神经网络的反向传播(Backpropagation)是一种通过计算梯度来更新网络权重的方法,它是训练神经网络的核心算法。如果你遇到反向传播代码不工作的问题,可能是由于以下几个原因:
反向传播广泛应用于各种深度学习任务,如图像识别、自然语言处理、语音识别等。
以下是一个简单的反向传播示例,使用Python和NumPy实现:
import numpy as np
# 前向传播
def forward_propagation(X, parameters):
caches = {}
A = X
L = len(parameters) // 2
for l in range(1, L):
A_prev = A
W = parameters['W' + str(l)]
b = parameters['b' + str(l)]
Z = np.dot(W, A_prev) + b
A = relu(Z)
caches['Z' + str(l)] = Z
caches['A' + str(l)] = A
W = parameters['W' + str(L)]
b = parameters['b' + str(L)]
Z = np.dot(W, A) + b
AL = sigmoid(Z)
caches['Z' + str(L)] = Z
caches['A' + str(L)] = AL
return AL, caches
# 反向传播
def backward_propagation(X, Y, caches, parameters):
grads = {}
m = X.shape[1]
L = len(parameters) // 2
dZ = caches['A' + str(L)] - Y
grads['dW' + str(L)] = (1 / m) * np.dot(dZ, caches['A' + str(L-1)].T)
grads['db' + str(L)] = (1 / m) * np.sum(dZ, axis=1, keepdims=True)
for l in reversed(range(1, L)):
dA = np.dot(parameters['W' + str(l+1)].T, dZ)
dZ = dA * relu_backward(dZ, caches['A' + str(l)])
grads['dW' + str(l)] = (1 / m) * np.dot(dZ, caches['A' + str(l-1)].T)
grads['db' + str(l)] = (1 / m) * np.sum(dZ, axis=1, keepdims=True)
return grads
# 激活函数及其导数
def relu(Z):
return np.maximum(0, Z)
def relu_backward(dA, Z):
dZ = np.array(dA, copy=True)
dZ[Z <= 0] = 0
return dZ
def sigmoid(Z):
return 1 / (1 + np.exp(-Z))
def sigmoid_backward(dA, Z):
s = sigmoid(Z)
dZ = dA * s * (1 - s)
return dZ
通过检查上述可能的原因并调整代码,你应该能够解决反向传播代码不工作的问题。
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