bp神经网络python代码
时间: 2023-03-28 17:02:26 浏览: 40
以下是一个简单的 BP 神经网络的 Python 代码示例:
```
import numpy as np
class NeuralNetwork:
def __init__(self, input_size, hidden_size, output_size):
self.input_size = input_size
self.hidden_size = hidden_size
self.output_size = output_size
self.weights1 = np.random.randn(self.input_size, self.hidden_size)
self.weights2 = np.random.randn(self.hidden_size, self.output_size)
def sigmoid(self, x):
return 1 / (1 + np.exp(-x))
def sigmoid_derivative(self, x):
return x * (1 - x)
def forward(self, X):
self.hidden = self.sigmoid(np.dot(X, self.weights1))
self.output = self.sigmoid(np.dot(self.hidden, self.weights2))
return self.output
def backward(self, X, y, output):
self.output_error = y - output
self.output_delta = self.output_error * self.sigmoid_derivative(output)
self.hidden_error = self.output_delta.dot(self.weights2.T)
self.hidden_delta = self.hidden_error * self.sigmoid_derivative(self.hidden)
self.weights1 += X.T.dot(self.hidden_delta)
self.weights2 += self.hidden.T.dot(self.output_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)
```
这个代码实现了一个具有一个隐藏层的 BP 神经网络,可以用于分类和回归问题。