python bp神经网络代码

Sure, I can help you with that! Do you have any specific requirements or do you just need a general implementation of a backpropagation neural network in Python?

相关问题

pythonbp神经网络代码

好的，以下是一个简单的 Python BP 神经网络代码示例：

import numpy as np

class NeuralNetwork:
    def __init__(self, layers):
        self.weights = []
        for i in range(1, len(layers)):
            w = np.random.randn(layers[i-1], layers[i])
            self.weights.append(w)

    def sigmoid(self, x):
        return 1 / (1 + np.exp(-x))

    def sigmoid_derivative(self, x):
        return x * (1 - x)

    def feed_forward(self, X):
        a = X
        for w in self.weights:
            z = np.dot(a, w)
            a = self.sigmoid(z)
        return a

    def backpropagation(self, X, y, learning_rate):
        a = X
        activations = [a]
        zs = []
        for w in self.weights:
            z = np.dot(a, w)
            zs.append(z)
            a = self.sigmoid(z)
            activations.append(a)

        delta = (activations[-1] - y) * self.sigmoid_derivative(activations[-1])
        for i in range(len(self.weights)-1, -1, -1):
            dw = np.dot(activations[i].T, delta)
            delta = np.dot(delta, self.weights[i].T) * self.sigmoid_derivative(activations[i])
            self.weights[i] -= learning_rate * dw

    def train(self, X, y, learning_rate=0.1, epochs=1000):
        for i in range(epochs):
            self.backpropagation(X, y, learning_rate)

    def predict(self, X):
        return self.feed_forward(X)

# 示例用法
X = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])
y = np.array([[0], [1], [1], [0]])
nn = NeuralNetwork([2, 4, 1])
nn.train(X, y)
print(nn.predict(X))

该代码实现了一个简单的 BP 神经网络，包括前向传播、反向传播和训练等功能。其中，`NeuralNetwork` 类的构造函数接受一个列表 `layers`，表示神经网络的层数和每层的神经元个数；`sigmoid` 和 `sigmoid_derivative` 方法分别实现了 sigmoid 函数和其导数；`feed_forward` 方法实现了前向传播；`backpropagation` 方法实现了反向传播；`train` 方法用于训练神经网络；`predict` 方法用于预测输出。

python实现BP神经网络代码

好的，以下是一个简单的Python BP神经网络实现的代码：

import numpy as np

class NeuralNetwork:
    
    def __init__(self, input_size, hidden_size, output_size):
        self.weights1 = np.random.randn(input_size, hidden_size)
        self.weights2 = np.random.randn(hidden_size, output_size)
        
    def sigmoid(self, x):
        return 1 / (1 + np.exp(-x))
    
    def sigmoid_derivative(self, x):
        return x * (1 - x)
    
    def feed_forward(self, X):
        self.hidden_layer = self.sigmoid(np.dot(X, self.weights1))
        self.output = self.sigmoid(np.dot(self.hidden_layer, self.weights2))
        return self.output
    
    def backpropagation(self, X, y, output):
        output_error = y - output
        output_delta = output_error * self.sigmoid_derivative(output)
        hidden_error = output_delta.dot(self.weights2.T)
        hidden_delta = hidden_error * self.sigmoid_derivative(self.hidden_layer)
        
        self.weights1 += X.T.dot(hidden_delta)
        self.weights2 += self.hidden_layer.T.dot(output_delta)
    
    def train(self, X, y):
        output = self.feed_forward(X)
        self.backpropagation(X, y, output)

X = np.array([[0,0,1], [0,1,1], [1,0,1], [1,1,1]])
y = np.array([[0,1,1,0]]).T

nn = NeuralNetwork(3, 5, 1)

for i in range(10000):
    nn.train(X, y)

print(nn.feed_forward(np.array([1,0,0])))

这是一个简单的三层神经网络，它可以学习XOR问题。我们使用sigmoid函数作为激活函数，并使用反向传播算法来更新权重。