脉冲神经网络python代码
时间: 2023-09-01 13:11:54 浏览: 61
脉冲神经网络(Pulse-Coupled Neural Network, PCNN)是一种生物启发式的神经网络结构,常用于图像处理和模式识别等领域。以下是一个用Python实现PCNN的简单代码示例:
```python
import numpy as np
class PCNN:
def __init__(self, image, threshold, beta, alpha):
self.image = image
self.threshold = threshold
self.beta = beta
self.alpha = alpha
self.rows, self.cols = image.shape
self.u = np.zeros((self.rows, self.cols))
self.v = np.zeros((self.rows, self.cols))
self.y = np.zeros((self.rows, self.cols))
def convolve(self):
kernel = np.array([[1, 1, 1], [1, 0, 1], [1, 1, 1]])
s = np.zeros((self.rows, self.cols))
for i in range(1, self.rows-1):
for j in range(1, self.cols-1):
s[i,j] = np.sum(kernel * self.image[i-1:i+2, j-1:j+2])
return s
def update_u(self, s):
for i in range(1, self.rows-1):
for j in range(1, self.cols-1):
self.u[i,j] = self.alpha*self.u[i,j] + self.beta*s[i,j]
def update_v(self):
for i in range(1, self.rows-1):
for j in range(1, self.cols-1):
self.v[i,j] = np.exp(-self.u[i,j])
def update_y(self):
for i in range(1, self.rows-1):
for j in range(1, self.cols-1):
if self.v[i,j] > self.threshold:
self.y[i,j] = 1
def run(self, iterations):
for i in range(iterations):
s = self.convolve()
self.update_u(s)
self.update_v()
self.update_y()
return self.y
```
使用示例:
```python
import cv2
# 读取图像
img = cv2.imread('image.jpg', 0)
# 创建PCNN对象
pcnn = PCNN(img, 0.5, 0.1, 0.9)
# 运行PCNN模型
result = pcnn.run(10)
# 显示结果
cv2.imshow('Original Image', img)
cv2.imshow('PCNN Image', result)
cv2.waitKey(0)
cv2.destroyAllWindows()
```
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