bp神经网络python

在Python中，可以使用多种库来实现BP神经网络，其中最常用的是NumPy和Keras。

1. 使用NumPy实现BP神经网络：

import numpy as np

# 定义sigmoid函数
def sigmoid(x):
    return 1 / (1 + np.exp(-x))

# 定义sigmoid函数的导数
def sigmoid_derivative(x):
    return x * (1 - x)

# 定义BP神经网络类
class NeuralNetwork:
    def __init__(self, input_size, hidden_size, output_size):
        # 初始化权重矩阵
        self.weights1 = np.random.randn(input_size, hidden_size)
        self.weights2 = np.random.randn(hidden_size, output_size)

    def forward(self, X):
        # 前向传播
        self.hidden_layer = sigmoid(np.dot(X, self.weights1))
        self.output_layer = sigmoid(np.dot(self.hidden_layer, self.weights2))
        return self.output_layer

    def backward(self, X, y, output):
        # 反向传播
        self.output_error = y - output
        self.output_delta = self.output_error * sigmoid_derivative(output)

        self.hidden_error = np.dot(self.output_delta, self.weights2.T)
        self.hidden_delta = self.hidden_error * sigmoid_derivative(self.hidden_layer)

        self.weights2 += np.dot(self.hidden_layer.T, self.output_delta)
        self.weights1 += np.dot(X.T, self.hidden_delta)

    def train(self, X, y, epochs):
        for i in range(epochs):
            output = self.forward(X)
            self.backward(X, y, output)

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

2. 使用Keras实现BP神经网络：

from keras.models import Sequential
from keras.layers import Dense

# 定义BP神经网络模型
model = Sequential()
model.add(Dense(10, input_dim=8, activation='sigmoid'))
model.add(Dense(1, activation='sigmoid'))

# 编译模型
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

# 训练模型
model.fit(X_train, y_train, epochs=100, batch_size=10)

# 评估模型
score = model.evaluate(X_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])

# 预测结果
y_pred = model.predict(X_test)