22、自动优化：深度学习框架的进阶之路

自动优化：深度学习框架的进阶之路

在深度学习的领域中，训练神经网络是一项核心任务。传统上，我们需要手动编写反向传播逻辑，这不仅繁琐，还容易出错。而现在，自动求导（autograd）系统的出现，极大地简化了这一过程。

手动反向传播的繁琐

首先，让我们看看手动进行反向传播的情况。以下是一个简单的神经网络手动反向传播的代码示例：

import numpy
np.random.seed(0)
data = np.array([[0,0],[0,1],[1,0],[1,1]])
target = np.array([[0],[1],[0],[1]])
weights_0_1 = np.random.rand(2,3)
weights_1_2 = np.random.rand(3,1)
for i in range(10):
    layer_1 = data.dot(weights_0_1)
    layer_2 = layer_1.dot(weights_1_2)
    diff = (layer_2 - target)
    sqdiff = (diff * diff)
    loss = sqdiff.sum(0)
    layer_1_grad = diff.dot(weights_1_2.transpose())
    weight_1_2_update = layer_1.transpose().dot(diff)
    weight_0_1_update = data.transpose().dot(layer_1_grad)
    weights_1_2 -= weight_1_2_update