grad_h)
# 更新权重
w1 -= learning_rate * grad_w1
w2 -= learning_rate * grad_w2